GenAI Assignment Solutions - Nithin N

GenAI.md

@@ -1,3 +1,16 @@
+<div align="center">
+
+### 👤 Submitted by: **Nithin N**
+
+> ⚠️ **Note:** My primary GitHub account is **[Nithin9585](https://github.com/Nithin9585)** — due to login issues, this submission is made from a secondary account.
+> To verify my profile, projects, and work history, please visit:
+>
+> ## 🔗 [github.com/Nithin9585](https://github.com/Nithin9585)
+
+</div>
+
+---
+
 # GenAI Assignment:
 
 **Evaluation Criteria**
@@ -26,7 +39,7 @@ No code required. We want a **clear, practical proposal** with architecture and
 
 ### Your Solution for problem 1:
 
-You need to put your solution here.
+[**> Click here to view the Architectural Proposal (Solution_1_Video_Notes.md)**](./Solution_1_Video_Notes.md)
 
 ## Problem 2: **Zero-Shot Prompt to generate 3 LinkedIn Post**
 
@@ -36,7 +49,7 @@ Design a **single zero-shot prompt** that takes a user’s persona configuration
 
 ### Your Solution for problem 2:
 
-You need to put your solution here.
+[**> Click here to view the Prompt Design (Solution_2_LinkedIn.md)**](./Solution_2_LinkedIn.md)
 
 ## Problem 3: **Smart DOCX Template → Bulk DOCX/PDF Generator (Proposal + Prompt)**
 
@@ -45,7 +58,7 @@ Users have many Word documents that act like templates (offer letters, certifica
 We want a system that:
 
 1. Converts an uploaded **DOCX** into a reusable **template** by identifying editable fields.
-2. Supports **single generation** (form-fill → DOCX/PDF download).
+2. Supports **single generation** (form-fill → DOCX/PDF output).
 3. Supports **bulk generation** via **Excel/Google Sheet** rows.
 
 ### **Task (No coding)**
@@ -54,7 +67,7 @@ Submit a **proposal** for building this system using GenAI (OpenAI/Gemini) for 
 
 ### Your Solution for problem 3:
 
-You need to put your solution here.
+[**> Click here to view the Template Engine Proposal (Solution_3_Doc_Template.md)**](./Solution_3_Doc_Template.md)
 
 ## Problem 4: Architecture Proposal for 5-Min Character Video Series Generator
 
@@ -66,4 +79,4 @@ Create a **small, clear architecture proposal** (no code, no prompts) describing
 
 ### Your Solution for problem 4:
 
-You need to put your solution here.
+[**> Click here to view the Character Video Architecture (Solution_4_Character_Video.md)**](./Solution_4_Character_Video.md)

