🤖 Image Classification Model

This is a Streamlit web app that integrates MobileNetV2 and a CIFAR-10 model for image classification. Users can upload images and receive predictions with confidence scores from either model. It features a sleek navigation bar for easy switching and real-time results, which is ideal for learning and practical use.

🌐 Live App: Streamlit Cloud Deployment (See deployment section)

🎯 Key Features

• Dual Model Support:

  • MobileNetV2 (ImageNet): Recognizes 1,000+ different classes - animals, vehicles, objects, etc.
  • Custom CIFAR-10 Model: Classifies into 10 specific categories - airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck.

• Intuitive Interface:

  • Navigation Bar: Seamlessly switch between models using sidebar menu
  • Real-Time Classification: Upload images and get instant predictions with confidence scores
  • Beautiful UI: Progress bars, formatted results, responsive design

• Educational & Practical:

  • Learn about deep learning models and their performance
  • Practical image classification for real-world applications
  • Fully open-source and customizable

📋 Prerequisites

• Python 3.10+ (Recommended: 3.11 for best compatibility)
• Git (for version control)
• Web Browser (Chrome, Firefox, Safari, Edge)
• ~2GB disk space (for dependencies and models)

🚀 Installation & Setup

Quick Start (Local Development)

1. Clone the repository:

   git clone https://github.com/vivek-kumar-P/Image-Classification-model
   cd Image-Classification-model

2. Create virtual environment (Python 3.11 recommended):

   python -m venv venv
   
   # On Windows:
   venv\Scripts\activate
   
   # On macOS/Linux:
   source venv/bin/activate

3. Install dependencies:

   pip install --upgrade pip
   pip install -r requirements.txt

4. Run the app:

   streamlit run app.py

5. Access the app: Opens automatically at http://localhost:8501

Windows Installation (Step-by-Step)

# Navigate to project
cd "e:\code better\Image-Classification-model"

# Activate venv
.\venv\Scripts\Activate.ps1

# Install requirements
pip install -r requirements.txt

# Run app
streamlit run app.py

Usage

1. Select a Model:

   • Use the sidebar to choose between MobileNetV2 or CIFAR-10

2. Upload an Image:

   • Click "Choose an image..." and select JPG/PNG file
   • Supported formats: JPEG, PNG, JPG
   • Max file size: 200MB

3. View Predictions:

   • Real-time classification results
   • Top predictions with confidence scores
   • Progress bars showing confidence levels

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🐛 Known Issues & Solutions

Issue 1: TensorFlow Installation Error

Error: ERROR: Could not find a version that satisfies the requirement tensorflow

Cause: TensorFlow 2.15+ requires Python 3.10-3.13. Python 3.14+ not yet supported.

Solution:

# Check Python version
python --version

# If Python 3.14+, use Python 3.11 instead
# Create new venv with Python 3.11
python3.11 -m venv venv
venv\Scripts\activate
pip install -r requirements.txt

Issue 2: Missing Visual C++ Redistributable

Error: OSError: [WinError 126] The specified module could not be found

Cause: TensorFlow needs Microsoft Visual C++ runtime libraries.

Solution:

1. Download: https://aka.ms/vs/17/release/vc_redist.x64.exe
2. Run the installer (admin mode)
3. Restart your terminal/IDE
4. Reinstall TensorFlow: pip install tensorflow

Issue 3: Port Already in Use

Error: Port 8501 is already in use

Cause: Another Streamlit app is running on the same port.

Solution:

# Use a different port
streamlit run app.py --server.port 8502

# Or kill existing process (Windows):
netstat -ano | findstr :8501
taskkill /PID <PID> /F

Issue 4: Dependencies Conflict

Error: error: subprocess-exited-with-error with numpy version conflict

Cause: Incompatible version pinning in requirements.txt

Solution:

# Clear pip cache
pip cache purge

# Upgrade pip
pip install --upgrade pip

# Reinstall with fresh environment
pip install -r requirements.txt --no-cache-dir

Issue 5: Streamlit Cloud Deployment Error

Error: TensorFlow installation fails on Streamlit Cloud

Cause: Streamlit Cloud trying to install with wrong Python version

Solution:

1. Delete the app from Streamlit Cloud
2. Redeploy and select Python 3.11 in Advanced Settings
3. Click Deploy

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🌐 Streamlit Cloud Deployment

Deploy Your App (Live on Web)

1. Push code to GitHub:

   git add .
   git commit -m "Your message"
   git push origin main

2. Go to Streamlit Cloud:

   • Visit https://share.streamlit.io
   • Sign in with GitHub

3. Deploy New App:

   • Click "New app"
   • Select repository: vivek-kumar-P/Image-Classification-model
   • Branch: main
   • Main file: app.py

4. Configure Python Version (IMPORTANT):

   • Click "Advanced settings"
   • Select Python 3.11 from dropdown
   • Click "Deploy"

5. Wait for deployment (2-3 minutes):

   • App will be live at: https://your-username-image-classification.streamlit.app

Troubleshooting Deployment

Build fails with TensorFlow error?

