-Leaf-Spot-Disease
🌿 Leaf Disease Detection System
An AI-powered Leaf Disease Detection System built with FastAPI (Backend) and Streamlit (Frontend) using Meta Llama Vision models via Groq API.
This system detects plant diseases from leaf images, evaluates severity, and provides actionable treatment recommendations.
🚀 Live Demo
🔗 Streamlit App:
🎯 Key Features
🔍 Disease Detection
- Detects 500+ plant diseases
- Supports fungal, bacterial, viral, pest & nutrient issues
- AI-powered image analysis
📊 Severity Assessment
- Mild / Moderate / Severe classification
- Confidence score (0–100%)
💡 Smart Recommendations
- Evidence-based treatment suggestions
- Symptom explanation
- Possible causes
⚡ Fast Processing
- 2–5 second response time
- Optimized inference pipeline
🏗️ Project Architecture
🔹 Frontend – main.py
- Streamlit Web App
- Image Upload
- Result Display
- API Integration
🔹 Backend – app.py
- FastAPI REST API
- File Upload Endpoint
- JSON Response Handling
🔹 Core AI Engine – Leaf Disease/main.py
- LeafDiseaseDetector Class
- Groq API Integration
- Base64 Image Processing
- Structured JSON Output
🔹 Utilities – utils.py
- Image Processing
- Base64 Conversion
- Helper Functions
📁 Project Structure
Leaf-Disease-Detection/ │ ├── main.py # Streamlit Frontend ├── app.py # FastAPI Backend ├── utils.py # Utility Functions ├── test_api.py # API Testing Script ├── requirements.txt # Dependencies ├── vercel.json # Deployment Config │ ├── Leaf Disease/ │ └── main.py # Core AI Engine │ └── Media/ # Sample Images
⚙️ Installation Guide
1️⃣ Clone Repository
git clone https://github.com/aniketd33/Leaf-Disease-Detection.git cd Leaf-Disease-Detection 2️⃣ Create Virtual Environment Windows python -m venv venv venv\Scripts\activate Mac/Linux python -m venv venv source venv/bin/activate 3️⃣ Install Dependencies pip install -r requirements.txt 4️⃣ Setup Environment Variables Create .env file:
GROQ_API_KEY=your_groq_api_key MODEL_NAME=meta-llama/llama-4-scout-17b-16e-instruct DEFAULT_TEMPERATURE=0.3 DEFAULT_MAX_TOKENS=1024 ▶ Running the Application 🔹 Run Backend (FastAPI) uvicorn app:app –reload –port 8000 Open:
http://localhost:8000/docs 🔹 Run Frontend (Streamlit) streamlit run main.py Open:
http://localhost:8501 🔹 Run Full Stack Terminal 1:
uvicorn app:app –reload –port 8000 Terminal 2:
streamlit run main.py 📡 API Reference POST /disease-detection-file Upload image for disease detection.
Request: Type: multipart/form-data
Field: file
Response Example: { “disease_detected”: true, “disease_name”: “Brown Spot”, “disease_type”: “fungal”, “severity”: “moderate”, “confidence”: 89.3, “symptoms”: [], “possible_causes”: [], “treatment”: [], “analysis_timestamp”: “2026-02-13T12:00:00” } 🧪 Testing Run API test:
python test_api.py Test with cURL:
curl -X POST “http://localhost:8000/disease-detection-file”
-H “accept: application/json”
-F “file=@Media/brown-spot-4 (1).jpg”
🌍 Deployment
🚀 Vercel (Backend)
npm install -g vercel
vercel –prod
Add environment variable:
GROQ_API_KEY
🌐 Streamlit Cloud (Frontend) Push repo to GitHub
Connect to https://share.streamlit.io
Add secrets
🔬 Tech Stack Python 3.9+
FastAPI
Streamlit
Groq API
Meta Llama Vision
Uvicorn
Requests
📊 Performance Response Time: 2–5 seconds
Accuracy: 85–95%
Max Image Size: 10MB
Supports: JPG, PNG, WebP, BMP, TIFF
🤝 Contributing git checkout -b feature/new-feature git commit -m “Added new feature” git push origin feature/new-feature Create Pull Request on GitHub.
📜 License MIT License
👨💻 Maintainer Aniket Dombale GitHub: https://github.com/aniketd33
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