Update README.md

README.md

@@ -1,389 +1,7 @@
-<<<<<<< HEAD
-# Plant Disease Detection - UI and Deployment
-
-This directory contains the Gradio-based user interface and deployment code for the Plant Disease Detection project.
-
-## Team Information
-
-**Team Number:** [Add your team number]
-
-**Team Members:**
-- [Add team member names here]
-
-## Links
-
-- **GitHub Repository:** https://github.kcl.ac.uk/K23064919/smallGroupProject
-- **Deployed App:** [Add Hugging Face Spaces URL here]
-- **Trained Model:** [Add model download link or ClearML model ID here]
-
-## Project Structure
-
-```
-plant-disease-ui/
-├── ui/
-│   ├── app.py              # Main Gradio application
-│   ├── config.py           # Configuration (class names, paths, etc.)
-│   ├── model_loader.py     # Model loading utilities
-│   ├── utils.py            # Utility functions (preprocessing, etc.)
-│   └── examples/           # Example images for gallery
-├── models/
-│   ├── mock_model.py       # Mock model for development
-│   └── best_model.pth      # (To be added) Trained model weights
-├── docs/
-│   └── deployment_guide.md # Deployment instructions
-├── requirements.txt        # Python dependencies
-└── README.md              # This file
-```
-
-## Features
-
-### Core Features
-- ✅ **Image Upload:** Upload plant leaf images for disease detection
-- ✅ **Top-K Predictions:** Display top 10 predictions with confidence scores
-- ✅ **Formatted Output:** Clean, readable prediction results
-
-### Advanced Features
-- ✅ **Multiple Models:** Switch between different trained models (CNN, Transfer Learning)
-- ✅ **Example Gallery:** Pre-loaded example images for quick testing
-- ✅ **Batch Processing:** Upload and classify multiple images at once
-- ✅ **Flag Predictions:** Report incorrect predictions
-- ✅ **Confidence Threshold:** Filter predictions by minimum confidence level
-- ✅ **Detailed Information:** View plant type, disease name, and health status
-
-## Setup Instructions
-
-### 1. Install Dependencies
-
-```bash
-# Create a virtual environment (recommended)
-python -m venv venv
-source venv/bin/activate  # On Windows: venv\Scripts\activate
-
-# Install required packages
-pip install -r requirements.txt
-```
-
-### 2. Add Example Images (Optional)
-
-To enable the example gallery feature:
-
-```bash
-# Create examples directory
-mkdir -p ui/examples
-
-# Add plant disease images to ui/examples/
-# You can download sample images from the PlantVillage dataset
-```
-
-To download example images programmatically:
-
-```python
-from datasets import load_dataset
-
-# Load PlantVillage dataset
-dataset = load_dataset("EdBianchi/plant-village")
-
-# Save some example images
-import os
-os.makedirs("ui/examples", exist_ok=True)
-
-for i in range(10):  # Save 10 examples
-    img = dataset['train'][i * 1000]['image']  # Sample every 1000th image
-    img.save(f"ui/examples/example_{i}.jpg")
-```
-
-### 3. Run the App Locally
-
-**Option A: Using Mock Model (for development)**
-
-```bash
-cd ui
-python app.py
-```
-
-The app will start at `http://localhost:7860`
-
-**Option B: Using Your Trained Model**
-
-First, modify `app.py` to load your real model:
-
-```python
-# In app.py, change the last line:
-demo = create_interface(use_mock=False)  # Change to False
-```
-
-Then run:
-
-```bash
-cd ui
-python app.py
-```
-
-### 4. Configure for Real Model
-
-When your team's model is ready, you have several options:
-
-#### Option 1: Load from Local File
-
-```python
-# In model_loader.py, update the model path
-MODEL_PATH = "models/best_model.pth"
-
-# Then in app.py:
-app = PlantDiseaseApp(use_mock=False)
-```
-
-#### Option 2: Load from ClearML
-
-```python
-# In app.py or model_loader.py:
-loader = ModelLoader(use_mock=False)
-model = loader.load_from_clearml(
-    project_name="Plant Disease Detection",
-    task_name="CNN Training"
-)
-```
-
-#### Option 3: Load from Hugging Face Hub
-
-```python
-# First, upload your model to HF Hub
-# Then in model_loader.py:
-loader = ModelLoader(use_mock=False)
-model = loader.load_from_huggingface("your-username/plant-disease-model")
-```
-
-## Deployment to Hugging Face Spaces
-
-### Step 1: Create a Hugging Face Account
-
-1. Go to https://huggingface.co/ and create an account
-2. Verify your email address
-
-### Step 2: Create a New Space
-
-1. Click on your profile → "New Space"
-2. Space name: `plant-disease-detection`
-3. License: Apache 2.0
-4. Select SDK: **Gradio**
-5. Make it **Public**
-6. Click "Create Space"
-
-### Step 3: Prepare Files for Deployment
-
-Create these files in the root of your Space:
-
-**app.py** (Simplified version for HF Spaces)
-```python
-# Copy ui/app.py and modify the imports to work in the flat structure
-```
-
-**requirements.txt**
-```
-torch
-torchvision
-gradio
-Pillow
-numpy
-huggingface-hub
-```
-
-**README.md** (for the Space)
-```markdown
 ---
-title: Plant Disease Detection
-emoji: 🌱
-colorFrom: green
-colorTo: blue
+title: smallGroupProject
 sdk: gradio
-sdk_version: 4.0.0
 app_file: app.py
 pinned: false
----
-
-# Plant Disease Detection
-
-AI-powered plant disease detection from leaf images.
