diff --git a/frontend/src/components/ui/ApiError.tsx b/frontend/src/components/ui/ApiError.tsx
new file mode 100644
index 0000000..3aca085
--- /dev/null
+++ b/frontend/src/components/ui/ApiError.tsx
@@ -0,0 +1,29 @@
+import { AlertCircle, X } from "lucide-react";
+
+
+interface ApiErrorProps {
+  message: string | null;
+  onDismiss: () => void;
+}
+
+export function ApiError({ message, onDismiss }: ApiErrorProps) {
+  if (!message) return null;
+
+  return (
+    <div role="alert" aria-live="assertive" className="w-full border border-red-500/40 bg-red-900/20 text-red-200 px-4 py-3 rounded-lg flex items-start justify-between gap-3">
+      <div className="flex items-start gap-2">
+        <AlertCircle className="w-5 h-5 mt-0.5" />
+
+        <p className="text-sm break-words">{message}</p>
+      </div>
+
+      <button
+        onClick={onDismiss}
+        aria-label="Dismiss error"
+        className="cursor-pointer hover:text-red-400 transition-colors"
+      >
+        <X className="w-4 h-4" />
+      </button>
+    </div>
+  );
+}
\ No newline at end of file
diff --git a/frontend/src/pages/NewAnalysis.tsx b/frontend/src/pages/NewAnalysis.tsx
index a670bc8..64fa897 100644
--- a/frontend/src/pages/NewAnalysis.tsx
+++ b/frontend/src/pages/NewAnalysis.tsx
@@ -10,6 +10,7 @@ import { ErrorBoundary } from '../components/ui/ErrorBoundary'
 import { useToast } from '../contexts/ToastContext'
 import { useApp } from '../contexts/AppContext'
 import type { Run } from '../api'
+import { ApiError } from '../components/ui/ApiError'
 
 const PRESETS = [
   { label: 'Last 30d', days: 30 },
@@ -44,6 +45,7 @@ export default function NewAnalysis() {
   const [busy, setBusy] = useState(false)
   const [resultRun, setResultRun] = useState<Run | null>(null)
   const [resultPayload, setResultPayload] = useState<Record<string, unknown> | null>(null)
+  const [error, setError] = useState<string | null>(null)
 
   const canSubmit = bbox !== null && startDate && endDate && !busy
 
@@ -62,13 +64,16 @@ export default function NewAnalysis() {
       return
     }
 
-    setBusy(true)
+    
     setResultRun(null)
     setResultPayload(null)
 
     try {
+      setBusy(true);
+      setError(null);
       const res = await predictJson({ kind: 'bbox', analysis_type: analysisType, bbox: bbox!, start_date: startDate, end_date: endDate })
       setResultPayload(res.result)
+      
       // Construct a minimal Run object for the results panel
       setResultRun({
         id: res.run_id,
@@ -85,8 +90,14 @@ export default function NewAnalysis() {
         label: 'View in history',
         onClick: () => navigate('/runs'),
       })
-    } catch (e) {
-      showToast('error', String(e))
+    } catch (e:any) {
+      const message =
+          e?.response?.data?.detail ||
+          e?.response?.data?.message ||
+          e?.message ||
+          'Prediction failed'
+      setError(message)
+      showToast('error', message)
     } finally {
       setBusy(false)
     }
@@ -94,7 +105,8 @@ export default function NewAnalysis() {
 
   return (
     <div className="max-w-4xl mx-auto px-6 py-8 space-y-8">
-
+        <ApiError message={error} onDismiss={() => setError(null)} />
+        
       {/* Step 1 — Analysis Type */}
       <section>
         <SectionLabel step={1} label="Analysis Type" />
diff --git a/frontend/src/pages/Upload.tsx b/frontend/src/pages/Upload.tsx
index 5107689..025ec17 100644
--- a/frontend/src/pages/Upload.tsx
+++ b/frontend/src/pages/Upload.tsx
@@ -8,6 +8,7 @@ import { MapBBoxPicker } from '../components/Map/MapBBoxPicker'
 import { ErrorBoundary } from '../components/ui/ErrorBoundary'
 import { useToast } from '../contexts/ToastContext'
 import { useApp } from '../contexts/AppContext'
+import { ApiError } from '../components/ui/ApiError'
 
 const ACCEPTED = ['.tif', '.tiff', '.geotiff', '.nc', '.hdf5']
 const MAX_MB = 500
@@ -31,6 +32,7 @@ export default function Upload() {
   const [startDate, setStartDate] = useState('')
   const [endDate, setEndDate] = useState('')
   const [busy, setBusy] = useState(false)
+  const [error, setError] = useState<string | null>(null)
   const [uploadProgress, setUploadProgress] = useState<number | null>(null)
   const fileInputRef = useRef<HTMLInputElement>(null)
 
@@ -57,6 +59,7 @@ export default function Upload() {
   const handleUpload = async () => {
     if (!file) return
     setBusy(true)
+    setError(null);
     setUploadProgress(0)
 
     try {
@@ -75,9 +78,14 @@ export default function Upload() {
       })
       setFile(null)
       setUploadProgress(null)
-    } catch (e) {
-      showToast('error', String(e))
-      setUploadProgress(null)
+    } catch (e:any) {
+     const message =
+        e?.response?.data?.detail ||
+        e?.response?.data?.message ||
+        e?.message ||
+        'Upload failed'
+      setError(message);
+      setUploadProgress(null);
     } finally {
       setBusy(false)
     }
@@ -85,7 +93,7 @@ export default function Upload() {
 
   return (
     <div className="max-w-3xl mx-auto px-6 py-8 space-y-6">
-
+        <ApiError message={error} onDismiss={() => setError(null)} />
       {/* Drop Zone */}
       <div
         onDragOver={(e) => { e.preventDefault(); setDragging(true) }}
diff --git a/team_docs/Francis_Umo_Role.pdf b/team_docs/Francis_Umo_Role.pdf
new file mode 100644
index 0000000..a263331
Binary files /dev/null and b/team_docs/Francis_Umo_Role.pdf differ
diff --git a/team_docs/Olufemi_Taiwo_Role.pdf b/team_docs/Olufemi_Taiwo_Role.pdf
new file mode 100644
index 0000000..9367f2c
Binary files /dev/null and b/team_docs/Olufemi_Taiwo_Role.pdf differ
diff --git a/team_docs/Victor_Mbachu_Role.pdf b/team_docs/Victor_Mbachu_Role.pdf
new file mode 100644
index 0000000..6e747fb
Binary files /dev/null and b/team_docs/Victor_Mbachu_Role.pdf differ
diff --git a/team_docs/generate_role_docs.py b/team_docs/generate_role_docs.py
new file mode 100644
index 0000000..0c4aaf2
--- /dev/null
+++ b/team_docs/generate_role_docs.py
@@ -0,0 +1,2312 @@
+#!/usr/bin/env python3
+"""
+Generate personalized ClimateVision role assignment PDFs for each team member.
+"""
+
+from fpdf import FPDF
+import os
+
+OUTPUT_DIR = "/Users/starrexshotit/Desktop/ClimateVision-main/team_docs"
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+
+class RoleDoc(FPDF):
+    def __init__(self, member_name):
+        super().__init__()
+        self.member_name = member_name
+
+    def header(self):
+        # Green header bar
+        self.set_fill_color(34, 120, 74)
+        self.rect(0, 0, 210, 28, 'F')
+        self.set_font("Helvetica", "B", 16)
+        self.set_text_color(255, 255, 255)
+        self.set_y(5)
+        self.cell(0, 10, "ClimateVision", align="L", new_x="LMARGIN", new_y="NEXT")
+        self.set_font("Helvetica", "", 9)
+        self.cell(0, 6, "Role Assignment & Codebase Ownership", align="L", new_x="LMARGIN", new_y="NEXT")
+        self.set_text_color(0, 0, 0)
+        self.ln(10)
+
+    def footer(self):
+        self.set_y(-15)
+        self.set_font("Helvetica", "I", 8)
+        self.set_text_color(130, 130, 130)
+        self.cell(0, 10, f"ClimateVision | Confidential - Prepared for {self.member_name} | Page {self.page_no()}", align="C")
+
+    def section_title(self, title):
+        self.set_font("Helvetica", "B", 13)
+        self.set_text_color(34, 120, 74)
+        self.cell(0, 8, title, new_x="LMARGIN", new_y="NEXT")
+        # Underline
+        self.set_draw_color(34, 120, 74)
+        self.set_line_width(0.5)
+        self.line(10, self.get_y(), 200, self.get_y())
+        self.ln(4)
+        self.set_text_color(0, 0, 0)
+
+    def subsection_title(self, title):
+        self.set_font("Helvetica", "B", 11)
+        self.set_text_color(50, 50, 50)
+        self.cell(0, 7, title, new_x="LMARGIN", new_y="NEXT")
+        self.ln(1)
+        self.set_text_color(0, 0, 0)
+
+    def _sanitize(self, text):
+        """Replace unicode chars that latin-1 can't handle."""
+        replacements = {
+            '\u2013': '-',   # en dash
+            '\u2014': '-',   # em dash
+            '\u2018': "'",   # left single quote
+            '\u2019': "'",   # right single quote
+            '\u201c': '"',   # left double quote
+            '\u201d': '"',   # right double quote
+            '\u2022': '-',   # bullet
+            '\u2026': '...', # ellipsis
+        }
+        for old, new in replacements.items():
+            text = text.replace(old, new)
+        return text
+
+    def body_text(self, text):
+        self.set_font("Helvetica", "", 10)
+        self.multi_cell(0, 5.5, self._sanitize(text))
+        self.ln(2)
+
+    def bullet(self, text):
+        self.set_font("Helvetica", "", 10)
+        x = self.get_x()
+        self.cell(6, 5.5, "-", new_x="END")
+        self.multi_cell(0, 5.5, self._sanitize(text))
+        self.ln(1)
+
+    def code_block(self, text):
+        self.set_font("Courier", "", 9)
+        self.set_fill_color(240, 240, 240)
+        lines = text.strip().split("\n")
+        for line in lines:
+            self.cell(0, 5, "  " + line, fill=True, new_x="LMARGIN", new_y="NEXT")
+        self.ln(3)
+        self.set_font("Helvetica", "", 10)
+
+    def key_value(self, key, value):
+        self.set_font("Helvetica", "B", 10)
+        self.cell(45, 6, self._sanitize(key) + ":", new_x="END")
+        self.set_font("Helvetica", "", 10)
+        self.multi_cell(0, 6, self._sanitize(value))
+        self.ln(1)
+
+    def month_block(self, month_title, weeks):
+        self.set_font("Helvetica", "B", 10)
+        self.set_fill_color(34, 120, 74)
+        self.set_text_color(255, 255, 255)
+        self.cell(0, 7, "  " + month_title, fill=True, new_x="LMARGIN", new_y="NEXT")
+        self.set_text_color(0, 0, 0)
+        self.ln(2)
+        for week_title, tasks in weeks:
+            self.set_font("Helvetica", "B", 10)
+            self.cell(0, 6, week_title, new_x="LMARGIN", new_y="NEXT")
+            self.ln(1)
+            for task in tasks:
+                self.bullet(task)
+        self.ln(2)
+
+
+def create_adeolu_doc():
+    pdf = RoleDoc("Adeolu Mary Oshadare")
+    pdf.add_page()
+
+    # Title
+    pdf.set_font("Helvetica", "B", 18)
+    pdf.cell(0, 10, "Adeolu Mary Oshadare", align="C", new_x="LMARGIN", new_y="NEXT")
+    pdf.set_font("Helvetica", "", 11)
+    pdf.set_text_color(100, 100, 100)
+    pdf.cell(0, 7, "Data Science Engineer 2 - Data Pipeline & GIS Lead", align="C", new_x="LMARGIN", new_y="NEXT")
+    pdf.set_text_color(0, 0, 0)
+    pdf.ln(5)
+
+    # Quick Info
+    pdf.key_value("GitHub", "@Oshgig")
+    pdf.key_value("Access Level", "Maintainer")
+    pdf.key_value("Reports To", "@Goldokpa (Project Owner)")
+    pdf.key_value("Project Duration", "3 Months")
+    pdf.ln(3)
+
+    # How It Fits Me
+    pdf.section_title("How This Role Fits You")
+    pdf.body_text(
+        "Your B.Tech in Remote Sensing & GIS from FUTA gives you something no one else on this team has - "
+        "a formal education in exactly the kind of spatial data ClimateVision processes. You understand "
+        "satellite imagery at a fundamental level: spectral bands, atmospheric correction, spatial resolution, "
+        "and coordinate reference systems."
+    )
+    pdf.body_text(
+        "As a GIS Analyst at Charis Tech Hub, you already worked with Google Earth Engine and AWS, writing "
+        "Python scripts to model and extract insights from large geospatial datasets. That is precisely what "
+        "ClimateVision's data pipeline needs - someone who can build the bridge between raw Sentinel-2 imagery "
+        "and the clean, preprocessed tensors our ML models consume."
+    )
+    pdf.body_text(
+        "Your MSc in Data Science from Hertfordshire added the machine learning layer: Scikit-Learn, TensorFlow, "
+        "XGBoost, Pandas, and data pipelines. Your credit card fraud detection project showed you can handle "
+        "imbalanced datasets (SMOTE) and build production-quality ML models - the same skills needed when dealing "
+        "with satellite imagery where cloud-free forest pixels are the minority class."
+    )
+    pdf.body_text(
+        "Your experience with Power BI, Tableau, ArcGIS Story Maps, and data storytelling means you can also "
+        "create the visual outputs that make our satellite data understandable to non-technical stakeholders "
+        "like conservation NGOs."
+    )
+    pdf.ln(2)
+
+    # Role Description
+    pdf.section_title("Your Role on ClimateVision")
+    pdf.body_text(
+        "You own the entire data layer - everything that happens between raw satellite imagery arriving from "
+        "APIs and clean, model-ready data being passed to the ML pipeline. You are the gatekeeper of data quality."
