Update app.py

app.py

@@ -1,13 +1,15 @@
-from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi import FastAPI, File, UploadFile, HTTPException, Form
 from fastapi.middleware.cors import CORSMiddleware
 from huggingface_hub import hf_hub_download
 from tensorflow import keras
 import numpy as np
 from PIL import Image
 import io
-from typing import Dict
+from typing import Dict, Optional
 import uvicorn
 import os
+import httpx
+from groq import Groq
 
 app = FastAPI(title="CNN Image Prediction API")
 
@@ -20,13 +22,20 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
+# Initialize Groq client
+client = Groq(
+    api_key="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",
+)
+
+# Weather API key - get from openweathermap.org
+WEATHER_API_KEY = "6af4193be6ef82758dde960743909a80"
+
 # Global variable for model
 model = None
-IMG_SIZE = (224, 224)  # Adjust based on your model's input size
+IMG_SIZE = (224, 224)
 
 # Class labels
 CLASS_LABELS = [
-    
     "Apple___Apple_scab", "Apple___Black_rot", "Apple___Cedar_apple_rust", "Apple___Healthy",
     "Blueberry___Healthy", "Cherry_(including_sour)___Powdery_mildew", "Cherry_(including_sour)___Healthy",
     "Corn_(maize)___Cercospora_leaf_spot_Gray_leaf_spot", "Corn_(maize)__Common_rust", "Corn_(maize)___Northern_Leaf_Blight",
@@ -34,35 +43,27 @@ CLASS_LABELS = [
     "Grape___Healthy", "Orange__Haunglongbing(Citrus_greening)", "Peach___Bacterial_spot", "Peach___Healthy",
     "Pepper,bell__Bacterial_spot", "Pepper,bell__Healthy", "Potato___Early_blight", "Potato___Late_blight", "Potato___Healthy",
     "Raspberry___Healthy", "Soybean___Healthy", "Squash___Powdery_mildew", "Strawberry___Leaf_scorch", "Strawberry___Healthy",
- 'cotton : bacterial_blight',
-' cotton : curl_virus',
- ' fussarium_wilt',"Tomato___Leaf_Mold", "Tomato___Septoria_leaf_spot",
- '  fussarium_wilt',"Tomato___Leaf_Mold", "Tomato___Septoria_leaf_spot",
+    'cotton : bacterial_blight', ' cotton : curl_virus', ' fussarium_wilt', "Tomato___Leaf_Mold", "Tomato___Septoria_leaf_spot",
+    '  fussarium_wilt', "Tomato___Leaf_Mold", "Tomato___Septoria_leaf_spot",
     "Tomato___Spider_mites_Two-spotted_spider_mite", "Tomato___Target_Spot", "Tomato___Tomato_Yellow_Leaf_Curl_Virus",
     "Tomato___Tomato_mosaic_virus", "Tomato___Healthy"
 ]
 
-
-
 @app.on_event("startup")
 async def load_model_keras():
     """Load the Keras model from Hugging Face on startup"""
     global model
     try:
-        # Replace with your Hugging Face repo details
-        repo_id = "AdeshJain/plant-detection"  # e.g., "john/plant-disease-classifier"
+        repo_id = "AdeshJain/plant-detection"
         filename = "plant_disease_model.keras"
         
-        # Download model from Hugging Face Hub
         model_path = hf_hub_download(
             repo_id=repo_id,
             filename=filename,
             cache_dir="./model_cache"
         )
         
-        # Load the model
         model = keras.models.load_model(model_path)
-
         print(f"Model loaded successfully from {repo_id}!")
     except Exception as e:
         print(f"Error loading model: {e}")
@@ -70,24 +71,112 @@ async def load_model_keras():
 
 def preprocess_image(image: Image.Image) -> np.ndarray:
     """Preprocess the image for model prediction"""
-    # Convert to RGB if needed
     if image.mode != 'RGB':
         image = image.convert('RGB')
     
-    # Resize image
     image = image.resize(IMG_SIZE)
-    
-    # Convert to numpy array
     img_array = np.array(image)
-    
-    # Normalize pixel values to [0, 1]
     img_array = img_array.astype('float32') / 255.0
-    
-    # Add batch dimension
     img_array = np.expand_dims(img_array, axis=0)
     