Solution_1_Video_Notes.md

@@ -0,0 +1,221 @@
+# Solution: Proposal for "Video-to-Notes" Platform
+
+## The Problem
+
+We have a local folder of long videos (3–4 hours each, 200MB+). We need an automated pipeline to generate a **Summary Package** per video:
+- `Summary.md` — structured notes with key takeaways
+- **Highlight clips** — short video segments of key moments
+- **Screenshots** — frames from important slides/moments
+
+---
+
+## Approach Comparison
+
+### Approach 1: Online/Cloud-Based SaaS (e.g., Pictory, ScreenApp, Exemplary.ai)
+
+```mermaid
+graph LR
+    User[User] -->|Upload 5GB+ per video| Cloud[SaaS Platform]
+    Cloud -->|Black-Box AI| Output[Summary + Clips]
+    Output -->|Download| User
+```
+
+**How it works:** Upload videos to a third-party platform. The platform transcribes, summarizes, and generates clips automatically.
+
+| Factor | Assessment |
+|--------|-----------|
+| File Size | [NO] Upload bottleneck — uploading 200MB–9GB per video is slow and fragile |
+| Duration | [NO] Most platforms cap at 3 hours (ScreenApp Business Plan) — our 3–4hr videos may fail |
+| Batch Processing | [NO] No bulk automation — manual upload per file via browser |
+| Customization | [NO] Black-box AI optimized for "viral" clips, not technical/informational content |
+| Cost | [NO] Subscription-based; 10 × 4hr videos = 2,400 min, exceeds most Pro plan limits |
+
+**Verdict: REJECTED** — Upload friction, duration limits, and no batch control make this unworkable.
+
+---
+
+### Approach 2: Hybrid Architecture — Local Processing + Cloud AI -- RECOMMENDED
+
+```mermaid
+graph LR
+    A[Local Video 2GB+] -->|FFmpeg: Extract Audio| B[Audio File ~60MB]
+    B -->|Upload only audio| C[Deepgram STT API]
+    C -->|Transcript + Timestamps| D[Claude 3.5 Sonnet LLM]
+    D -->|Structured JSON| E[Local FFmpeg]
+    A -->|Original quality source| E
+    E --> F[Clips + Screenshots + Summary.md]
+```
+
+**How it works:**
+1. **Local FFmpeg** extracts only the audio from each video (Opus codec, 64kbps -> ~60MB for 4hrs)
+2. **Deepgram API** transcribes the audio with word-level timestamps (~12 sec per hour of audio)
+3. **Claude 3.5 Sonnet** (200k token context) reads the full transcript and returns a JSON with summary + highlight timestamps
+4. **Local FFmpeg** cuts clips and screenshots from the original high-quality video using those timestamps
+
+**Full Pipeline (Detailed):**
+
+```mermaid
+graph TD
+    subgraph "Phase 1: Ingestion"
+    Start([Start Batch]) --> Scan[Scan Input Folder]
+    Scan --> Check{Valid File?}
+    Check -- No --> LogError[Log to skipped.csv]
+    Check -- Yes --> FFprobe[Extract Metadata via ffprobe]
+    end
+
+    subgraph "Phase 2: Audio Extraction"
+    FFprobe --> Extract["FFmpeg: Extract Opus Audio (-vn -acodec libopus -b:a 64k)"]
+    Extract --> AudioFile(output_audio.opus ~60MB)
+    end
+
+    subgraph "Phase 3: Transcription"
+    AudioFile --> Deepgram["Deepgram Nova-2 API (diarize + timestamps)"]
+    Deepgram --> Transcript[Full Transcript + Word Timestamps JSON]
+    end
+
+    subgraph "Phase 4: Intelligence"
+    Transcript --> LLM["Claude 3.5 Sonnet (200k context window)"]
+    LLM --> Analysis[Structured JSON: Summary + Highlight Segments]
+    end
+
+    subgraph "Phase 5 & 6: Asset Production"
+    Analysis --> Cut["FFmpeg: Cut Clips (-ss start -t duration)"]
+    Analysis --> Snap["FFmpeg: Screenshots (-vframes 1)"]
+    Cut --> Assets[assets folder]
+    Snap --> Assets
+    Assets --> Assemble[Generate Summary.md via Jinja2]
+    end
+
+    Assemble --> End([Done])
+```
+
+| Factor | Assessment |
+|--------|-----------|
+| File Size | [YES] Only ~60MB audio uploaded (97% bandwidth reduction) |
+| Duration | [YES] No limit — Claude 3.5 handles 200k tokens (full 4hr transcript) |
+| Batch Processing | [YES] Python script with retry logic, state persistence, skips corrupt files |
+| Customization | [YES] Full control over prompt — prioritize technical/informational content |
+| Cost |  ~$1.50 per 4hr video (Deepgram $0.0043/min + Claude API) |
+
+**Verdict: RECOMMENDED** — Solves the bandwidth problem (audio extraction) and the context problem (200k token LLM).
+
+---
+
+### Approach 3: Fully Offline — Open-Source Models (Faster-Whisper + Llama 3)
+
+```mermaid
+graph LR
+    A[Local Video] -->|Faster-Whisper on GPU| B[Local Transcript]