+    )
+    pdf.subsection_title("Core Responsibilities")
+    pdf.bullet("Build and maintain the automated satellite data ingestion pipeline (Sentinel Hub, Google Earth Engine)")
+    pdf.bullet("Develop preprocessing workflows: cloud masking, atmospheric correction, image normalization, tiling")
+    pdf.bullet("Create PyTorch Dataset & DataLoader classes for training and inference")
+    pdf.bullet("Implement data augmentation strategies (rotation, flipping, spectral perturbations)")
+    pdf.bullet("Engineer spectral features: NDVI, EVI, moisture indices from raw multispectral bands")
+    pdf.bullet("Build data validation and quality checks for incoming satellite imagery")
+    pdf.bullet("Manage the data/ directory structure (raw, processed, satellite)")
+    pdf.bullet("Create EDA notebooks for spatial data exploration and visualization")
+    pdf.ln(2)
+
+    # Codebase Ownership
+    pdf.section_title("Your Codebase Ownership")
+    pdf.body_text("You are the primary owner of the following files and directories:")
+    pdf.code_block(
+        "src/climatevision/data/              # PRIMARY OWNER - Entire data module\n"
+        "  sentinel2.py                        # Sentinel-2 downloader & preprocessor\n"
+        "  landsat.py                          # Landsat data loader\n"
+        "  dataset.py                          # PyTorch Dataset classes\n"
+        "  preprocess.py                       # Cloud masking, normalization\n"
+        "  augmentation.py                     # Data augmentation pipeline\n"
+        "  __init__.py                         # Module exports\n"
+        "\n"
+        "src/climatevision/utils/\n"
+        "  geospatial.py                       # CO-OWNER - Geospatial utilities\n"
+        "  visualization.py                    # CO-OWNER - Spatial visualizations\n"
+        "\n"
+        "scripts/\n"
+        "  setup_gee.py                        # Google Earth Engine setup\n"
+        "  download_data.py                    # Automated satellite data download\n"
+        "\n"
+        "data/                                 # Data directory structure\n"
+        "  raw/ | processed/ | satellite/\n"
+        "\n"
+        "notebooks/\n"
+        "  02_data_exploration.ipynb            # EDA notebook"
+    )
+    pdf.ln(2)
+
+    # 3-Month Timeline
+    pdf.section_title("Your 3-Month Delivery Timeline")
+    pdf.month_block("MONTH 1: Foundation (Weeks 1-4)", [
+        ("Week 1-2: Data Ingestion", [
+            "Set up Sentinel Hub API and Google Earth Engine authentication",
+            "Build sentinel2.py - download, parse, and store Sentinel-2 imagery",
+            "Create landsat.py - Landsat 8/9 data loader with band mapping",
+            "Implement basic cloud masking using SCL (Scene Classification Layer)",
+        ]),
+        ("Week 3-4: PyTorch Data Pipeline", [
+            "Build dataset.py - PyTorch Dataset class for satellite image tiles",
+            "Implement preprocess.py - normalization, atmospheric correction, tiling (256x256)",
+            "Create data validation checks (band count, resolution, CRS consistency)",
+            "Write 02_data_exploration.ipynb - EDA notebook with sample visualizations",
+        ]),
+    ])
+    pdf.month_block("MONTH 2: Advanced Features (Weeks 5-8)", [
+        ("Week 5-6: Feature Engineering & Augmentation", [
+            "Implement spectral index calculation: NDVI, EVI, SAVI, moisture indices",
+            "Build augmentation.py using albumentations (rotation, flip, spectral noise)",
+            "Add temporal compositing - median/max NDVI composites over time windows",
+        ]),
+        ("Week 7-8: Scale & Performance", [
+            "Integrate Dask for distributed preprocessing of large image collections",
+            "Optimize data loading with parallel I/O and memory-mapped files",
+            "Build data caching layer for preprocessed tiles",
+        ]),
+    ])
+    pdf.month_block("MONTH 3: Production Readiness (Weeks 9-12)", [
+        ("Week 9-10: Quality & Validation", [
+            "Implement data validation framework (schema checks, anomaly detection)",
+            "Set up DVC (Data Version Control) for dataset tracking",
+            "Create data quality reports and monitoring dashboards",
+        ]),
+        ("Week 11-12: Documentation & Integration", [
+            "Write comprehensive docstrings and module documentation",
+            "Integration testing with ML pipeline (ensure DataLoader feeds models correctly)",
+            "Create data pipeline tutorial notebook for onboarding",
+        ]),
+    ])
+
+    # Git Workflow
+    pdf.section_title("Your Git Workflow")
+    pdf.body_text("Follow this branching convention for all your work:")
+    pdf.code_block(
+        "# Create feature branches from develop\n"
+        "git checkout develop\n"
+        "git pull origin develop\n"
+        "git checkout -b feature/data-sentinel2-loader\n"
+        "\n"
+        "# Your branch naming convention:\n"
+        "feature/data-*          (new data features)\n"
+        "fix/data-*              (bug fixes in data module)\n"
+        "refactor/data-*         (restructuring data code)"
+    )
+    pdf.body_text(
+        "All PRs go to the develop branch. PRs require at least 1 review from another team member. "
+        "Tag @edoh-Onuh or @franchaise for data-related reviews since they consume your data outputs."
+    )
+    pdf.ln(3)
+
+    # Key Collaborators
+    pdf.section_title("Your Key Collaborators")
+    pdf.bullet("@edoh-Onuh (ML Lead) - Your DataLoaders feed directly into their training pipeline. Coordinate on tensor shapes, normalization, and augmentation strategies.")
+    pdf.bullet("@franchaise (Analytics Lead) - They need processed data for carbon estimation. Align on feature formats and metadata.")
+    pdf.bullet("Olufemi Taiwo (API Lead) - Inference pipeline uses your preprocessing code. Ensure consistency between training and inference data paths.")
+    pdf.bullet("@cutewizzy11 (Full-Stack) - Frontend map visualizations may need GeoJSON exports from your geospatial utils.")
+
+    # Code Pipeline
+    pdf.section_title("Your Code Pipeline")
+    pdf.body_text("This is your end-to-end working pipeline from environment setup to pushing code.")
+
+    pdf.subsection_title("Step 1: Environment Setup")
+    pdf.code_block(
+        "# Clone and install dependencies\n"
+        "git clone https://github.com/Climate-Vision/ClimateVision.git\n"
+        "cd ClimateVision\n"
+        "pip install -r requirements.txt\n"
+        "\n"
+        "# Authenticate Google Earth Engine\n"
+        "python scripts/setup_gee.py\n"
+        "# Follow browser prompt to authorise your GEE service account"
+    )
+
+    pdf.subsection_title("Step 2: Ingest Satellite Data")
+    pdf.code_block(
+        "# Download Sentinel-2 imagery for a bounding box and date range\n"
+        "python scripts/prepare_data.py \\\n"
+        "  --bbox \"-60,-15,-45,5\" \\\n"
+        "  --start 2023-01-01 \\\n"
+        "  --end   2023-12-31 \\\n"
+        "  --source sentinel2 \\\n"
+        "  --output data/raw/amazon_2023\n"
+        "\n"
+        "# Output: GeoTIFF tiles saved to data/raw/amazon_2023/"
+    )
+
+    pdf.subsection_title("Step 3: Preprocess & Build Dataset")
+    pdf.code_block(
+        "# Run cloud masking, normalization, and 256x256 tiling\n"
+        "python - <<'EOF'\n"
+        "from climatevision.data.preprocessing import preprocess_tiles\n"
+        "preprocess_tiles(\n"
+        "    input_dir='data/raw/amazon_2023/',\n"
+        "    output_dir='data/processed/amazon_2023/',\n"
+        "    tile_size=256,\n"
+        "    cloud_threshold=0.2\n"
+        ")\n"
+        "EOF\n"
+        "\n"
+        "# Validate the PyTorch dataset loads correctly\n"
+        "python - <<'EOF'\n"
+        "from climatevision.data.dataset import SatelliteDataset\n"
+        "ds = SatelliteDataset('data/processed/amazon_2023/', split='train')\n"
+        "img, mask = ds[0]\n"
+        "print(f'Dataset size: {len(ds)} | Image shape: {img.shape} | Mask shape: {mask.shape}')\n"
+        "EOF"
+    )
+
+    pdf.subsection_title("Step 4: Compute Spectral Indices")
+    pdf.code_block(
+        "# Calculate NDVI, EVI, and moisture indices from raw bands\n"
+        "python - <<'EOF'\n"
+        "from climatevision.utils.geospatial import compute_indices\n"
+        "compute_indices(\n"
+        "    tile_dir='data/processed/amazon_2023/',\n"
+        "    indices=['ndvi', 'evi', 'moisture'],\n"
+        "    output_dir='data/processed/amazon_2023_features/'\n"
+        ")\n"
+        "EOF"
+    )
+
+    pdf.subsection_title("Step 5: Commit & Push Your Work")
+    pdf.code_block(
+        "# Switch to your git identity\n"
+        "source team_docs/switch_user.sh adeolu\n"
+        "\n"
+        "# Create a feature branch\n"
+        "git checkout develop\n"
+        "git pull origin develop\n"
+        "git checkout -b feature/data-sentinel2-preprocessing\n"
+        "\n"
+        "# Stage your files\n"
+        "git add src/climatevision/data/\n"
+        "git add scripts/prepare_data.py\n"
+        "\n"
+        "# Commit\n"
+        "git commit -m \"feat(data): add Sentinel-2 cloud masking and tile preprocessing pipeline\"\n"
+        "\n"
+        "# Push from your account\n"
+        "git push adeolu feature/data-sentinel2-preprocessing"
+    )
+
+    pdf.output(os.path.join(OUTPUT_DIR, "Adeolu_Mary_Oshadare_Role.pdf"))
+    print("Created: Adeolu_Mary_Oshadare_Role.pdf")
+
+
+def create_francis_doc():
+    pdf = RoleDoc("Francis Umo")
+    pdf.add_page()
+
+    pdf.set_font("Helvetica", "B", 18)
+    pdf.cell(0, 10, "Francis Umo", align="C", new_x="LMARGIN", new_y="NEXT")
+    pdf.set_font("Helvetica", "", 11)
+    pdf.set_text_color(100, 100, 100)
+    pdf.cell(0, 7, "Data Science Engineer 3 - Carbon Analytics & Validation Lead", align="C", new_x="LMARGIN", new_y="NEXT")
+    pdf.set_text_color(0, 0, 0)
+    pdf.ln(5)
+
+    pdf.key_value("GitHub", "@franchaise")
+    pdf.key_value("Access Level", "Maintainer")
+    pdf.key_value("Reports To", "@Goldokpa (Project Owner)")
+    pdf.key_value("Project Duration", "3 Months")
+    pdf.ln(3)
+
+    # How It Fits Me
+    pdf.section_title("How This Role Fits You")
+    pdf.body_text(
+        "With 8+ years of progressive experience in data analysis and business intelligence, you bring "
+        "the deepest analytical maturity on this team. While others focus on building models and pipelines, "
+        "you are the person who makes sure the numbers tell the right story and that the results are trustworthy."
+    )
+    pdf.body_text(
+        "Your expertise in Python, PostgreSQL, and SQL means you can build the carbon estimation models that "
+        "require heavy data querying, aggregation, and statistical analysis. At Dataleum, you conducted data "
+        "quality checks, developed dashboards to monitor financial data, and created reports that reduced fraud "
+        "by 80% - that same rigour is exactly what's needed when validating whether our ML models are correctly "
+        "estimating carbon loss from deforestation."
+    )
+    pdf.body_text(
+        "Your proficiency in Tableau and Power BI is a direct match for building the impact reporting layer. "
+        "ClimateVision needs to produce clear, visual reports that conservation organizations and government "
+        "agencies can act on. Your data storytelling background makes you the ideal person to translate "
+        "raw model outputs into actionable intelligence."
+    )
+    pdf.body_text(
+        "Your cross-functional collaboration experience - working with IT teams, stakeholders, and bringing "
+        "analytical models into production - means you understand how to bridge the gap between a data science "
+        "experiment and a production metric that decision-makers rely on."
+    )
+    pdf.ln(2)
+
+    # Role Description
+    pdf.section_title("Your Role on ClimateVision")
+    pdf.body_text(
+        "You own the analytics and validation layer - everything that turns raw model predictions into "
+        "meaningful environmental metrics. If the ML model says 'this pixel is deforested,' you quantify "
+        "what that means in tons of carbon, hectares of forest, and dollars of environmental impact."
+    )
+    pdf.subsection_title("Core Responsibilities")
+    pdf.bullet("Develop carbon stock estimation models (Random Forest, XGBoost regression)")
+    pdf.bullet("Build biomass-to-carbon conversion pipelines using allometric equations")
+    pdf.bullet("Implement uncertainty quantification (bootstrap, Monte Carlo, confidence intervals)")
+    pdf.bullet("Create ground truth validation framework - compare model outputs to known data")
+    pdf.bullet("Build statistical testing suite (hypothesis testing, A/B testing for model versions)")
+    pdf.bullet("Design and generate impact reports (area deforested, carbon lost, trends over time)")
+    pdf.bullet("Develop KPI dashboards for monitoring model performance and environmental outcomes")
+    pdf.bullet("Create validation notebooks demonstrating model accuracy across regions")
+    pdf.ln(2)
+
+    # Codebase Ownership
+    pdf.section_title("Your Codebase Ownership")
+    pdf.body_text("You are the primary owner of the following files and directories:")
+    pdf.code_block(
+        "src/climatevision/analytics/          # PRIMARY OWNER - New analytics module\n"
+        "  carbon.py                            # Carbon stock estimation models\n"
+        "  statistics.py                        # Statistical testing & analysis\n"
+        "  reporting.py                         # Impact report generation\n"
+        "  validation.py                        # Ground truth validation framework\n"
+        "  __init__.py                          # Module exports\n"
+        "\n"
+        "src/climatevision/models/\n"
+        "  regression.py                        # PRIMARY OWNER - Biomass/carbon regression\n"
+        "\n"
+        "src/climatevision/utils/\n"
+        "  metrics.py                           # CO-OWNER - Extend with carbon metrics\n"
+        "\n"
+        "notebooks/\n"
+        "  03_carbon_analysis.ipynb             # Carbon estimation analysis\n"
+        "  04_model_validation.ipynb            # Validation & benchmarking\n"
+        "  05_impact_reporting.ipynb            # Reporting notebook\n"
+        "\n"
+        "outputs/\n"
+        "  reports/                             # Generated impact reports\n"
+        "  dashboards/                          # Dashboard configs"
+    )
+    pdf.ln(2)
+
+    # 3-Month Timeline
+    pdf.section_title("Your 3-Month Delivery Timeline")
+    pdf.month_block("MONTH 1: Foundation (Weeks 1-4)", [
+        ("Week 1-2: Carbon Estimation Models", [
+            "Research allometric equations for biomass estimation by forest type",
+            "Build carbon.py - Random Forest & XGBoost regression for biomass prediction",
+            "Create feature pipeline: spectral indices -> biomass -> carbon conversion",
+            "Implement metrics for regression evaluation (RMSE, MAE, R-squared)",
+        ]),
+        ("Week 3-4: Validation Framework", [
+            "Build validation.py - compare model predictions to ground truth datasets",
+            "Source and integrate reference data (Global Forest Watch, forest inventory data)",
+            "Create confusion matrix, precision/recall analysis for segmentation outputs",
+            "Write 04_model_validation.ipynb with baseline validation results",
+        ]),
+    ])
+    pdf.month_block("MONTH 2: Advanced Analytics (Weeks 5-8)", [
+        ("Week 5-6: Uncertainty & Statistical Testing", [
+            "Implement bootstrap confidence intervals for carbon estimates",
+            "Build Monte Carlo simulation for uncertainty propagation",
+            "Create statistics.py - hypothesis testing, trend analysis functions",
+            "Implement A/B testing framework for comparing model versions",
+        ]),
+        ("Week 7-8: Impact Reporting", [
+            "Build reporting.py - automated report generation (PDF/HTML)",
+            "Design KPI framework: hectares lost, carbon tons, trend direction",
+            "Create 05_impact_reporting.ipynb - template for regional impact reports",
+            "Integrate with PostgreSQL for historical metric storage",
+        ]),
+    ])
+    pdf.month_block("MONTH 3: Production Readiness (Weeks 9-12)", [
+        ("Week 9-10: Dashboard & Integration", [
+            "Build dashboard data endpoints (feed metrics to frontend charts)",
+            "Create time-series analysis for deforestation trend tracking",
+            "Implement anomaly detection for unusual forest loss patterns",
+        ]),
+        ("Week 11-12: Documentation & Case Studies", [
+            "Produce 3 regional case study reports (Amazon, Congo, Southeast Asia)",
+            "Write comprehensive documentation for analytics module",
+            "Final validation sweep across all model outputs",
+            "Performance benchmarking and accuracy documentation",
+        ]),
+    ])
+
+    # Git Workflow
+    pdf.section_title("Your Git Workflow")
+    pdf.code_block(
+        "# Create feature branches from develop\n"
+        "git checkout develop\n"
+        "git pull origin develop\n"
+        "git checkout -b feature/analytics-carbon-estimation\n"
+        "\n"
+        "# Your branch naming convention:\n"
+        "feature/analytics-*     (new analytics features)\n"
+        "fix/analytics-*         (bug fixes)\n"
+        "refactor/analytics-*    (code restructuring)"
+    )
+    pdf.body_text(
+        "All PRs go to the develop branch. PRs require at least 1 review. "
+        "Tag @edoh-Onuh for reviews on model evaluation metrics, and @Oshgig for data format questions."