-Developed by [Your Team Name] for King's College London.
-```
-
-### Step 4: Upload Your Model
-
-**Option A: Upload weights to the Space**
-
-1. Upload your `best_model.pth` to the Space
-2. Modify `app.py` to load from this file
-
-**Option B: Use Hugging Face Hub**
-
-1. Upload model to HF Model Hub:
-```python
-from huggingface_hub import HfApi
-
-api = HfApi()
-api.upload_file(
-    path_or_fileobj="models/best_model.pth",
-    path_in_repo="model.pth",
-    repo_id="your-username/plant-disease-model",
-    repo_type="model"
-)
-```
-
-2. Load in app:
-```python
-from huggingface_hub import hf_hub_download
-model_path = hf_hub_download(
-    repo_id="your-username/plant-disease-model",
-    filename="model.pth"
-)
-```
-
-**Option C: Fetch from ClearML**
-
-1. Add ClearML credentials to Space Secrets
-2. Use the `load_from_clearml()` function
-
-### Step 5: Deploy
-
-1. Upload all files to your HF Space repository
-2. The app will automatically build and deploy
-3. Test at: `https://huggingface.co/spaces/your-username/plant-disease-detection`
-
-## Model Integration Guide
-
-### Your CNN Model Structure
-
-When integrating your actual trained model, make sure to update `model_loader.py` with your actual CNN architecture:
-
-```python
-class YourCNNModel(nn.Module):
-    def __init__(self, num_classes=39):
-        super(YourCNNModel, self).__init__()
-
-        # Add your actual CNN architecture here
-        # This should match what you used for training
-
-    def forward(self, x):
-        # Your forward pass
-        return x
-```
-
-### Loading Trained Weights
-
-```python
-# Load model
-model = YourCNNModel(num_classes=39)
-
-# Load trained weights
-checkpoint = torch.load('path/to/best_model.pth', map_location=device)
-
-# If you saved the entire model:
-model = checkpoint
-
-# If you saved just state_dict:
-model.load_state_dict(checkpoint)
-
-# Or if you saved optimizer and other info:
-model.load_state_dict(checkpoint['model_state_dict'])
-```
-
-## Testing the UI
-
-### Manual Testing Checklist
-
-- [ ] Upload a single image and get predictions
-- [ ] Try different models from the dropdown
-- [ ] Adjust confidence threshold slider
-- [ ] Test example gallery (if images added)
-- [ ] Upload multiple images for batch processing
-- [ ] Flag a prediction
-- [ ] Check all tabs load correctly
-- [ ] Verify predictions match expected classes
-
-### Automated Testing
-
-```python
-# Run tests
-cd ui
-python -m pytest test_app.py  # (Create tests if needed)
-```
-
-## Troubleshooting
-
-### Common Issues
-
-**1. ModuleNotFoundError**
-```bash
-# Make sure all dependencies are installed
-pip install -r requirements.txt
-```
-
-**2. Model Loading Error**
-```python
-# Check that the model architecture matches the saved weights
-# Make sure you're using the same num_classes (39)
-```
-
-**3. Image Size Issues**
-```python
-# Ensure images are being resized to (256, 256)
-# Check config.py IMAGE_SIZE setting
-```
-
-**4. CUDA/GPU Errors**
-```python
-# The app automatically falls back to CPU
-# Check: torch.cuda.is_available()
-```
-
-## Contributing
-
-When contributing to this UI:
-
-1. Create a new branch for your feature
-2. Test locally with mock model first
-3. Test with real model before pushing
-4. Update this README if adding new features
-5. Ensure code is well-commented
-
-## TODO
-
-- [ ] Add more example images to gallery
-- [ ] Integrate with actual trained models
-- [ ] Add disease information/treatment suggestions
-- [ ] Implement persistent flagging system (database)
-- [ ] Add data visualization for batch results
-- [ ] Create comprehensive tests
-
-## Resources
-
-- [Gradio Documentation](https://gradio.app/docs/)
-- [HuggingFace Spaces Guide](https://huggingface.co/docs/hub/spaces)
-- [ClearML Python API](https://clear.ml/docs/latest/docs/references/sdk/)
-- [PlantVillage Dataset](https://huggingface.co/datasets/EdBianchi/plant-village)
-
-## License
-
-[Specify your license here]
-
-## Acknowledgments
-
-- King's College London, 5CCSAGAP Course
-- PlantVillage Dataset creators
-- Course instructors and TAs
-=======
----
-title: SmallGroupProject
-emoji: 🏢
-colorFrom: pink
-colorTo: green
-sdk: gradio
-sdk_version: 5.49.1
-app_file: app.py
-pinned: false
-short_description: plant disease classifier
 base-model: develop
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
->>>>>>> 28a307c67a9240ecbd9634ebcf90deb7de38d076
+---