• Delete app → Redeploy with Python 3.11 selected

Port/Process errors?

• Streamlit Cloud handles this automatically, just redeploy

Dependencies not installing?

• Update requirements.txt with compatible versions
• Commit → Push → Redeploy

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📊 System Architecture

Local Development:

Your Computer
├─ Python 3.11
├─ Virtual Environment
├─ Dependencies (from requirements.txt)
├─ App code (app.py)
├─ Models (cifar10_model.h5)
└─ Runs on localhost:8501

Streamlit Cloud:

Streamlit's Servers
├─ Python 3.11 (selected during deploy)
├─ Virtual Environment (auto-created)
├─ Dependencies (installed from requirements.txt)
├─ Your code (cloned from GitHub)
└─ Runs 24/7 at streamlit.app URL

Key Point: Cloud app is independent from local setup! Deleting local files won't affect the live app.

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📦 Project Structure

Image-Classification-model/
├── app.py                      # Main Streamlit application
├── train.py                    # Model training script
├── cifar10_model.h5           # Pre-trained CIFAR-10 model
├── requirements.txt           # Python dependencies
├── README.md                  # Documentation (this file)
├── .streamlit/
│   └── config.toml            # Streamlit Cloud config
└── Testing Images/
    ├── CIFAR-10/              # Test images
    └── MobileNetV2/           # Test images

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📝 Recent Updates (January 28, 2026)

Changes Made:

✅ Fixed dependency conflicts in requirements.txt ✅ Added flexible version constraints instead of strict pinning ✅ Added .streamlit/config.toml for Cloud deployment ✅ Updated app.py with better error handling ✅ Tested on Python 3.11 with TensorFlow 2.15.0 ✅ Verified Streamlit Cloud deployment

Problems Tackled:

1. TensorFlow not available for Python 3.14 → Switched to Python 3.11
2. Dependency version conflicts → Updated requirements.txt with compatible versions
3. Missing Visual C++ libraries → Provided download and install instructions
4. Port conflicts on local → Added port configuration examples
5. Streamlit Cloud deployment failures → Added Python version selection guide

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🔧 Troubleshooting Checklist

Problem	Command	Status
Wrong Python version	python --version	Check output
venv not activating	venv\Scripts\activate.ps1	PowerShell
Dependencies failing	pip install --upgrade pip	Then reinstall
App not starting	streamlit run app.py --logger.level=error	Debug mode
Port in use	streamlit run app.py --server.port 8502	Alt port
TensorFlow error	Use Python 3.11	Fallback version

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📚 Model Information

MobileNetV2 (ImageNet)

• Classes: 1,000+
• Input: 224×224 RGB images
• Performance: Fast, mobile-friendly
• Use case: General object detection

CIFAR-10

• Classes: 10 (airplane, car, bird, cat, deer, dog, frog, horse, ship, truck)
• Input: 32×32 RGB images
• Performance: Specialized classification
• Use case: Specific object detection in 10 categories

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🚀 Future Enhancements

• Add image download with predictions
• Add batch image processing
• Deploy custom model training interface
• Add prediction history
• Mobile app version
• Real-time webcam classification

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📄 License

This project is open-source and available for educational and commercial use.

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👥 Contributing

Feel free to:

• Fork the repository
• Create issues for bugs/features
• Submit pull requests
• Share feedback and improvements

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🙏 Acknowledgments

• Streamlit - Web app framework
• TensorFlow - Deep learning library
• Keras - Neural network API
• MobileNetV2 - Pre-trained model
• CIFAR-10 Dataset - Training data

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📞 Support & Contact

For issues or questions:

1. Check the Troubleshooting section above
2. Review Known Issues & Solutions
3. Open an issue on GitHub
4. Contact the maintainer

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Last Updated: January 28, 2026 Status: ✅ Production Ready for Streamlit Cloud