+    )
+    pdf.ln(3)
+
+    # Key Collaborators
+    pdf.section_title("Your Key Collaborators")
+    pdf.bullet("@edoh-Onuh (ML Lead) - Their model predictions are your primary input. Coordinate on output formats, probability thresholds, and confidence scores.")
+    pdf.bullet("@Oshgig (Data Pipeline Lead) - She provides the preprocessed data you need for carbon regression features. Align on spectral indices and metadata.")
+    pdf.bullet("Olufemi Taiwo (API Lead) - Your analytics endpoints need to be exposed through the API. Coordinate on response schemas.")
+    pdf.bullet("@cutewizzy11 (Full-Stack) - Frontend dashboards visualize your metrics. Provide JSON data contracts for charts.")
+
+    # Code Pipeline
+    pdf.section_title("Your Code Pipeline")
+    pdf.body_text("Your pipeline starts where the ML model ends - taking prediction masks and turning them into carbon impact numbers and stakeholder reports.")
+
+    pdf.subsection_title("Step 1: Environment Setup")
+    pdf.code_block(
+        "git clone https://github.com/Climate-Vision/ClimateVision.git\n"
+        "cd ClimateVision\n"
+        "pip install -r requirements.txt\n"
+        "\n"
+        "# Verify analytics dependencies\n"
+        "python -c \"import xgboost, sklearn, mlflow, optuna; print('Analytics stack ready')\""
+    )
+
+    pdf.subsection_title("Step 2: Run Inference to Get Prediction Masks")
+    pdf.code_block(
+        "# Generate deforestation masks from a trained model\n"
+        "python scripts/infer.py \\\n"
+        "  --bbox \"-60,-15,-45,5\" \\\n"
+        "  --date 2023-06-01 \\\n"
+        "  --analysis_type deforestation \\\n"
+        "  --output outputs/masks/\n"
+        "\n"
+        "# Output: outputs/masks/deforestation_mask.tif + confidence_scores.npy"
+    )
+
+    pdf.subsection_title("Step 3: Estimate Carbon Loss")
+    pdf.code_block(
+        "# Run carbon stock estimation on the prediction mask\n"
+        "python - <<'EOF'\n"
+        "from climatevision.analytics.carbon import estimate_carbon\n"
+        "result = estimate_carbon(\n"
+        "    mask_path='outputs/masks/deforestation_mask.tif',\n"
+        "    region='amazon',\n"
+        "    forest_type='tropical_moist'\n"
+        ")\n"
+        "print(f\"Deforested area: {result['hectares']:.1f} ha\")\n"
+        "print(f\"Carbon lost:     {result['carbon_tonnes']:.1f} tCO2e\")\n"
+        "print(f\"Confidence CI:   {result['ci_lower']:.1f} - {result['ci_upper']:.1f} tCO2e\")\n"
+        "EOF"
+    )
+
+    pdf.subsection_title("Step 4: Validate Against Ground Truth")
+    pdf.code_block(
+        "# Compare model outputs to Global Forest Watch reference data\n"
+        "python - <<'EOF'\n"
+        "from climatevision.analytics.validation import validate_predictions\n"
+        "metrics = validate_predictions(\n"
+        "    pred_mask='outputs/masks/deforestation_mask.tif',\n"
+        "    ground_truth='data/ground_truth/amazon_gfw_2023.tif'\n"
+        ")\n"
+        "print(f\"IoU: {metrics['iou']:.3f} | F1: {metrics['f1']:.3f} | Precision: {metrics['precision']:.3f}\")\n"
+        "EOF"
+    )
+
+    pdf.subsection_title("Step 5: Generate Impact Report")
+    pdf.code_block(
+        "# Auto-generate a PDF/HTML impact report for stakeholders\n"
+        "python - <<'EOF'\n"
+        "from climatevision.analytics.reporting import generate_report\n"
+        "generate_report(\n"
+        "    region='amazon',\n"
+        "    period='2023-Q2',\n"
+        "    carbon_result=result,\n"
+        "    validation_metrics=metrics,\n"
+        "    output_dir='outputs/reports/'\n"
+        ")\n"
+        "EOF\n"
+        "\n"
+        "# Output: outputs/reports/amazon_2023-Q2_impact_report.pdf"
+    )
+
+    pdf.subsection_title("Step 7: Commit & Push Your Work")
+    pdf.code_block(
+        "# Switch to your git identity\n"
+        "source team_docs/switch_user.sh francis\n"
+        "\n"
+        "git checkout develop && git pull origin develop\n"
+        "git checkout -b feature/analytics-carbon-estimation\n"
+        "\n"
+        "git add src/climatevision/analytics/\n"
+        "git add notebooks/03_carbon_analysis.ipynb\n"
+        "git commit -m \"feat(analytics): add carbon stock estimation with confidence intervals\"\n"
+        "\n"
+        "git push francis feature/analytics-carbon-estimation"
+    )
+
+    pdf.output(os.path.join(OUTPUT_DIR, "Francis_Umo_Role.pdf"))
+    print("Created: Francis_Umo_Role.pdf")
+
+
+def create_olufemi_doc():
+    pdf = RoleDoc("Olufemi Taiwo")
+    pdf.add_page()
+
+    pdf.set_font("Helvetica", "B", 18)
+    pdf.cell(0, 10, "Olufemi Taiwo", align="C", new_x="LMARGIN", new_y="NEXT")
+    pdf.set_font("Helvetica", "", 11)
+    pdf.set_text_color(100, 100, 100)
+    pdf.cell(0, 7, "Data Science Engineer 4 - API & Data Quality Lead", align="C", new_x="LMARGIN", new_y="NEXT")
+    pdf.set_text_color(0, 0, 0)
+    pdf.ln(5)
+
+    pdf.key_value("GitHub", "(To be assigned)")
+    pdf.key_value("Access Level", "Maintainer")
+    pdf.key_value("Reports To", "@Goldokpa (Project Owner)")
+    pdf.key_value("Project Duration", "3 Months")
+    pdf.ln(3)
+
+    # How It Fits Me
+    pdf.section_title("How This Role Fits You")
+    pdf.body_text(
+        "Your current role as Reporting and Data Quality Officer at the Royal Marsden NHS Foundation Trust "
+        "is the clearest signal for this assignment. Every working day you validate data flows, investigate "
+        "mismatches across Epic EPR, troubleshoot system errors using SQL, and hold the line on reporting "
+        "accuracy for senior clinical stakeholders. That obsessive attention to data integrity at every step "
+        "from input to output is exactly what ClimateVision's API and inference pipeline need."
+    )
+    pdf.body_text(
+        "At Fidelity Bank, you kept payment platforms reliable around the clock as an Application Support "
+        "Analyst - triaging incidents, analysing root causes, and producing service reports that guided "
+        "operational decisions. ClimateVision runs a similar system: satellite images arrive as requests, "
+        "the API must respond correctly and quickly, and any failure needs to be caught, logged, and "
+        "escalated before it reaches users. That is your wheelhouse."
+    )
+    pdf.body_text(
+        "Your Business Intelligence work at Dataleum - building Power BI dashboards, conducting data quality "
+        "checks, achieving 98% GDPR compliance - means you already understand auditability. In a climate "
+        "monitoring system used by NGOs and government agencies, every prediction must be traceable, every "
+        "alert explainable, and every data flow compliant. You build that confidence layer."
+    )
+    pdf.body_text(
+        "Your ITIL 4 certification is a direct fit for incident management, change control, and problem "
+        "management in production. Combined with your MSc in Data Science, you are the person who makes "
+        "the API not just functional, but operationally trustworthy - with structured logging, audit trails, "
+        "validated schemas, and monitoring that surfaces issues before users notice them."
+    )
+    pdf.ln(2)
+
+    # Role Description
+    pdf.section_title("Your Role on ClimateVision")
+    pdf.body_text(
+        "You own the API layer and the inference pipeline - everything between a trained model and a user "
+        "receiving a validated, structured response. You ensure the system is reliable, observable, and "
+        "produces outputs that are correct and auditable. You are the data quality gatekeeper for every "
+        "prediction that leaves the system."
+    )
+    pdf.subsection_title("Core Responsibilities")
+    pdf.bullet("Extend and maintain the FastAPI backend (endpoints, authentication, request validation)")
+    pdf.bullet("Build Pydantic schemas for all API request/response objects - the contract for data quality")
+    pdf.bullet("Implement structured logging, error handling, and audit trails throughout the inference flow")
+    pdf.bullet("Build the inference validation layer - catch bad inputs, validate outputs, flag anomalies")
+    pdf.bullet("Create the deforestation alert system with configurable thresholds and notification routing")
+    pdf.bullet("Build API monitoring endpoints: health checks, data quality metrics, run status dashboards")
+    pdf.bullet("Write SQL queries and admin endpoints for operational reporting and data audits")
+    pdf.bullet("Design and document the API contract (request/response schemas, error codes, versioning)")
+    pdf.ln(2)
+
+    # Codebase Ownership
+    pdf.section_title("Your Codebase Ownership")
+    pdf.body_text("You are the primary owner of the following files and directories:")
+    pdf.code_block(
+        "src/climatevision/inference/           # PRIMARY OWNER\n"
+        "  pipeline.py                          # Core inference pipeline\n"
+        "  batch_processor.py                   # Batch processing with job queuing\n"
+        "  postprocess.py                       # Output filtering & thresholding\n"
+        "  alert_generator.py                   # Deforestation alert system\n"
+        "  __init__.py\n"
+        "\n"
+        "src/climatevision/api/                 # PRIMARY OWNER\n"
+        "  main.py                              # FastAPI application\n"
+        "  auth.py                              # API key authentication\n"
+        "  middleware.py                         # Request logging, CORS\n"
+        "  schemas.py                           # Pydantic request/response schemas\n"
+        "  __init__.py\n"
+        "\n"
+        "src/climatevision/db.py                # CO-OWNER - Database & audit queries\n"
+        "\n"
+        "run_api.sh                             # API startup script\n"
+        "config.yaml                            # API & inference config sections"
+    )
+    pdf.ln(2)
+
+    # 3-Month Timeline
+    pdf.section_title("Your 3-Month Delivery Timeline")
+    pdf.month_block("MONTH 1: Foundation (Weeks 1-4)", [
+        ("Week 1-2: Schemas & Validation", [
+            "Build schemas.py - Pydantic models for every API request and response object",
+            "Extend pipeline.py with input validation: image shape, band count, coordinate bounds",
+            "Add structured JSON logging throughout the inference flow (request ID, timestamps, errors)",
+            "Implement output validation - flag predictions outside expected confidence ranges",
+        ]),
+        ("Week 3-4: API Hardening", [
+            "Implement auth.py - API key authentication and organisation-based access control",
+            "Build middleware.py - request logging, CORS, request size limits",
+            "Create /api/health, /api/status, and /api/metrics endpoints for operational monitoring",
+            "Write API integration tests covering validation edge cases and error responses",
+        ]),
+    ])
+    pdf.month_block("MONTH 2: Quality & Alerts (Weeks 5-8)", [
+        ("Week 5-6: Inference Quality Layer", [
+            "Build postprocess.py - confidence thresholding and prediction filtering",
+            "Implement anomaly detection for unusual inference outputs (flag for review)",
+            "Create audit log entries for every prediction: input hash, model version, output summary",
+            "Build batch_processor.py - parallel image processing with per-job status tracking",
+        ]),
+        ("Week 7-8: Alert System & Reporting", [
+            "Build alert_generator.py - configurable deforestation threshold alerting",
+            "Implement notification routing (email, webhook) for triggered alerts",
+            "Write SQL reporting queries for run history, error rates, and data quality KPIs",
+            "Create admin endpoints for operational dashboards: throughput, failure rates, alert volumes",
+        ]),
+    ])
+    pdf.month_block("MONTH 3: Observability & Documentation (Weeks 9-12)", [
+        ("Week 9-10: Monitoring & Data Quality Reports", [
+            "Build a /api/reports endpoint returning data quality metrics over configurable time windows",
+            "Implement request tracing: correlate API requests to inference runs to alerts",
+            "Create a data quality dashboard feed (JSON) for the frontend to visualise pipeline health",
+            "SQL-based audit trail queries: who requested what, when, and with what result",
+        ]),
+        ("Week 11-12: Documentation & Launch Readiness", [
+            "Write the API reference: all endpoints, schemas, error codes, and usage examples",
+            "Document the incident response runbook: what each error means and how to resolve it",
+            "Security review: input sanitisation, SQL injection checks, API key rotation procedures",
+            "Final integration testing with all team modules - validate end-to-end data flow",
+        ]),
+    ])
+
+    # Git Workflow
+    pdf.section_title("Your Git Workflow")
+    pdf.code_block(
+        "# Create feature branches from develop\n"
+        "git checkout develop\n"
+        "git pull origin develop\n"
+        "git checkout -b feature/api-schemas\n"
+        "\n"
+        "# Your branch naming convention:\n"
+        "feature/api-*           (API features & endpoints)\n"
+        "feature/inference-*     (inference pipeline & validation)\n"
+        "feature/schemas-*       (Pydantic schema changes)\n"
+        "fix/api-*               (bug fixes)"
+    )
+    pdf.body_text(
+        "All PRs go to the develop branch. Tag @cutewizzy11 for API contract reviews (he consumes your "
+        "endpoints from the frontend) and @edoh-Onuh when touching inference logic that involves model outputs."
+    )
+    pdf.ln(3)
+
+    # Key Collaborators
+    pdf.section_title("Your Key Collaborators")
+    pdf.bullet("@edoh-Onuh (ML Lead) - Their trained models are loaded by your inference pipeline. Coordinate on model format (.pth vs ONNX), input shapes, output schemas, and confidence score formats.")
+    pdf.bullet("@Oshgig (Data Pipeline Lead) - Your inference input validation must match her preprocessing exactly. Align on normalization constants, expected band order, and coordinate formats.")
+    pdf.bullet("@franchaise (Analytics Lead) - Their analytics endpoints are exposed through your API. Coordinate on response schemas, pagination, and data quality flags in outputs.")
+    pdf.bullet("@cutewizzy11 (Full-Stack & CI/CD) - He consumes your API from the frontend and manages Docker and deployment. You two define the API contract together - endpoints, schemas, error codes.")
+
+    # Code Pipeline
+    pdf.section_title("Your Code Pipeline")
+    pdf.body_text("Your pipeline covers running and validating the FastAPI server, testing all endpoints, enforcing data quality, and maintaining the inference layer.")