+    B -->|Llama 3 70B| C[Local JSON Summary]
+    C -->|FFmpeg| D[Clips + Screenshots]
+```
+
+**How it works:** Run everything locally — Faster-Whisper for transcription, Llama 3 70B for summarization, FFmpeg for asset generation. Zero data leaves the machine.
+
+| Factor | Assessment |
+|--------|-----------|
+| File Size | [YES] No upload needed |
+| Duration | [WARN] Llama 3 70B needs 40GB VRAM (dual GPU or A6000 $4,000+) |
+| Batch Processing | [WARN] Prone to OOM crashes on long files; requires chunking (lossy summaries) |
+| Customization | [YES] Full control |
+| Cost | [WARN] High CapEx (hardware); $0 per-run after setup |
+| Privacy | [YES] Air-gapped — no data leaves premises |
+
+**Verdict: CONDITIONAL -- Viable only if data is classified** — Requires enterprise GPU hardware. Smaller models (8B) hallucinate timestamps and lose context on 4hr videos.
+
+---
+
+## Strategic Recommendation Summary
+
+| Feature | SaaS (Cloud Only) | **Hybrid (Local + API)** | Offline (Local Only) |
+|---|---|---|---|
+| Data Movement | [NO] Upload GBs | [YES] Upload MBs (audio only) | [YES] Zero transfer |
+| Long Context (4hr) | [NO] Often capped <3hrs | [YES] 200k+ tokens | [WARN] Hardware limited |
+| Cost Efficiency | [NO] High subscriptions |  ~$1.50/video | [WARN] High CapEx |
+| Privacy | [NO] 3rd party storage | [WARN] Transient API calls | [YES] Air-gapped |
+| Batch Automation | [NO] Manual uploads | [YES] Fully scripted | [WARN] OOM risk |
+| **Recommendation** | **Reject** | ** Adopt** | **Reject (unless classified)** |
+
+---
+
+## JSON Schema (LLM Output Contract)
+
+The LLM must return a strict JSON so FFmpeg commands can be generated reliably:
+
+```json
+{
+  "meta": {
+    "title": "Q3 All-Hands Meeting",
+    "main_topics": ["Financials", "Roadmap", "Q&A"]
+  },
+  "summary_content": {
+    "executive_summary": "200-300 word overview...",
+    "key_takeaways": ["Insight 1", "Insight 2"],
+    "action_items": ["Follow up on budget", "Schedule roadmap review"]
+  },
+  "segments": [
+    {
+      "id": "seg_001",
+      "timestamp_start": "00:15:20",
+      "timestamp_end": "00:18:45",
+      "segment_title": "Q3_Financials_Overview",
+      "description": "CFO presents Q3 revenue breakdown",
+      "reasoning": "High information density — key financial decision point",
+      "assets_to_generate": { "clip": true, "screenshot": false }
+    }
+  ]
+}
+```
+
+**Key design decisions:**
+- `timestamp_start/end` enforced as `HH:MM:SS` regex — FFmpeg rejects any other format
+- `reasoning` field forces Chain-of-Thought, reducing hallucinated timestamps
+- `assets_to_generate` flags let the LLM decide: not every moment needs a 50MB clip
+
+---
+
+## Zero-Shot Prompt (The LLM Instruction)
+
+```
+You are a Senior Technical Archivist. Process the transcript below into a structured JSON knowledge artifact.
+
+RULES (Anti-Hallucination Protocol):
+1. Only use timestamps that exist verbatim in the transcript. Never guess.
+2. Add a 10-second pad: subtract 10s from start, add 10s to end of each clip.
+3. Clips must be 30 seconds–3 minutes long.
+4. Prioritize: technical demos, decisions, debates, conclusions. Skip banter/logistics.
+5. Output ONLY valid JSON. No markdown fencing, no preamble.
+
+PROCESS:
+1. Scan the full transcript to map the video structure.
+2. Identify 5–10 highlight candidates.
+3. Verify timestamps exist in the source text.
+4. Output the JSON.
+
+[TRANSCRIPT BELOW]
+```
+
+**Why Zero-Shot?** Few-shot examples waste context window tokens. With a 4hr transcript (40k tokens), we need every token for the actual content. Claude 3.5 follows detailed zero-shot instructions reliably.
+
+---
+
+## Bulk Processing & Error Handling
+
+**Resilience features:**
+- `ffprobe` validates each file before processing — corrupt files logged to `skipped.csv`, batch continues
+- API calls wrapped in exponential backoff retry (2s  4s  8s, max 5 retries)
+- `job_status.json` tracks completed videos — if script crashes at video #49, it resumes at #50
+
+**Output structure:**
+```
+Output/
+ 2024-11-05_Q3_All_Hands/
+    Summary.md
+    manifest.json
+    assets/
+        Clip_01_Financials_00-15-20.mp4
+        Clip_02_Roadmap_01-10-00.mp4
+        Screenshot_01_Slide_A.jpg
+```
+
+**Batch Report** generated at end: `Batch_Report.csv` with filename, duration, status, cost estimate per video.