+
+    pdf.subsection_title("Step 1: Environment Setup")
+    pdf.code_block(
+        "git clone https://github.com/Climate-Vision/ClimateVision.git\n"
+        "cd ClimateVision\n"
+        "pip install -r requirements.txt\n"
+        "\n"
+        "# Set environment variables\n"
+        "cp .env.example .env\n"
+        "# Edit .env: set MODEL_PATH, DB_PATH, API_KEY_SECRET"
+    )
+
+    pdf.subsection_title("Step 2: Start the API Server")
+    pdf.code_block(
+        "# Start FastAPI in development mode with auto-reload\n"
+        "uvicorn climatevision.api.main:app \\\n"
+        "  --reload \\\n"
+        "  --host 0.0.0.0 \\\n"
+        "  --port 8000\n"
+        "\n"
+        "# Interactive API docs available at:\n"
+        "# http://localhost:8000/docs\n"
+        "# http://localhost:8000/redoc"
+    )
+
+    pdf.subsection_title("Step 3: Test Prediction Endpoints")
+    pdf.code_block(
+        "# Test JSON prediction endpoint\n"
+        "curl -X POST http://localhost:8000/predict/json \\\n"
+        "  -H \"Content-Type: application/json\" \\\n"
+        "  -d '{\n"
+        "    \"bbox\": [-60, -15, -45, 5],\n"
+        "    \"start_date\": \"2023-01-01\",\n"
+        "    \"end_date\":   \"2023-12-31\",\n"
+        "    \"analysis_type\": \"deforestation\"\n"
+        "  }'\n"
+        "\n"
+        "# Test file-upload endpoint\n"
+        "curl -X POST http://localhost:8000/predict/upload \\\n"
+        "  -F \"file=@data/test/sample_tile.tif\" \\\n"
+        "  -F \"analysis_type=flooding\"\n"
+        "\n"
+        "# Health check\n"
+        "curl http://localhost:8000/health"
+    )
+
+    pdf.subsection_title("Step 4: Run Data Quality Checks")
+    pdf.code_block(
+        "# Validate all run records in the database meet schema requirements\n"
+        "python - <<'EOF'\n"
+        "from climatevision.db import get_db_connection, validate_run_schema\n"
+        "conn = get_db_connection()\n"
+        "issues = validate_run_schema(conn)\n"
+        "if issues:\n"
+        "    print(f'Data quality issues found: {len(issues)}')\n"
+        "    for issue in issues:\n"
+        "        print(f'  - {issue}')\n"
+        "else:\n"
+        "    print('All records pass quality checks')\n"
+        "EOF"
+    )
+
+    pdf.subsection_title("Step 5: Register an NGO Organisation")
+    pdf.code_block(
+        "# Create an NGO organisation via the API\n"
+        "curl -X POST http://localhost:8000/organizations \\\n"
+        "  -H \"Content-Type: application/json\" \\\n"
+        "  -d '{\n"
+        "    \"name\": \"Amazon Conservation Trust\",\n"
+        "    \"email\": \"alerts@amazonconservation.org\",\n"
+        "    \"region\": \"amazon\"\n"
+        "  }'\n"
+        "\n"
+        "# Add a regional monitoring subscription\n"
+        "curl -X POST http://localhost:8000/organizations/1/subscriptions \\\n"
+        "  -H \"Content-Type: application/json\" \\\n"
+        "  -d '{\"bbox\": [-60,-15,-45,5], \"analysis_type\": \"deforestation\", \"alert_threshold\": 0.15}'"
+    )
+
+    pdf.subsection_title("Step 6: Commit & Push Your Work")
+    pdf.code_block(
+        "# Switch to your git identity\n"
+        "source team_docs/switch_user.sh olufemi\n"
+        "\n"
+        "git checkout develop && git pull origin develop\n"
+        "git checkout -b feature/api-input-validation\n"
+        "\n"
+        "git add src/climatevision/api/main.py\n"
+        "git add src/climatevision/db.py\n"
+        "git commit -m \"feat(api): add Pydantic input validation and audit logging to predict endpoints\"\n"
+        "\n"
+        "# Push from YOUR GitHub account (femi23)\n"
+        "git push olufemi feature/api-input-validation"
+    )
+
+    pdf.output(os.path.join(OUTPUT_DIR, "Olufemi_Taiwo_Role.pdf"))
+    print("Created: Olufemi_Taiwo_Role.pdf")
+
+
+def create_edoh_doc():
+    pdf = RoleDoc("Edoh-Onuh")
+    pdf.add_page()
+
+    pdf.set_font("Helvetica", "B", 18)
+    pdf.cell(0, 10, "Edoh-Onuh (John Edoh Onuh)", align="C", new_x="LMARGIN", new_y="NEXT")
+    pdf.set_font("Helvetica", "", 11)
+    pdf.set_text_color(100, 100, 100)
+    pdf.cell(0, 7, "Data Science Engineer 1 - ML Model Development Lead", align="C", new_x="LMARGIN", new_y="NEXT")
+    pdf.set_text_color(0, 0, 0)
+    pdf.ln(5)
+
+    pdf.key_value("GitHub", "@edoh-Onuh")
+    pdf.key_value("Access Level", "Maintainer")
+    pdf.key_value("Reports To", "@Goldokpa (Project Owner)")
+    pdf.key_value("Project Duration", "3 Months")
+    pdf.ln(3)
+
+    # How It Fits Me
+    pdf.section_title("How This Role Fits You")
+    pdf.body_text(
+        "Your GitHub portfolio makes the case better than any job description could. You built JED Climate - "
+        "a full-stack climate intelligence platform - independently. It has a FastAPI analytics engine serving "
+        "a carbon calculator and climate predictor, PyTorch/TensorFlow ML services, real-time Recharts "
+        "dashboards for CO2 levels, Arctic ice extent, and sea level rise, and a 14-service Docker Compose "
+        "local stack. That is almost exactly what ClimateVision is. You already know this problem space."
+    )
+    pdf.body_text(
+        "Your fintech-fraud-detection repo demonstrates the depth of ML engineering this role needs: "
+        "XGBoost, Random Forest, and Neural Network ensembles with sub-100ms inference latency, SHAP/LIME "
+        "explainability, concept drift detection, and a production-grade FastAPI serving layer. The same "
+        "engineering discipline - fast, explainable, reliable model inference - is exactly what ClimateVision's "
+        "deforestation detection pipeline requires."
+    )
+    pdf.body_text(
+        "Your classification track record is consistent and strong: diabetes risk prediction (Scikit-learn), "
+        "fraud detection (XGBoost + Neural Networks), text classification (NLP), and time series forecasting "
+        "(Tesla stock). Every one of those is a direct analogue to forest vs. non-forest pixel segmentation - "
+        "the core problem you will be solving here with U-Net and Siamese architectures."
+    )
+    pdf.body_text(
+        "Your sustainable energy analysis and JED Climate's environmental dashboards show you genuinely "
+        "understand the climate data domain - spectral trends, temporal signals, and what makes environmental "
+        "metrics meaningful. That context matters when you are tuning a model to detect 5% forest loss "
+        "in Sentinel-2 imagery at 10-metre resolution."
+    )
+    pdf.ln(2)
+
+    # Role Description
+    pdf.section_title("Your Role on ClimateVision")
+    pdf.body_text(
+        "You own all deep learning model architectures, the training pipeline, and model evaluation. "
+        "Your goal is to train models that achieve high accuracy on forest segmentation and change "
+        "detection, then package them cleanly for the inference pipeline. Carbon regression modelling "
+        "sits with the Analytics Lead - your focus is purely classification and change detection."
+    )
+    pdf.subsection_title("Core Responsibilities")
+    pdf.bullet("Improve and extend the U-Net architecture (Attention U-Net, residual connections, multi-scale features)")
+    pdf.bullet("Train and evaluate the Siamese network for temporal bi-date change detection")
+    pdf.bullet("Build a complete training pipeline: data loading, training loop, validation, checkpointing")
+    pdf.bullet("Implement loss functions tuned for satellite imagery class imbalance (Focal Loss, Dice Loss)")
+    pdf.bullet("Run hyperparameter optimisation using Optuna (learning rate, batch size, architecture depth)")
+    pdf.bullet("Implement transfer learning from pretrained encoders (ResNet, EfficientNet backbones)")
+    pdf.bullet("Build model evaluation framework: F1, IoU, precision-recall curves, confusion matrices")
+    pdf.bullet("Export optimised models to ONNX for production inference speed")
+    pdf.bullet("Implement experiment tracking with MLflow - log runs, metrics, and artefacts")
+    pdf.ln(2)
+
+    # Codebase Ownership
+    pdf.section_title("Your Codebase Ownership")
+    pdf.body_text("You are the primary owner of the following files and directories:")
+    pdf.code_block(
+        "src/climatevision/models/              # PRIMARY OWNER\n"
+        "  unet.py                              # U-Net & Attention U-Net\n"
+        "  siamese.py                           # Siamese change detection network\n"
+        "  __init__.py\n"
+        "  # Note: regression.py is owned by @franchaise (Analytics Lead)\n"
+        "\n"
+        "src/climatevision/training/            # PRIMARY OWNER - New module\n"
+        "  trainer.py                           # Training loop & checkpointing\n"
+        "  evaluator.py                         # Model evaluation framework\n"
+        "  scheduler.py                         # Learning rate schedulers\n"
+        "  callbacks.py                         # Early stopping, logging\n"
+        "  __init__.py\n"
+        "\n"
+        "src/climatevision/utils/\n"
+        "  metrics.py                           # CO-OWNER - Loss functions, metrics\n"
+        "\n"
+        "scripts/\n"
+        "  run_training.py                      # Training pipeline script\n"
+        "  train.py                             # Existing training script\n"
+        "  hyperparameter_search.py             # Optuna hyperparameter search\n"
+        "\n"
+        "models/                                # Trained model weights\n"
+        "models_pretrained/                     # Pretrained backbone weights"
+    )
+    pdf.ln(2)
+
+    # 3-Month Timeline
+    pdf.section_title("Your 3-Month Delivery Timeline")
+    pdf.month_block("MONTH 1: Foundation (Weeks 1-4)", [
+        ("Week 1-2: Training Infrastructure", [
+            "Build trainer.py - complete training loop with mixed-precision, gradient accumulation",
+            "Implement checkpointing (save best model, resume from checkpoint)",
+            "Create evaluator.py - F1, IoU, precision, recall, confusion matrix",
+            "Set up experiment tracking with MLflow - log all runs, hyperparameters, artefacts",
+        ]),
+        ("Week 3-4: Baseline Models", [
+            "Train baseline U-Net on curated forest segmentation dataset",
+            "Implement Focal Loss and Dice Loss for forest/non-forest class imbalance",
+            "Run initial benchmarks: accuracy on Amazon, Congo, Southeast Asia test sets",
+            "Document baseline results as the performance floor to beat",
+        ]),
+    ])
+    pdf.month_block("MONTH 2: Advanced Models (Weeks 5-8)", [
+        ("Week 5-6: Architecture Improvements", [
+            "Implement Attention U-Net with skip connection attention gates",
+            "Add ResNet/EfficientNet encoder backbone via transfer learning (ImageNet pretrained)",
+            "Run hyperparameter search with Optuna (learning rate, batch size, depth, dropout)",
+            "Train Siamese network for bi-temporal change detection",
+        ]),
+        ("Week 7-8: Model Optimisation", [
+            "Implement model ensemble (U-Net + Attention U-Net prediction averaging)",
+            "Build Monte Carlo Dropout for per-pixel uncertainty estimation",
+            "Spatial cross-validation to prevent data leakage across adjacent image tiles",
+            "Performance benchmarking across all model variants - pick production candidate",
+        ]),
+    ])
+    pdf.month_block("MONTH 3: Production Models (Weeks 9-12)", [
+        ("Week 9-10: Export & Versioning", [
+            "Export best-performing models to ONNX format for fast production inference",
+            "Implement model quantisation and pruning for latency reduction",
+            "Set up model registry with versioning, metadata, and performance records",
+            "Create model cards: accuracy, known limitations, training data, bias notes",
+        ]),
+        ("Week 11-12: Final Evaluation", [
+            "Comprehensive evaluation on held-out test sets across all regions",
+            "Ablation studies: measure contribution of each architectural choice",
+            "Write model documentation and training reproduction guide",
+            "Integration testing with Olufemi's inference pipeline - validate end-to-end",
+        ]),
+    ])
+
+    # Git Workflow
+    pdf.section_title("Your Git Workflow")
+    pdf.code_block(
+        "# Create feature branches from develop\n"
+        "git checkout develop\n"
+        "git pull origin develop\n"
+        "git checkout -b feature/model-attention-unet\n"
+        "\n"
+        "# Your branch naming convention:\n"
+        "feature/model-*         (new model architectures)\n"
+        "feature/training-*      (training pipeline features)\n"
+        "fix/model-*             (bug fixes)\n"
+        "experiment/model-*      (experimental architectures)"
+    )
+    pdf.body_text(
+        "All PRs go to the develop branch. Tag @Oshgig when your models require different data formats, "
+        "@franchaise when evaluation metrics or output confidence formats change, and Olufemi Taiwo "
+        "when touching model export formats or inference input shapes."
+    )
+    pdf.ln(3)
+
+    # Key Collaborators
+    pdf.section_title("Your Key Collaborators")
+    pdf.bullet("@Oshgig (Data Pipeline Lead) - She builds the DataLoaders you train on. Coordinate on tensor shapes, normalization values, band order, and augmentation strategies.")
+    pdf.bullet("@franchaise (Analytics Lead) - He owns carbon regression modelling and validates your classification outputs against ground truth. Share model confidence scores and prediction probability formats.")
+    pdf.bullet("Olufemi Taiwo (API & Data Quality Lead) - He loads your trained models into the inference pipeline. Coordinate on model file format (.pth vs ONNX), expected input shapes, and output schema.")
+    pdf.bullet("@cutewizzy11 (Full-Stack & CI/CD) - CI/CD pipeline runs your training scripts. Keep scripts deterministic, well-documented, and reproducible.")
+
+    # Code Pipeline
+    pdf.section_title("Your Code Pipeline")
+    pdf.body_text("Your pipeline covers model architecture development, training, evaluation, and exporting production-ready checkpoints.")
+
+    pdf.subsection_title("Step 1: Environment Setup")
+    pdf.code_block(
+        "git clone https://github.com/Climate-Vision/ClimateVision.git\n"
+        "cd ClimateVision\n"
+        "pip install -r requirements.txt\n"
+        "\n"
+        "# Verify PyTorch and GPU availability\n"
+        "python -c \"import torch; print(f'PyTorch {torch.__version__} | CUDA: {torch.cuda.is_available()}')\""
+    )
+
+    pdf.subsection_title("Step 2: Verify Data Is Ready")
+    pdf.code_block(
+        "# Confirm @Oshgig's DataLoader feeds correctly into your model\n"
+        "python - <<'EOF'\n"
+        "from climatevision.data.dataset import SatelliteDataset\n"
+        "from torch.utils.data import DataLoader\n"
+        "ds = SatelliteDataset('data/processed/', split='train')\n"
+        "loader = DataLoader(ds, batch_size=4, num_workers=2)\n"
+        "imgs, masks = next(iter(loader))\n"
+        "print(f'Batch shape: {imgs.shape} | Mask shape: {masks.shape}')\n"
+        "# Expected: torch.Size([4, 13, 256, 256]) | torch.Size([4, 256, 256])\n"
+        "EOF"
+    )
+
+    pdf.subsection_title("Step 3: Train Baseline U-Net")
+    pdf.code_block(
+        "# Train baseline segmentation model\n"
+        "python scripts/train.py \\\n"
+        "  --model unet \\\n"
+        "  --analysis-type deforestation \\\n"
+        "  --epochs 50 \\\n"
+        "  --batch-size 16 \\\n"
+        "  --lr 1e-4 \\\n"
+        "  --checkpoint-dir models/ \\\n"
+        "  --mlflow-tracking\n"
+        "\n"
+        "# Monitor training: open http://localhost:5000 (MLflow UI)\n"
+        "mlflow ui --port 5000"
+    )
+
+    pdf.subsection_title("Step 4: Hyperparameter Search")
+    pdf.code_block(
+        "# Run Optuna search over learning rate, batch size, depth\n"
+        "python scripts/hyperparameter_search.py \\\n"
+        "  --model unet \\\n"
+        "  --n-trials 50 \\\n"
+        "  --study-name unet_deforestation_v1 \\\n"
+        "  --metric val_iou\n"
+        "\n"
+        "# Best trial is automatically saved to models/best_hparam_unet.pth"
+    )
+
+    pdf.subsection_title("Step 5: Evaluate & Export Model")
+    pdf.code_block(
+        "# Full evaluation on held-out test set\n"
+        "python scripts/evaluate.py \\\n"
+        "  --checkpoint models/best_unet.pth \\\n"
+        "  --split test \\\n"
+        "  --analysis-type deforestation\n"
+        "\n"
+        "# Export to ONNX for fast production inference\n"
+        "python scripts/export_model.py \\\n"
+        "  --checkpoint models/best_unet.pth \\\n"
+        "  --format onnx \\\n"
+        "  --output models/unet_deforestation_v1.onnx"
+    )
+
+    pdf.subsection_title("Step 6: Commit & Push Your Work")
+    pdf.code_block(
+        "# Switch to your git identity\n"
+        "source team_docs/switch_user.sh edoh\n"
+        "\n"
+        "git checkout develop && git pull origin develop\n"
+        "git checkout -b feature/model-attention-unet\n"
+        "\n"
+        "git add src/climatevision/models/unet.py\n"
+        "git add src/climatevision/training/\n"
+        "git commit -m \"feat(model): add attention gates to U-Net encoder skip connections\"\n"
+        "\n"
+        "git push edoh feature/model-attention-unet"
+    )
+
+    pdf.output(os.path.join(OUTPUT_DIR, "Edoh_Onuh_Role.pdf"))
+    print("Created: Edoh_Onuh_Role.pdf")
+
+
+def create_victor_doc():
+    pdf = RoleDoc("Victor Mbachu")
+    pdf.add_page()
+
+    pdf.set_font("Helvetica", "B", 18)
+    pdf.cell(0, 10, "Victor Mbachu", align="C", new_x="LMARGIN", new_y="NEXT")
+    pdf.set_font("Helvetica", "", 11)
+    pdf.set_text_color(100, 100, 100)
+    pdf.cell(0, 7, "Senior Full-Stack Engineer & Infrastructure Co-Owner", align="C", new_x="LMARGIN", new_y="NEXT")
+    pdf.set_text_color(0, 0, 0)
+    pdf.ln(5)
+
+    pdf.key_value("GitHub", "@cutewizzy11")
+    pdf.key_value("Access Level", "Co-Owner (Admin)")
+    pdf.key_value("Reports To", "@Goldokpa (Project Owner)")
+    pdf.key_value("Project Duration", "3 Months")
+    pdf.ln(3)
+
+    # How It Fits Me
+    pdf.section_title("How This Role Fits You")
+    pdf.body_text(
+        "At Zeta Global you design and run distributed microservice systems handling over 2 million API "
+        "requests daily with 99.9% uptime across multiple AWS regions - ECS Fargate clusters, RDS Aurora, "
+        "SNS/SQS messaging, and blue-green CI/CD deployments provisioned via Terraform. You also serve as "
+        "on-call engineer with a 15-minute average incident resolution time. That is the production "
+        "engineering standard ClimateVision needs to reach, and you have already built it professionally."
+    )
+    pdf.body_text(
+        "At RWS Global you containerised applications with Docker, deployed across dev, staging, and "
+        "production environments, led a team of 3 engineers in Agile sprints, and maintained GitHub Actions "
+        "CI/CD pipelines with TDD coverage. The Docker and deployment ownership on this project - "
+        "previously unassigned - is a natural fit: you do this as part of your day job, not as a "
+        "stretch task."
+    )
+    pdf.body_text(
+        "Your stack breadth is the reason you can serve as repository co-owner rather than just a "
+        "frontend contributor. React, Next.js, Vue, TypeScript, Node.js, PHP/Laravel, Python/Django - "
+        "you can read and reason about the FastAPI backend, the PyTorch inference pipeline, and the "
+        "React dashboard with equal confidence. Reviewing PRs across four data scientists requires "
+        "that range. Your AWS Certified Cloud Practitioner and Professional Scrum Master certifications "
+        "anchor both the infrastructure ownership and the project coordination function."
+    )
+    pdf.body_text(
+        "Your AI integration experience - GPT-4 and Anthropic API work at RWS Global and PetMe - "
+        "means you understand the ML serving layer you are wrapping with a frontend. When @edoh-Onuh "
+        "exports a model and Olufemi builds the inference API, you are not reading foreign code. You "
+        "have shipped production AI features before. Your two co-authored papers on agentic AI systems "
+        "show that engagement runs deeper than implementation."
+    )
+    pdf.ln(2)
+
+    # Role Description
+    pdf.section_title("Your Role on ClimateVision")
+    pdf.body_text(
+        "You own the frontend application, the CI/CD infrastructure, and the Docker/deployment layer. "
+        "As co-owner you are also the quality gate for all code entering the repository - the one "
+        "person on the team who can review and reason about every layer of the stack."
+    )
+    pdf.subsection_title("Core Responsibilities - Frontend")
+    pdf.bullet("Build the React/TypeScript dashboard with interactive Leaflet map for satellite analysis results")
+    pdf.bullet("Create Recharts components for deforestation trends, carbon metrics, and model performance")
+    pdf.bullet("Implement api.ts - the fully-typed API client for all FastAPI backend communication")
+    pdf.bullet("Build the alert notification panel for real-time deforestation alerts")
+    pdf.bullet("Implement responsive TailwindCSS design for desktop and tablet viewports")
+    pdf.bullet("Create the deep-dive analysis page with region selector, date range picker, and model comparison")
+    pdf.ln(1)
+
+    pdf.subsection_title("Core Responsibilities - Infrastructure & CI/CD")
+    pdf.bullet("Own the Dockerfile - multi-stage production build for the FastAPI + frontend application")
+    pdf.bullet("Own docker-compose.yml - local development stack wiring API, database, and frontend services")
+    pdf.bullet("Build and maintain GitHub Actions CI/CD pipelines: lint, type-check, test, and deploy on every PR")
+    pdf.bullet("Manage production environment configuration - dev/staging/prod separation and secrets management")
+    pdf.bullet("Serve as first responder for production incidents - triage, diagnose, and coordinate resolution")
+    pdf.ln(1)
+
+    pdf.subsection_title("Sprint Progress - April 2026")
+    pdf.bullet("DONE: GitHub Actions CI pipeline (Python flake8 + pytest, frontend npm build)")
+    pdf.bullet("DONE: Test scaffolding (tests/ directory with pytest fixtures)")
+    pdf.bullet("DONE: Frontend build fixes (case-sensitive import paths)")
+    pdf.bullet("DONE: Dependency fixes (removed gdal pip package, added email-validator)")
+    pdf.bullet("PENDING: Frontend unit tests with Vitest + React Testing Library")
+    pdf.bullet("PENDING: Auth UI - capture X-API-Key in AppContext")
+    pdf.bullet("PENDING: WebSocket client for real-time run status")
+    pdf.bullet("PENDING: Alert notification UI with severity filters")
+    pdf.bullet("PENDING: Mask overlay on map component")
+    pdf.bullet("PENDING: Docker Compose for full-stack local dev")
+    pdf.ln(1)
+
+    pdf.subsection_title("Core Responsibilities - Co-Owner")
+    pdf.bullet("Review and merge pull requests from all team members (target: <24 hour turnaround)")
+    pdf.bullet("Manage GitHub issues, milestones, project boards, and sprint planning")
+    pdf.bullet("Enforce branch protection rules, code quality standards, and API contract consistency")
+    pdf.bullet("Manage the release process: version tagging, changelog, and release notes")
+    pdf.ln(2)
+
+    # Codebase Ownership
+    pdf.section_title("Your Codebase Ownership")
+    pdf.body_text("You are the primary owner of the following files and directories:")
+    pdf.code_block(
+        "frontend/                              # PRIMARY OWNER - Entire frontend\n"
+        "  src/\n"
+        "    App.tsx                             # Main application shell\n"
+        "    api.ts                              # Typed API client\n"
+        "    main.tsx                            # Entry point\n"
+        "    styles.css                          # TailwindCSS styles\n"
+        "    components/                         # Component library\n"
+        "      Map.tsx                           # Leaflet map\n"
+        "      ResultsViewer.tsx                 # Prediction results\n"
+        "      Charts.tsx                        # Recharts visualizations\n"
+        "      AlertPanel.tsx                    # Alert notifications\n"
+        "      Settings.tsx                      # User settings\n"
+        "    pages/\n"
+        "      Dashboard.tsx                     # Main dashboard\n"
+        "      Analysis.tsx                      # Deep analysis view\n"
+        "      History.tsx                       # Run history\n"
+        "  package.json | vite.config.ts | tsconfig.json\n"
+        "\n"
+        "Dockerfile                             # PRIMARY OWNER - Multi-stage production build\n"
+        "docker-compose.yml                     # PRIMARY OWNER - Local development stack\n"
+        "\n"
+        ".github/workflows/                     # PRIMARY OWNER\n"
+        "  ci.yml                               # Continuous integration\n"
+        "  deploy.yml                            # Deployment pipeline\n"
+        "  tests.yml                            # Test automation\n"
+        "\n"
+        "tests/                                 # CO-OWNER (with all DS engineers)"
+    )
+    pdf.ln(2)
+
+    # 3-Month Timeline
+    pdf.section_title("Your 3-Month Delivery Timeline")
+    pdf.month_block("MONTH 1: Foundation (Weeks 1-4)", [
+        ("Week 1-2: Infrastructure & CI/CD", [
+            "Write multi-stage Dockerfile for optimised API + frontend production image",
+            "Build docker-compose.yml wiring FastAPI, SQLite/PostgreSQL, and frontend services locally",
+            "Set up GitHub Actions CI: lint, type-check, pytest, and Vite build on every PR",
+            "Create branch protection rules: require passing CI and 1 review before merging to develop",
+        ]),
+        ("Week 3-4: Frontend Architecture & Core Components", [
+            "Configure React Router, Vite, TypeScript strict mode, TailwindCSS, ESLint, and Prettier",
+            "Build Map.tsx - Leaflet map with GeoJSON overlay for deforestation masks",
+            "Implement api.ts - fully-typed API client for all FastAPI endpoints",
+            "Create Dashboard.tsx - main landing page with summary metrics and run status",
+        ]),
+    ])
+    pdf.month_block("MONTH 2: Feature Development (Weeks 5-8)", [
+        ("Week 5-6: Data Visualisation", [
+            "Build Charts.tsx - Recharts components for deforestation trend lines, bar charts, gauges",
+            "Create ResultsViewer.tsx - segmentation masks overlaid on satellite imagery",
+            "Implement Analysis.tsx - region selector, date picker, model comparison view",
+            "Set up Vitest and React Testing Library - component test coverage from the start",
+        ]),
+        ("Week 7-8: Real-Time & Interactivity", [
+            "Build WebSocket integration for live prediction job status updates",
+            "Create AlertPanel.tsx - real-time deforestation alert notification feed",
+            "Implement History.tsx - paginated, filterable list of past analysis runs",
+            "Build Settings.tsx - user preferences and API key management",
+        ]),
+    ])
+    pdf.month_block("MONTH 3: Production Readiness (Weeks 9-12)", [
+        ("Week 9-10: Deployment & Environment Config", [
+            "Configure dev/staging/prod environment separation with secrets management",
+            "Set up deployment pipeline to Vercel (frontend) and Docker-based backend hosting",
+            "Implement health monitoring and automated alerting for production incidents",
+            "Performance pass: code splitting, lazy loading, image optimisation, bundle analysis",
+        ]),
+        ("Week 11-12: Integration, Testing & Release", [
+            "Full end-to-end integration testing against all backend API endpoints",
+            "Responsive design audit for tablet and large desktop breakpoints",
+            "Accessibility review: keyboard navigation and screen reader compatibility",
+            "Manage v1.0 release: changelog, version tag, release notes, and deployment sign-off",
+        ]),
+    ])
+
+    # Git Workflow
+    pdf.section_title("Your Git Workflow")
+    pdf.code_block(
+        "# Create feature branches from develop\n"
+        "git checkout develop\n"
+        "git pull origin develop\n"
+        "git checkout -b feature/frontend-leaflet-map\n"
+        "\n"
+        "# Your branch naming convention:\n"
+        "feature/frontend-*     (frontend features)\n"
+        "feature/infra-*        (Docker, CI/CD, deployment)\n"
+        "feature/ci-*           (GitHub Actions changes)\n"
+        "fix/frontend-*         (bug fixes)\n"
+        "release/v*             (release branches)"
+    )
+    pdf.body_text(
+        "As co-owner, you can merge directly to develop after self-review for frontend-only or infra-only "
+        "changes. For changes touching shared Python code or API contracts, get a review from @Goldokpa "
+        "or the relevant module owner."
+    )
+    pdf.ln(3)
+
+    # Key Collaborators
+    pdf.section_title("Your Key Collaborators")
+    pdf.bullet("Olufemi Taiwo (API & Data Quality Lead) - He owns the FastAPI schemas, inference validation, and audit logging. You own the Docker image and deployment pipeline that runs his API. Define the API contract together: endpoint URLs, request/response shapes, auth headers, and error formats.")
+    pdf.bullet("@franchaise (Analytics Lead) - His carbon metrics and KPI data feed your dashboard charts. Align on JSON data contracts, refresh intervals, and pagination formats.")
+    pdf.bullet("@edoh-Onuh (ML Lead) - Model prediction outputs need to be visualised on the map. Coordinate on GeoJSON output format, confidence score rendering, and how prediction jobs report status via the API.")
+    pdf.bullet("@Oshgig (Data Pipeline Lead) - Satellite imagery tile previews on the map may draw on her geospatial utilities. Align on tile formats, coordinate systems, and GeoJSON structures.")
+
+    # Code Pipeline
+    pdf.section_title("Your Code Pipeline")
+    pdf.body_text("Your pipeline covers frontend development, Docker orchestration, CI/CD management, and full-stack integration testing.")
+
+    pdf.subsection_title("Step 1: Environment Setup")
+    pdf.code_block(
+        "git clone https://github.com/Climate-Vision/ClimateVision.git\n"
+        "cd ClimateVision\n"
+        "\n"
+        "# Backend dependencies\n"
+        "pip install -r requirements.txt\n"
+        "\n"
+        "# Frontend dependencies\n"
+        "cd frontend && npm install && cd .."
+    )
+
+    pdf.subsection_title("Step 2: Start Full Local Dev Stack")
+    pdf.code_block(
+        "# Option A: Docker Compose (full stack - recommended)\n"
+        "docker-compose up --build\n"
+        "# API:      http://localhost:8000\n"
+        "# Frontend: http://localhost:5173\n"
+        "# MLflow:   http://localhost:5000\n"
+        "\n"
+        "# Option B: Run services individually for faster iteration\n"
+        "uvicorn climatevision.api.main:app --reload --port 8000 &\n"
+        "cd frontend && npm run dev"
+    )
+
+    pdf.subsection_title("Step 3: Frontend Development Loop")
+    pdf.code_block(
+        "cd frontend\n"
+        "\n"
+        "# Run linting and type checks\n"
+        "npm run lint\n"
+        "npm run type-check\n"
+        "\n"
+        "# Run component tests\n"
+        "npm run test\n"
+        "\n"
+        "# Build production bundle and check for errors\n"
+        "npm run build\n"
+        "\n"
+        "# Preview production build locally\n"
+        "npm run preview"
+    )
+
+    pdf.subsection_title("Step 4: Current CI/CD Configuration")
+    pdf.body_text("The following .github/workflows/ci.yml is live and runs on every PR to main/develop:")
+    pdf.code_block(
+        "name: CI\n"
+        "on:\n"
+        "  push:\n"
+        "    branches: [main, develop]\n"
+        "  pull_request:\n"
+        "    branches: [main, develop]\n"
+        "\n"
+        "jobs:\n"
+        "  python:\n"
+        "    runs-on: ubuntu-latest\n"
+        "    steps:\n"
+        "      - uses: actions/checkout@v4\n"
+        "      - uses: actions/setup-python@v5\n"
+        "        with: {python-version: '3.11'}\n"
+        "      - run: sudo apt-get update && sudo apt-get install -y libgl1\n"
+        "      - run: pip install -r requirements.txt && pip install -e .\n"
+        "      - run: flake8 src/ --select=E9,F63,F7,F82\n"
+        "      - run: pytest tests/ -v --tb=short\n"
+        "\n"
+        "  frontend:\n"
+        "    runs-on: ubuntu-latest\n"
+        "    defaults: {run: {working-directory: frontend}}\n"
+        "    steps:\n"
+        "      - uses: actions/checkout@v4\n"
+        "      - uses: actions/setup-node@v4\n"
+        "        with: {node-version: '20', cache: 'npm'}\n"
+        "      - run: npm ci\n"
+        "      - run: npm run build"
+    )
+    pdf.ln(2)
+
+    pdf.subsection_title("Step 5: Build & Test Docker Image")
+    pdf.code_block(
+        "# Build production Docker image\n"
+        "docker build -t climatevision:latest .\n"
+        "\n"
+        "# Run container and verify it starts cleanly\n"
+        "docker run -p 8000:8000 climatevision:latest\n"
+        "\n"
+        "# Check all services are healthy inside the container\n"
+        "curl http://localhost:8000/health\n"
+        "\n"
+        "# Inspect image size and layers\n"
+        "docker image inspect climatevision:latest | grep Size"
+    )
+
+    pdf.subsection_title("Step 6: Run Full CI Checks Locally")
+    pdf.code_block(
+        "# Simulate the GitHub Actions CI pipeline before pushing\n"
+        "\n"
+        "# 1. Python: lint and tests\n"
+        "flake8 src/ --count --select=E9,F63,F7,F82 --show-source --statistics\n"
+        "flake8 src/ --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics\n"
+        "pytest tests/ -v --tb=short\n"
+        "\n"
+        "# 2. Frontend: build\n"
+        "cd frontend && npm run build\n"
+        "\n"
+        "# 3. Docker build succeeds\n"
+        "docker-compose build"
+    )
+
+    pdf.subsection_title("Step 6: Commit & Push Your Work")
+    pdf.code_block(
+        "# Switch to your git identity\n"
+        "source team_docs/switch_user.sh victor\n"
+        "\n"
+        "git checkout develop && git pull origin develop\n"
+        "git checkout -b feature/frontend-leaflet-map\n"
+        "\n"
+        "git add frontend/src/components/Map.tsx\n"
+        "git add frontend/src/api.ts\n"
+        "git commit -m \"feat(frontend): add Leaflet map with GeoJSON deforestation overlay\"\n"
+        "\n"
+        "git push victor feature/frontend-leaflet-map\n"
+        "\n"
+        "# As co-owner: review and merge PRs from the team\n"
+        "# gh pr review <PR_NUMBER> --approve\n"
+        "# gh pr merge <PR_NUMBER> --squash"
+    )
+
+    pdf.output(os.path.join(OUTPUT_DIR, "Victor_Mbachu_Role.pdf"))
+    print("Created: Victor_Mbachu_Role.pdf")
+
+
+def create_godswill_doc():
+    pdf = RoleDoc("Godswill Okoroafor Chukwu")
+    pdf.add_page()
+
+    pdf.set_font("Helvetica", "B", 18)
+    pdf.cell(0, 10, "Godswill Okoroafor Chukwu", align="C", new_x="LMARGIN", new_y="NEXT")
+    pdf.set_font("Helvetica", "", 11)
+    pdf.set_text_color(100, 100, 100)
+    pdf.cell(0, 7, "Data Science Engineer 5 - ML Training, Experiment Tracking & Insights Lead", align="C", new_x="LMARGIN", new_y="NEXT")
+    pdf.set_text_color(0, 0, 0)
+    pdf.ln(5)
+
+    pdf.key_value("GitHub", "(To be assigned)")
+    pdf.key_value("Access Level", "Maintainer")
+    pdf.key_value("Reports To", "@Goldokpa (Project Owner)")
+    pdf.key_value("Project Duration", "3 Months")
+    pdf.ln(3)
+
+    # How It Fits Me
+    pdf.section_title("How This Role Fits You")
+    pdf.body_text(
+        "Your MSc in Big Data and Data Science Technology (Distinction) from Northumbria University is the "
+        "academic backbone this role demands. You have not just studied machine learning - you have delivered "
+        "it in production environments. At Amdari Inc., you built predictive and forecasting models that drove "
+        "strategic revenue decisions, applied clustering to identify at-risk student groups, and automated "
+        "reporting pipelines that cut manual processing time significantly. Every one of those deliverables "
+        "maps directly onto what ClimateVision needs from its ML training and insights layer."
+    )
+    pdf.body_text(
+        "Where @edoh-Onuh architects the deep learning models (U-Net, Siamese networks), you are the engineer "
+        "who drives those models through rigorous training cycles, tracks every experiment, measures every "
+        "metric, and extracts insights from the results. Your experience running classification, regression, "
+        "and clustering pipelines in Python - combined with your Data Scientist role at Amdari - means you "
+        "understand the full lifecycle: data in, model trained, results validated, insights delivered."
+    )
+    pdf.body_text(
+        "Your proficiency in Power BI and Looker Studio is a strategic asset here. ClimateVision generates "
+        "real predictions - deforestation percentages, ice extent loss, flood area - that conservation NGOs "
+        "and research partners need presented clearly. You build the reporting layer that translates raw model "
+        "outputs into KPI dashboards, trend reports, and alert summaries that non-technical stakeholders "
+        "can act on. That is the last mile between a working model and measurable real-world impact."
+    )
+    pdf.body_text(
+        "Your background in automating recurring reporting processes with Python and designing cross-functional "
+        "dashboards means you also own the bridge between the ML pipeline and the business intelligence layer. "
+        "With your DataCamp Associate Data Scientist certification and Full Stack Data Science qualification "
+        "from 10Alytics, you bring both the theoretical depth and the applied toolkit that this role requires."
+    )
+    pdf.ln(2)
+
+    # Role Description
+    pdf.section_title("Your Role on ClimateVision")
+    pdf.body_text(
+        "You own the training analytics, experiment tracking, and insights reporting pipeline - the layer that "
+        "connects raw model outputs to actionable environmental intelligence. While the ML Lead builds model "
+        "architectures and the Data Pipeline Lead ingests satellite imagery, you are the engineer who runs "
+        "training experiments at scale, tracks what works and why, measures model impact, and delivers "
+        "structured insights to teams and stakeholders. You are the system's analytical conscience."
+    )
+    pdf.subsection_title("Core Responsibilities")
+    pdf.bullet("Orchestrate model training runs using scripts/train.py and scripts/run_training.py with full experiment tracking via MLflow")
+    pdf.bullet("Design and execute hyperparameter tuning experiments using Optuna to maximise IoU, F1, and Dice scores")
+    pdf.bullet("Build and maintain the model evaluation pipeline - benchmarking across deforestation, ice melting, and flooding tasks")
+    pdf.bullet("Implement clustering analysis on prediction outputs to identify regional environmental patterns and hotspots")
+    pdf.bullet("Develop forecasting models to project deforestation trends, ice melt rates, and flood risk over time")
+    pdf.bullet("Automate KPI reporting pipelines that summarise model performance and environmental metrics for NGO stakeholders")
+    pdf.bullet("Design and maintain Power BI / Looker Studio dashboards tracking training progress, model accuracy, and climate impact")
+    pdf.bullet("Create data quality reports that validate training datasets and flag anomalies before they reach the model")
+    pdf.bullet("Produce regional impact analysis notebooks showing before/after environmental change metrics")
+    pdf.bullet("Feed structured insight data to the API layer and React dashboard for live reporting")
+    pdf.ln(2)
+
+    # Codebase Ownership
+    pdf.section_title("Your Codebase Ownership")
+    pdf.body_text("You are the primary owner of the following files and directories:")
+    pdf.code_block(
+        "scripts/                              # PRIMARY OWNER - Training & evaluation scripts\n"
+        "  train.py                            # Model training entry point\n"
+        "  run_training.py                     # Training orchestration & scheduling\n"
+        "  evaluate.py                         # Model evaluation & benchmarking\n"
+        "  infer.py                            # Single inference runner\n"
+        "\n"
+        "src/climatevision/training/\n"
+        "  trainer.py                          # CO-OWNER - Training loop, EMA, mixed precision\n"
+        "  losses.py                           # CO-OWNER - Focal Loss, Dice Loss tuning\n"
+        "\n"
+        "src/climatevision/utils/\n"
+        "  metrics.py                          # CO-OWNER - IoU, F1, Dice, recall tracking\n"
+        "  visualization.py                    # CO-OWNER - Training curve & result plots\n"
+        "\n"
+        "notebooks/\n"
+        "  06_training_analysis.ipynb          # Experiment tracking & training insights\n"
+        "  07_model_benchmarking.ipynb         # Cross-task model performance comparison\n"
+        "  08_regional_insights.ipynb          # Clustering & trend analysis by region\n"
+        "\n"
+        "outputs/\n"
+        "  reports/training/                   # Training run reports\n"
+        "  dashboards/kpi/                     # KPI dashboard configs\n"
+        "\n"
+        "logs/                                 # Training logs & MLflow run artifacts\n"
+        "models/                               # Model checkpoints (coordinate with ML Lead)"
+    )
+    pdf.ln(2)
+
+    # Key Impact Areas
+    pdf.section_title("Your High-Impact Contributions")
+    pdf.body_text(
+        "Your work directly determines whether ClimateVision's models are as accurate as possible and whether "
+        "their outputs are trusted by the organisations that rely on them. Three areas define your impact:"
+    )
+    pdf.subsection_title("1. Experiment-Driven Model Improvement")
+    pdf.body_text(
+        "Every training run you log is a data point. By systematically tracking learning rate schedules, "
+        "augmentation strategies, loss function weights, and batch sizes via MLflow and Optuna, you will "
+        "build the evidence base that drives model accuracy from baseline to production-grade. Your tuning "
+        "work is the difference between a model that detects 65% of deforestation events and one that "
+        "detects 85%."
+    )
+    pdf.subsection_title("2. Regional Clustering & Trend Forecasting")
+    pdf.body_text(
+        "Your clustering expertise turns raw pixel predictions into geographic intelligence. By grouping "
+        "regions with similar deforestation trajectories or flood risk patterns, you reveal insights that "
+        "no single prediction run can show. Paired with time-series forecasting models, you can project "
+        "where the next environmental crisis is developing before it becomes catastrophic - giving NGO "
+        "partners the lead time they need to act."
+    )
+    pdf.subsection_title("3. Stakeholder-Ready Reporting")
+    pdf.body_text(
+        "Raw model metrics mean nothing to a conservation officer or a policy researcher. Your Power BI "
+        "and automated Python reporting pipelines convert IoU scores and segmentation masks into carbon "
+        "loss estimates, hectare counts, and trend alerts that stakeholders can put in a board report. "
+        "This is the last mile of impact - and you own it."
+    )
+    pdf.ln(2)
+
+    # 3-Month Timeline
+    pdf.section_title("Your 3-Month Delivery Timeline")
+    pdf.month_block("MONTH 1: Foundation (Weeks 1-4)", [
+        ("Week 1-2: Training Infrastructure & Experiment Tracking", [
+            "Set up MLflow tracking server and connect to scripts/train.py",
+            "Instrument trainer.py to log all hyperparameters, metrics, and artifacts per run",
+            "Run baseline training experiments for deforestation, ice melting, and flooding tasks",
+            "Document baseline IoU, F1, and Dice scores per analysis type",
+        ]),
+        ("Week 3-4: Evaluation Pipeline", [
+            "Build scripts/evaluate.py - full evaluation suite with per-class metrics",
+            "Extend metrics.py with precision-recall curves and confusion matrix exports",
+            "Create 07_model_benchmarking.ipynb - cross-task performance comparison",
+            "Identify top 3 weaknesses in baseline models and propose tuning strategies",
+        ]),
+    ])
+    pdf.month_block("MONTH 2: Optimisation & Insights (Weeks 5-8)", [
+        ("Week 5-6: Hyperparameter Tuning", [
+            "Set up Optuna study for learning rate, batch size, loss weights, and augmentation",
+            "Run tuning experiments targeting IoU improvement of at least 10% over baseline",
+            "Log all trials in MLflow with full reproducibility (seed, config, checkpoint)",
+            "Implement best-config automatic checkpoint promotion pipeline",
+        ]),
+        ("Week 7-8: Clustering & Trend Forecasting", [
+            "Build regional clustering pipeline using K-Means / DBSCAN on prediction outputs",
+            "Develop time-series forecasting models for deforestation and ice melt trends",
+            "Create 08_regional_insights.ipynb - hotspot identification and trend projections",
+            "Generate first set of regional environmental trend reports",
+        ]),
+    ])
+    pdf.month_block("MONTH 3: Reporting & Production Readiness (Weeks 9-12)", [
+        ("Week 9-10: KPI Dashboard & Automated Reporting", [
+            "Build automated Python reporting pipeline - weekly model performance summaries",
+            "Design Power BI / Looker Studio KPI dashboard (accuracy trends, alert counts, coverage)",
+            "Expose dashboard data via API endpoints coordinated with Olufemi",
+            "Automate NGO-facing impact reports: area affected, confidence scores, trend direction",
+        ]),
+        ("Week 11-12: Documentation & Final Benchmarks", [
+            "Write 06_training_analysis.ipynb - full experiment history and lessons learned",
+            "Produce final benchmark report comparing all model versions across 3 months",
+            "Document all MLflow experiments, best checkpoints, and recommended configs",
+            "Deliver 3 regional case study insight reports to the team for stakeholder use",
+        ]),
+    ])
+
+    # Git Workflow
+    pdf.section_title("Your Git Workflow")
+    pdf.body_text("Follow this branching convention for all your work:")
+    pdf.code_block(
+        "# Create feature branches from develop\n"
+        "git checkout develop\n"
+        "git pull origin develop\n"
+        "git checkout -b feature/training-mlflow-setup\n"
+        "\n"
+        "# Your branch naming convention:\n"
+        "feature/training-*      (training pipeline features)\n"
+        "feature/insights-*      (reporting and analytics features)\n"
+        "fix/training-*          (bug fixes in training scripts)\n"
+        "experiment/tuning-*     (hyperparameter experiment branches)"
+    )
+    pdf.body_text(
+        "All PRs go to the develop branch. PRs require at least 1 review. "
+        "Tag @edoh-Onuh for model architecture questions and @franchaise for analytics overlap reviews. "
+        "Always attach MLflow run IDs in PRs that change training logic so reviewers can verify metrics."
+    )
+    pdf.ln(3)
+
+    # Key Collaborators
+    pdf.section_title("Your Key Collaborators")
+    pdf.bullet("@edoh-Onuh (ML Model Development Lead) - You run the training experiments on their model architectures. Coordinate on loss function choices, training hyperparameters, and checkpoint formats. Their architecture decisions constrain your tuning search space.")
+    pdf.bullet("@Oshgig (Data Pipeline Lead) - Your training runs consume her PyTorch DataLoaders. Align on tensor shapes, normalization ranges, augmentation strategies, and the data split structure (train/val/test).")
+    pdf.bullet("@franchaise (Carbon Analytics Lead) - Your model evaluation outputs are the input to their carbon estimation and validation work. Provide segmentation mask formats, confidence scores, and per-class metrics in agreed schemas.")
+    pdf.bullet("Olufemi Taiwo (API & Data Quality Lead) - Your KPI reporting data needs to be surfaced via API endpoints. Coordinate on response formats, refresh cycles, and how training run metadata is exposed to the dashboard.")
+    pdf.bullet("Victor Mbachu (Full-Stack & Infrastructure) - Your dashboard configs and reporting outputs feed the React frontend visualisations. Align on JSON contracts for time-series charts, gauge metrics, and alert summaries.")
+
+    # Code Pipeline
+    pdf.section_title("Your Code Pipeline")
+    pdf.body_text("Your pipeline covers experiment tracking setup, running and tuning training jobs, evaluating model performance, and generating insight reports for stakeholders.")
+
+    pdf.subsection_title("Step 1: Environment Setup")
+    pdf.code_block(
+        "git clone https://github.com/Climate-Vision/ClimateVision.git\n"
+        "cd ClimateVision\n"
+        "pip install -r requirements.txt\n"
+        "\n"
+        "# Verify ML and analytics stack\n"
+        "python -c \"import torch, mlflow, optuna, sklearn; print('ML stack ready')\"\n"
+        "\n"
+        "# Start MLflow tracking server\n"
+        "mlflow server --host 0.0.0.0 --port 5000 &\n"
+        "# Dashboard: http://localhost:5000"
+    )
+
+    pdf.subsection_title("Step 2: Run a Training Experiment")
+    pdf.code_block(
+        "# Run a tracked training job\n"
+        "python scripts/run_training.py \\\n"
+        "  --config config/deforestation.yaml \\\n"
+        "  --mlflow-tracking \\\n"
+        "  --experiment-name deforestation_v1\n"
+        "\n"
+        "# All metrics, params, and checkpoints auto-logged to MLflow\n"
+        "# View results: http://localhost:5000/#/experiments"
+    )
+
+    pdf.subsection_title("Step 3: Hyperparameter Tuning with Optuna")
+    pdf.code_block(
+        "# Launch an Optuna study to find the best training config\n"
+        "python - <<'EOF'\n"
+        "import optuna, mlflow\n"
+        "from climatevision.training.trainer import train_with_config\n"
+        "\n"
+        "def objective(trial):\n"
+        "    config = {\n"
+        "        'lr':         trial.suggest_float('lr', 1e-5, 1e-3, log=True),\n"
+        "        'batch_size': trial.suggest_categorical('batch_size', [8, 16, 32]),\n"
+        "        'dropout':    trial.suggest_float('dropout', 0.1, 0.5),\n"
+        "    }\n"
+        "    return train_with_config(config, metric='val_iou')\n"
+        "\n"
+        "study = optuna.create_study(direction='maximize', study_name='unet_deforestation')\n"
+        "study.optimize(objective, n_trials=50)\n"
+        "print(f'Best IoU: {study.best_value:.4f}')\n"
+        "print(f'Best params: {study.best_params}')\n"
+        "EOF"
+    )
+
+    pdf.subsection_title("Step 4: Evaluate & Benchmark Models")
+    pdf.code_block(
+        "# Evaluate best checkpoint across all analysis types\n"
+        "python scripts/evaluate.py \\\n"
+        "  --checkpoint models/best_unet.pth \\\n"
+        "  --split test \\\n"
+        "  --analysis-type deforestation \\\n"
+        "  --export-metrics outputs/reports/training/deforestation_eval.json\n"
+        "\n"
+        "# Compare all model versions logged in MLflow\n"
+        "python - <<'EOF'\n"
+        "import mlflow\n"
+        "runs = mlflow.search_runs(experiment_names=['deforestation_v1'],\n"
+        "                          order_by=['metrics.val_iou DESC'])\n"
+        "print(runs[['run_id','metrics.val_iou','params.lr','params.batch_size']].head(10))\n"
+        "EOF"
+    )
+
+    pdf.subsection_title("Step 5: Generate Stakeholder KPI Report")
+    pdf.code_block(
+        "# Run clustering on prediction outputs to find regional hotspots\n"
+        "python - <<'EOF'\n"
+        "from sklearn.cluster import KMeans\n"
+        "import numpy as np, json\n"
+        "predictions = np.load('outputs/masks/deforestation_confidence.npy')\n"
+        "kmeans = KMeans(n_clusters=5, random_state=42).fit(predictions.reshape(-1, 1))\n"
+        "hotspot_regions = np.where(kmeans.labels_ == kmeans.cluster_centers_.argmax())[0]\n"
+        "print(f'High-risk tiles identified: {len(hotspot_regions)}')\n"
+        "EOF\n"
+        "\n"
+        "# Auto-generate weekly KPI summary report\n"
+        "python - <<'EOF'\n"
+        "from climatevision.analytics.reporting import generate_kpi_report\n"
+        "generate_kpi_report(\n"
+        "    metrics_dir='outputs/reports/training/',\n"
+        "    period='2024-W12',\n"
+        "    output='outputs/dashboards/kpi/weekly_summary.pdf'\n"
+        ")\n"
+        "EOF"
+    )
+
+    pdf.subsection_title("Step 6: Commit & Push Your Work")
+    pdf.code_block(
+        "# Switch to your git identity\n"
+        "source team_docs/switch_user.sh godswill\n"
+        "\n"
+        "git checkout develop && git pull origin develop\n"
+        "git checkout -b feature/training-mlflow-setup\n"
+        "\n"
+        "git add scripts/run_training.py\n"
+        "git add scripts/evaluate.py\n"
+        "git add notebooks/06_training_analysis.ipynb\n"
+        "git commit -m \"feat(training): add MLflow experiment tracking and Optuna hyperparameter search\"\n"
+        "\n"
+        "git push godswill feature/training-mlflow-setup"
+    )
+
+    pdf.output(os.path.join(OUTPUT_DIR, "Godswill_Chukwu_Role.pdf"))
+    print("Created: Godswill_Chukwu_Role.pdf")
+
+
+def create_paul_doc():
+    pdf = RoleDoc("Paul (cutewizzy11)")
+    pdf.add_page()
+
+    pdf.set_font("Helvetica", "B", 18)
+    pdf.cell(0, 10, "Paul", align="C", new_x="LMARGIN", new_y="NEXT")
+    pdf.set_font("Helvetica", "", 11)
+    pdf.set_text_color(100, 100, 100)
+    pdf.cell(0, 7, "Frontend Developer - React Dashboard & UI Lead", align="C", new_x="LMARGIN", new_y="NEXT")
+    pdf.set_text_color(0, 0, 0)
+    pdf.ln(5)
+
+    pdf.key_value("GitHub", "@cutewizzy11")
+    pdf.key_value("Access Level", "Maintainer")
+    pdf.key_value("Reports To", "@Goldokpa (Project Owner)")
+    pdf.key_value("Project Duration", "3 Months")
+    pdf.ln(3)
+
+    # How It Fits
+    pdf.section_title("How This Role Fits You")
+    pdf.body_text(
+        "Your GitHub portfolio shows a developer who is comfortable across the full stack but has a clear "
+        "strength in TypeScript and JavaScript-driven interfaces. nova-agent, Data-management-Koinonia, "
+        "and anyebe-web-craft are all TypeScript projects - the same language ClimateVision's frontend is "
+        "built in. Your react-projects and ecommerce-app repositories show hands-on React experience, and "
+        "your Heart-Attack-Risk-Predictor on Streamlit shows you can bridge data science outputs and "
+        "interactive user interfaces - exactly the challenge you face here."
+    )
+    pdf.body_text(
+        "ClimateVision's dashboard already has a working foundation: React 18, TypeScript strict mode, "
+        "Vite, TailwindCSS, React Router, Recharts, and a fully-typed API client. Your job is not to "
+        "start from scratch - it is to take this functional base and build the components, pages, and "
+        "interactions that turn it into a polished, production-ready environmental monitoring dashboard "
+        "that NGOs and researchers can actually use."
+    )
+    pdf.body_text(
+        "Your experience with data management interfaces (Koinonia church app) and e-commerce UIs means "
+        "you understand how to build interfaces where users interact with structured data - filtering, "
+        "searching, viewing records, managing subscriptions. That skill maps directly onto ClimateVision's "
+        "run history browser, NGO subscription manager, and alert tracking panel. You have shipped this "
+        "category of UI before."
+    )
+    pdf.ln(2)
+
+    # Role Description
+    pdf.section_title("Your Role on ClimateVision")
+    pdf.body_text(
+        "You own the React dashboard - every pixel the end user sees. The backend API is built, the "
+        "data models are defined, and the component library has a strong foundation. Your mission is "
+        "to complete the user-facing layer: build missing pages, wire components to live API data, "
+        "implement real-time updates, and ensure the interface is responsive, accessible, and fast. "
+        "You are the engineer who makes ClimateVision feel like a real product."
+    )
+    pdf.subsection_title("Core Responsibilities")
+    pdf.bullet("Build and complete all dashboard pages: Dashboard home, NGO Management, Alerts, and deep-dive Analysis views")
+    pdf.bullet("Wire all components to live API data using the existing api.ts client - replace mock/static data throughout")
+    pdf.bullet("Implement real-time run status updates using polling (useRunPolling hook) and WebSocket for live job tracking")
+    pdf.bullet("Build the NGO management page - organisation registration, subscription setup, alert acknowledgment")
+    pdf.bullet("Implement the Alerts page - filterable, paginated alert feed with severity badges and map drill-down")
+    pdf.bullet("Extend the Map components - overlay segmentation masks on the map after prediction completes")
+    pdf.bullet("Add component-level tests using Vitest and React Testing Library")
+    pdf.bullet("Ensure full responsive design for tablet and desktop breakpoints using TailwindCSS")
+    pdf.bullet("Implement accessibility: keyboard navigation, screen reader labels, focus management")
+    pdf.bullet("Performance: code splitting, lazy loading pages, skeleton loading states already in the UI library")
+    pdf.ln(2)
+
+    # Codebase Ownership
+    pdf.section_title("Your Codebase Ownership")
+    pdf.body_text("You are the primary owner of the entire frontend directory:")
+    pdf.code_block(
+        "frontend/src/                          # PRIMARY OWNER - Full frontend\n"
+        "\n"
+        "  pages/                               # PRIMARY OWNER - All page components\n"
+        "    NewAnalysis.tsx                    # Exists - extend with live map result overlay\n"
+        "    Upload.tsx                         # Exists - connect to /predict/upload endpoint\n"
+        "    RunHistory.tsx                     # Exists - add filters, pagination, search\n"
+        "    Analytics.tsx                      # Exists - connect live data, add date picker\n"
+        "    Settings.tsx                       # Exists - wire to API key and config endpoints\n"
+        "    Dashboard.tsx                      # BUILD - Home page KPI summary\n"
+        "    NGOManagement.tsx                  # BUILD - Org registration + subscriptions\n"
+        "    Alerts.tsx                         # BUILD - Alert feed with severity filters\n"
+        "\n"
+        "  components/                          # PRIMARY OWNER - All UI components\n"
+        "    charts/                            # Extend existing Recharts components\n"
+        "    Map/                               # Extend - add mask overlay on results\n"
+        "    ngo/                               # Complete - wire AlertsPanel, SubscriptionManager\n"
+        "    results/                           # Complete - wire ResultsPanel to live predictions\n"
+        "    runs/                              # Extend RunCard with status polling\n"
+        "    ui/                                # Extend UI library as needed\n"
+        "\n"
+        "  api.ts                               # CO-OWNER - Add any missing endpoint calls\n"
+        "  types.ts                             # CO-OWNER - Add frontend-specific types\n"
+        "  contexts/                            # CO-OWNER - AppContext, ToastContext\n"
+        "  hooks/                               # PRIMARY OWNER - useGeocoding, useRunPolling\n"
+        "\n"
+        "  tests/                               # PRIMARY OWNER - Component tests (to be created)\n"
+        "    components/\n"
+        "    pages/"
+    )
+    pdf.ln(2)
+
+    # 3-Month Timeline
+    pdf.section_title("Your 3-Month Delivery Timeline")
+    pdf.month_block("MONTH 1: Foundation & Live Data (Weeks 1-4)", [
+        ("Week 1-2: Setup & API Wiring", [
+            "Clone repo, install deps, run dev server - verify all pages render",
+            "Run the FastAPI backend locally and confirm api.ts endpoints connect",
+            "Wire RunHistory page to live /runs API data - replace any static data",
+            "Wire Analytics page to live run metrics - confirm charts render with real data",
+            "Add loading skeletons (SkeletonCard already exists) to all data-fetching pages",
+        ]),
+        ("Week 3-4: Dashboard Home & Settings", [
+            "Build Dashboard.tsx - KPI summary cards: total runs, alerts, analysis breakdown",
+            "Add Dashboard as the new root route (/) and move NewAnalysis to /new-analysis",
+            "Wire Settings.tsx to API config endpoints - API base URL, analysis preferences",
+            "Implement Toast notifications for success/error states across all forms",
+        ]),
+    ])
+    pdf.month_block("MONTH 2: NGO Features & Real-Time (Weeks 5-8)", [
+        ("Week 5-6: NGO Management Page", [
+            "Build NGOManagement.tsx - list registered organisations from /organizations endpoint",
+            "Implement organisation registration form with validation",
+            "Build SubscriptionManager UI - region bbox picker + analysis type + threshold",
+            "Wire to POST /organizations and POST /organizations/{id}/subscriptions endpoints",
+        ]),
+        ("Week 7-8: Alerts & Real-Time Updates", [
+            "Build Alerts.tsx - paginated alert feed filtered by severity and analysis type",
+            "Implement alert acknowledgment button wired to PATCH /organizations/{id}/alerts/{id}",
+            "Extend useRunPolling hook to poll job status and update UI when predictions complete",
+            "Add live segmentation mask overlay on RegionMap after a prediction run finishes",
+        ]),
+    ])
+    pdf.month_block("MONTH 3: Polish & Production (Weeks 9-12)", [
+        ("Week 9-10: Testing & Accessibility", [
+            "Set up Vitest and React Testing Library - write tests for all page components",
+            "Test all API integration points with mocked responses",
+            "Accessibility audit: add aria-labels, keyboard nav, focus rings across all pages",
+            "Responsive design audit - tablet (768px) and large desktop (1440px) breakpoints",
+        ]),
+        ("Week 11-12: Performance & Final Integration", [
+            "Implement React.lazy() and Suspense for all page-level code splitting",
+            "Bundle analysis with vite-bundle-visualizer - eliminate unused dependencies",
+            "Full end-to-end test: bbox input -> prediction job -> live status -> result on map",
+            "Final UI polish pass: spacing, typography, colour consistency across all pages",
+        ]),
+    ])
+
+    # Code Pipeline
+    pdf.section_title("Your Code Pipeline")
+    pdf.body_text("Your daily pipeline as frontend developer - from clone to a live feature pushed to GitHub.")
+
+    pdf.subsection_title("Step 1: Setup")
+    pdf.code_block(
+        "git clone https://github.com/Climate-Vision/ClimateVision.git\n"
+        "cd ClimateVision/frontend\n"
+        "npm install\n"
+        "\n"
+        "# Start the backend API (needed for live data)\n"
+        "cd .. && uvicorn climatevision.api.main:app --reload --port 8000 &\n"
+        "\n"
+        "# Start the frontend dev server\n"
+        "cd frontend && npm run dev\n"
+        "# App running at: http://localhost:5173"
+    )
+
+    pdf.subsection_title("Step 2: Build a New Page or Component")
+    pdf.code_block(
+        "# Example: building the Dashboard home page\n"
+        "touch src/pages/Dashboard.tsx\n"
+        "\n"
+        "# Import existing UI primitives - don't rebuild what exists\n"
+        "# Available: Card, Badge, StatusBadge, SkeletonCard, ProgressBar,\n"
+        "#            Tooltip, EmptyState, ErrorBoundary, AnalysisTypeSelector\n"
+        "\n"
+        "# Import charts - already built with Recharts\n"
+        "# Available: TimeSeriesChart, BarChart, GaugeChart\n"
+        "\n"
+        "# Import API functions from api.ts\n"
+        "# import { listRuns, listOrganizations, listAlerts } from '../api'"
+    )
+
+    pdf.subsection_title("Step 3: Connect to Live API Data")
+    pdf.code_block(
+        "# Example: fetching live runs in a component\n"
+        "import { useEffect, useState } from 'react'\n"
+        "import { listRuns } from '../api'\n"
+        "import type { Run } from '../api'\n"
+        "\n"
+        "const [runs, setRuns] = useState<Run[]>([])\n"
+        "const [loading, setLoading] = useState(true)\n"
+        "\n"
+        "useEffect(() => {\n"
+        "  listRuns().then(data => {\n"
+        "    setRuns(data)\n"
+        "    setLoading(false)\n"
+        "  })\n"
+        "}, [])\n"
+        "\n"
+        "# Use SkeletonCard while loading\n"
+        "if (loading) return <SkeletonCard />"
+    )
+
+    pdf.subsection_title("Step 4: Run Quality Checks")
+    pdf.code_block(
+        "# From the frontend/ directory:\n"
+        "\n"
+        "# TypeScript type check - zero errors before pushing\n"
+        "npm run type-check\n"
+        "\n"
+        "# Lint check\n"
+        "npm run lint\n"
+        "\n"
+        "# Run component tests\n"
+        "npm run test\n"
+        "\n"
+        "# Production build - must succeed before any PR\n"
+        "npm run build"
+    )
+
+    pdf.subsection_title("Step 5: Commit & Push Your Work")
+    pdf.code_block(
+        "# Switch to your git identity\n"
+        "source team_docs/switch_user.sh paul\n"
+        "\n"
+        "git checkout develop && git pull origin develop\n"
+        "git checkout -b feature/frontend-dashboard-home\n"
+        "\n"
+        "# Stage only frontend files\n"
+        "git add frontend/src/pages/Dashboard.tsx\n"
+        "git add frontend/src/main.tsx\n"
+        "\n"
+        "git commit -m \"feat(frontend): add Dashboard home page with KPI summary cards\"\n"
+        "\n"
+        "# Push from your GitHub account\n"
+        "git push paul feature/frontend-dashboard-home\n"
+        "\n"
+        "# Branch naming convention:\n"
+        "# feature/frontend-*    new UI features\n"
+        "# fix/frontend-*        bug fixes\n"
+        "# refactor/frontend-*   component refactoring"
+    )
+
+    pdf.section_title("Your Key Collaborators")
+    pdf.bullet("Olufemi Taiwo (femi23) - He owns the FastAPI backend your api.ts calls. Any new endpoint you need, request it from him. Coordinate on response shapes, pagination, and error formats.")
+    pdf.bullet("@Goldokpa (Project Owner) - He built the original api.ts and App shell. He is your first point of contact for architecture questions and has context on every frontend design decision.")
+    pdf.bullet("@franchaise (Analytics Lead) - His carbon metrics and KPI data feed your Analytics and Dashboard pages. Agree on the JSON structure for chart data with him.")
+    pdf.bullet("Victor Mbachu (@cutewizzy11 in other refs) - If Docker or CI/CD issues block your local dev, coordinate with the infrastructure owner.")
+    pdf.bullet("@edoh-Onuh (ML Lead) - Model prediction outputs appear as map overlays in your UI. Coordinate on the GeoJSON mask format and confidence score schema so your map component renders them correctly.")
+
+    pdf.output(os.path.join(OUTPUT_DIR, "Paul_cutewizzy11_Role.pdf"))
+    print("Created: Paul_cutewizzy11_Role.pdf")
+
+
+def create_gold_doc():
+    pdf = RoleDoc("Gold Okpa")
+    pdf.add_page()
+
+    pdf.set_font("Helvetica", "B", 18)
+    pdf.cell(0, 10, "Gold Okpa", align="C", new_x="LMARGIN", new_y="NEXT")
+    pdf.set_font("Helvetica", "", 11)
+    pdf.set_text_color(100, 100, 100)
+    pdf.cell(0, 7, "Project Owner & Lead Architect - ClimateVision", align="C", new_x="LMARGIN", new_y="NEXT")
+    pdf.set_text_color(0, 0, 0)
+    pdf.ln(5)
+
+    pdf.key_value("GitHub", "@Goldokpa")
+    pdf.key_value("Access Level", "Owner (Admin)")
+    pdf.key_value("Email", "okpagold@gmail.com")
+    pdf.key_value("Project Duration", "Ongoing")
+    pdf.ln(3)
+
+    # Role Overview
+    pdf.section_title("Your Role on ClimateVision")
+    pdf.body_text(
+        "You built ClimateVision from the ground up. Every foundational layer of this system - the React "
+        "frontend and API client, the Google Earth Engine integration with service account auth and synthetic "
+        "NDVI fallback, the data pipeline scripts, the training and evaluation infrastructure, the Colab "
+        "training notebook, and the overall architecture - was shipped by you. You are not just the project "
+        "owner in title. You are the technical architect, the integration lead, and the person who knows "
+        "every module of this codebase at a deep level."
+    )
+    pdf.body_text(
+        "As the team scales, your role shifts from building everything yourself to orchestrating six "
+        "specialist engineers - setting the architectural direction, reviewing and merging their code, "
+        "maintaining the integrity of the overall system, and ensuring every module fits together cleanly. "
+        "You are the final authority on what goes into the main branch and what ships to users."
+    )
+    pdf.ln(2)
+
+    pdf.subsection_title("Core Responsibilities")
+    pdf.bullet("Own the overall system architecture and make final decisions on design patterns, module boundaries, and API contracts")
+    pdf.bullet("Review and merge all pull requests into the develop and main branches")
+    pdf.bullet("Maintain config.yaml - the single source of truth for all model, data, and API configuration")
+    pdf.bullet("Own the Google Earth Engine integration and satellite data orchestration at the system level")
+    pdf.bullet("Manage GitHub repository: branch protection rules, secrets, environment variables, and access permissions")
+    pdf.bullet("Coordinate sprint planning, milestone tracking, and cross-team dependency resolution")
+    pdf.bullet("Own the release process: version tagging, changelog, and production deployment sign-off")
+    pdf.bullet("Onboard new team members and ensure every engineer has the access and context they need")
+    pdf.bullet("Make final calls on model selection, analysis type prioritisation, and stakeholder deliverables")
+    pdf.ln(2)
+
+    # Codebase Ownership
+    pdf.section_title("Your Codebase Ownership")
+    pdf.body_text("As project owner you have authority over the full codebase. Your primary ownership areas are:")
+    pdf.code_block(
+        "config.yaml                            # PRIMARY OWNER - All system configuration\n"
+        ".env / .env.example                    # PRIMARY OWNER - Environment secrets template\n"
+        "setup.py / requirements.txt            # PRIMARY OWNER - Package definition\n"
+        "\n"
+        "src/climatevision/                     # ARCHITECT - Full codebase authority\n"
+        "  api/main.py                          # Co-owner with Olufemi - original author\n"
+        "  analysis/                            # Original author - analysis framework\n"
+        "  config.py                            # PRIMARY OWNER - Config management\n"
+        "  db.py                                # PRIMARY OWNER - Database schema\n"
+        "\n"
+        "scripts/                               # ORIGINAL AUTHOR - All pipeline scripts\n"
+        "  prepare_data.py                      # GEE data pipeline (you built this)\n"
+        "  setup_gee.py                         # GEE service account auth\n"
+        "  train.py | evaluate.py | infer.py    # Training & inference scripts\n"
+        "  export_model.py                      # ONNX export\n"
+        "\n"
+        "frontend/                              # ORIGINAL AUTHOR - App shell & API client\n"
+        "  src/App.tsx                          # Main application\n"
+        "  src/api.ts                           # API client (you wrote this)\n"
+        "\n"
+        "notebooks/                             # ORIGINAL AUTHOR\n"
+        "  train_on_colab.ipynb                 # Colab training notebook\n"
+        "\n"
+        ".github/                               # PRIMARY OWNER - CI/CD and repo rules\n"
+        "README.md / CONTRIBUTING.md            # PRIMARY OWNER - Public documentation"
+    )
+    pdf.ln(2)
+
+    # 3-Month Plan
+    pdf.section_title("Your 3-Month Orchestration Plan")
+    pdf.month_block("MONTH 1: Team Integration (Weeks 1-4)", [
+        ("Week 1-2: Onboarding & Access", [
+            "Grant all 6 engineers Maintainer access on GitHub",
+            "Set up branch protection: require passing CI + 1 review on develop",
+            "Create GitHub project board with milestones mapped to each engineer's 3-month timeline",
+            "Distribute and walk through each team member's role document",
+            "Verify all engineers can clone the repo, install dependencies, and run the API locally",
+        ]),
+        ("Week 3-4: Architecture Alignment", [
+            "Hold kickoff session: walkthrough of config.yaml, module boundaries, and API contracts",
+            "Define and document tensor shapes, data formats, and model output schemas",
+            "Review and merge first PRs from each team member - establish code review rhythm",
+            "Set up MLflow server on shared infrastructure for experiment tracking",
+        ]),
+    ])
+    pdf.month_block("MONTH 2: Integration & Quality (Weeks 5-8)", [
+        ("Week 5-6: Cross-Module Integration", [
+            "Integration test: Adeolu's DataLoader -> Edoh's model -> Olufemi's inference API",
+            "Integration test: Olufemi's API output -> Francis' carbon estimation -> Victor's dashboard",
+            "Resolve any data contract mismatches between modules",
+            "Set up automated integration test suite in GitHub Actions",
+        ]),
+        ("Week 7-8: Architecture Reviews", [
+            "Review all module implementations against original architecture design",
+            "Identify and resolve any technical debt or design drift before it compounds",
+            "Run end-to-end test: satellite bbox input -> dashboard output for all 3 analysis types",
+            "Performance profiling: measure API latency and model inference time",
+        ]),
+    ])
+    pdf.month_block("MONTH 3: Production & Release (Weeks 9-12)", [
+        ("Week 9-10: Production Hardening", [
+            "Review all security configurations: API keys, CORS, input validation, secrets management",
+            "Final review of Docker and CI/CD pipeline with Victor",
+            "Load test the API endpoints - verify stability under concurrent requests",
+            "Complete documentation audit: README, API docs, and module docstrings",
+        ]),
+        ("Week 11-12: v1.0 Release", [
+            "Final code review sweep across all modules",
+            "Tag v1.0 release with full changelog",
+            "Deploy to production environment and verify all services healthy",
+            "Publish project to open-source community and notify NGO partners",
+        ]),
+    ])
+
+    # Code Pipeline
+    pdf.section_title("Your Code Pipeline")
+    pdf.body_text("As project owner your pipeline covers architecture, integration testing, PR reviews, and release management - as well as direct development when extending core systems.")
+
+    pdf.subsection_title("Step 1: Daily Project Management")
+    pdf.code_block(
+        "# Check open PRs and review queue\n"
+        "gh pr list --repo Climate-Vision/ClimateVision\n"
+        "\n"
+        "# Check CI status across all branches\n"
+        "gh run list --repo Climate-Vision/ClimateVision --limit 10\n"
+        "\n"
+        "# View open issues\n"
+        "gh issue list --repo Climate-Vision/ClimateVision --label bug"
+    )
+
+    pdf.subsection_title("Step 2: Review & Merge a Team Member's PR")
+    pdf.code_block(
+        "# Fetch and checkout their branch for local testing\n"
+        "git fetch origin\n"
+        "git checkout feature/data-sentinel2-preprocessing\n"
+        "\n"
+        "# Test their code runs correctly\n"
+        "pip install -r requirements.txt\n"
+        "python -c \"from climatevision.data.preprocessing import preprocess_tiles; print('OK')\"\n"
+        "\n"
+        "# Review on GitHub and approve\n"
+        "gh pr review <PR_NUMBER> --approve --body \"Tested locally - preprocessing pipeline works correctly\"\n"
+        "\n"
+        "# Merge into develop\n"
+        "gh pr merge <PR_NUMBER> --squash --delete-branch"
+    )
+
+    pdf.subsection_title("Step 3: Run End-to-End Integration Test")
+    pdf.code_block(
+        "# Start all services\n"
+        "docker-compose up --build -d\n"
+        "\n"
+        "# Test the full pipeline: bbox -> prediction -> response\n"
+        "curl -X POST http://localhost:8000/predict/json \\\n"
+        "  -H \"Content-Type: application/json\" \\\n"
+        "  -d '{\"bbox\": [-60,-15,-45,5], \"start_date\": \"2023-01-01\",\n"
+        "       \"end_date\": \"2023-12-31\", \"analysis_type\": \"deforestation\"}'\n"
+        "\n"
+        "# Run automated integration tests\n"
+        "pytest tests/integration/ -v\n"
+        "\n"
+        "# Verify frontend builds and loads dashboard data\n"
+        "cd frontend && npm run build && npm run preview"
+    )
+
+    pdf.subsection_title("Step 4: Update System Configuration")
+    pdf.code_block(
+        "# Edit the master config (all analysis types, thresholds, model params)\n"
+        "# File: config.yaml\n"
+        "\n"
+        "# Example: update deforestation alert threshold\n"
+        "# deforestation:\n"
+        "#   alert_threshold: 0.15  -> 0.10  (more sensitive)\n"
+        "\n"
+        "# Validate config loads correctly after changes\n"
+        "python - <<'EOF'\n"
+        "from climatevision.config import load_config\n"
+        "cfg = load_config('config.yaml')\n"
+        "print(f\"Analysis types: {list(cfg.keys())}\")\n"
+        "EOF"
+    )
+
+    pdf.subsection_title("Step 5: Tag a Release")
+    pdf.code_block(
+        "# Ensure you are on the owner identity\n"
+        "source team_docs/switch_user.sh gold\n"
+        "\n"
+        "# Merge develop into main for release\n"
+        "git checkout main\n"
+        "git merge develop --no-ff -m \"release: v1.0.0\"\n"
+        "\n"
+        "# Tag the release\n"
+        "git tag -a v1.0.0 -m \"ClimateVision v1.0.0 - Deforestation, Ice Melt, Flood Detection\"\n"
+        "\n"
+        "# Push main and tag to GitHub\n"
+        "git push origin main\n"
+        "git push origin v1.0.0\n"
+        "\n"
+        "# Create GitHub release with changelog\n"
+        "gh release create v1.0.0 \\\n"
+        "  --title \"ClimateVision v1.0.0\" \\\n"
+        "  --notes \"First production release. Supports deforestation, arctic ice, and flood detection.\""
+    )
+
+    pdf.subsection_title("Step 6: Direct Development (Core Systems)")
+    pdf.code_block(
+        "# When extending core architecture directly\n"
+        "source team_docs/switch_user.sh gold\n"
+        "\n"
+        "git checkout develop && git pull origin develop\n"
+        "git checkout -b feature/core-new-analysis-type\n"
+        "\n"
+        "# Make changes to core modules (analysis/, config.py, db.py, api/main.py)\n"
+        "\n"
+        "git add src/climatevision/analysis/\n"
+        "git add config.yaml\n"
+        "git commit -m \"feat(core): add drought detection analysis type to registry\"\n"
+        "\n"
+        "# Push as project owner\n"
+        "git push origin feature/core-new-analysis-type"
+    )
+
+    pdf.section_title("Your Key Collaborators")
+    pdf.bullet("Victor Mbachu (@cutewizzy11) - Co-owner for infrastructure decisions. Coordinate on Dockerfile, CI/CD pipelines, and production deployment architecture.")
+    pdf.bullet("Edoh-Onuh (@edoh-Onuh) - ML Lead. Final authority on model architecture decisions sits with you, but Edoh drives the implementation. Review all model PRs carefully.")
+    pdf.bullet("Olufemi Taiwo (femi23) - API Lead. You are the original author of main.py. Any structural changes to the API must go through your review.")
+    pdf.bullet("Adeolu Mary Oshadare (@Oshgig) - Data Pipeline Lead. You built the GEE scripts she extends. Maintain alignment on data contracts between ingestion and training.")
+    pdf.bullet("Francis Umo (@franchaise) - Analytics Lead. Carbon estimates and impact reports are the primary stakeholder-facing output. Review these deliverables closely.")
+    pdf.bullet("Godswill Chukwu - ML Insights Lead. His experiment results and KPI reports inform your architectural and model selection decisions.")
+
+    pdf.output(os.path.join(OUTPUT_DIR, "Gold_Okpa_Role.pdf"))
+    print("Created: Gold_Okpa_Role.pdf")
+
+
+if __name__ == "__main__":
+    create_adeolu_doc()
+    create_francis_doc()
+    create_olufemi_doc()
+    create_edoh_doc()
+    create_victor_doc()
+    create_godswill_doc()
+    create_paul_doc()
+    create_gold_doc()
+    print(f"\nAll 8 role documents generated in: {OUTPUT_DIR}")