     return img_array
 
+async def get_weather_data(lat: float, lon: float) -> Dict:
+    """Fetch weather data from OpenWeather API"""
+    url = f"https://api.openweathermap.org/data/2.5/weather?lat={lat}&lon={lon}&appid={WEATHER_API_KEY}&units=metric"
+    
+    try:
+        async with httpx.AsyncClient() as client_http:
+            response = await client_http.get(url)
+            if response.status_code != 200:
+                return None
+            return response.json()
+    except Exception as e:
+        print(f"Weather API error: {e}")
+        return None
+
+def get_llm_remedies(disease: str, weather_data: Optional[Dict], location: str) -> Dict:
+    """Get disease remedies from Groq LLM"""
+    
+    # Prepare weather context
+    weather_context = ""
+    if weather_data:
+        temp = weather_data['main']['temp']
+        humidity = weather_data['main']['humidity']
+        weather_desc = weather_data['weather'][0]['description']
+        weather_context = f"""
+Current Weather Conditions at {location}:
+- Temperature: {temp}°C
+- Humidity: {humidity}%
+- Conditions: {weather_desc}
+"""
+    else:
+        weather_context = f"Location: {location}\n(Weather data unavailable)"
+    
+    prompt = f"""You are an expert agricultural consultant specializing in plant disease management. A farmer has detected the following plant disease through image analysis:
+
+**Detected Disease: {disease}**
+
+{weather_context}
+
+Please provide comprehensive treatment recommendations in the following structured format:
+
+## 1. CHEMICAL TREATMENT METHODS
+Provide specific chemical treatments including:
+- Recommended fungicides/pesticides (with active ingredients)
+- Application dosage and concentration
+- Application frequency and timing
+- Safety precautions and protective equipment needed
+- Pre-harvest intervals if applicable
+
+## 2. SUSTAINABLE & NATURAL TREATMENT METHODS
+Provide organic and eco-friendly solutions including:
+- Natural/organic sprays and remedies
+- Biological control methods
+- Cultural practices and preventive measures
+- Soil management techniques
+- Plant-based solutions
+
+## 3. WEATHER-SPECIFIC RECOMMENDATIONS
+Based on the current weather conditions:
+- How weather affects disease progression
+- Best time to apply treatments
+- Additional precautions needed
+- Environmental considerations
+
+## 4. PREVENTIVE MEASURES
+Long-term strategies to prevent recurrence:
+- Crop rotation suggestions
+- Irrigation management
+- Nutrient management
+- Monitoring practices
+
+Please be specific, practical, and actionable. Consider both immediate treatment and long-term disease management."""
+
+    try:
+        chat_completion = client.chat.completions.create(
+            messages=[
+                {
+                    "role": "system",
+                    "content": "You are an expert agricultural consultant with deep knowledge of plant pathology and sustainable farming practices. Provide detailed, practical advice for farmers."
+                },
+                {
+                    "role": "user",
+                    "content": prompt
+                }
+            ],
+            model="llama-3.3-70b-versatile",
+            temperature=0.7,
+            max_tokens=500
+        )
+        
+        return {
+            "remedies": chat_completion.choices[0].message.content,
+            "model_used": "llama-3.3-70b-versatile"
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"LLM error: {str(e)}")
+
 @app.get("/")
 async def root():
     """Health check endpoint"""
@@ -97,39 +186,68 @@ async def root():
         "model_loaded": model is not None
     }
 
-@app.get("/test")
-async def test_prediction():
-    """
-    Test endpoint using a hardcoded image from local directory
-    Place a test image named 'test_image.jpg' in the same directory as main.py
-    """
+# @app.get("/test")
+# async def test_prediction():
+#     """Test endpoint using a hardcoded image from local directory"""
+#     if model is None:
+#         raise HTTPException(status_code=500, detail="Model not loaded")
+    
+#     test_image_path = "test2.jpg"
+    
+#     if not os.path.exists(test_image_path):
+#         raise HTTPException(
+#             status_code=404, 
+#             detail=f"Test image not found at {test_image_path}. Please place a test image in the directory."
+#         )
+    
+#     try:
+#         image = Image.open(test_image_path)
+#         processed_img = preprocess_image(image)
+#         prediction = model.predict(processed_img)
+        
+#         predicted_class_idx = int(np.argmax(prediction, axis=1)[0])
+#         confidence = float(np.max(prediction))
+#         predicted_class_name = CLASS_LABELS[predicted_class_idx]
+
+#         top_5_indices = np.argsort(prediction[0])[-5:][::-1]
+#         top_5_predictions = [
+#             {
+#                 "class": CLASS_LABELS[idx],
+#                 "confidence": float(prediction[0][idx])
+#             }
+#             for idx in top_5_indices
+#         ]
+#         print("Top-5 indices & confidences:", top_5_indices)
+
+#         return {
+#             "top_prediction": {
+#                 "class": predicted_class_name,
+#                 "confidence": confidence
+#             }
+#         }
+
+#     except Exception as e:
+#         raise HTTPException(status_code=500, detail=f"Test prediction error: {str(e)}")
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)) -> Dict:
+    """Predict image class using the loaded CNN model"""
     if model is None:
         raise HTTPException(status_code=500, detail="Model not loaded")
     
-    # Path to test image (change this to your test image path)
-    test_image_path = "test2.jpg"
-    
-    if not os.path.exists(test_image_path):
-        raise HTTPException(
-            status_code=404, 
-            detail=f"Test image not found at {test_image_path}. Please place a test image in the directory."
-        )
+    if not file.content_type.startswith('image/'):
+        raise HTTPException(status_code=400, detail="File must be an image")
     
     try:
-        # Load and process the test image
-        image = Image.open(test_image_path)
+        contents = await file.read()
+        image = Image.open(io.BytesIO(contents))
         processed_img = preprocess_image(image)
-        
-        # Make prediction
         prediction = model.predict(processed_img)
         
-        # Get predicted class and confidence
-    # Get top-1 prediction
         predicted_class_idx = int(np.argmax(prediction, axis=1)[0])
         confidence = float(np.max(prediction))
         predicted_class_name = CLASS_LABELS[predicted_class_idx]
-
-        # Get top-5 predictions
+        
         top_5_indices = np.argsort(prediction[0])[-5:][::-1]
         top_5_predictions = [
             {
@@ -138,79 +256,118 @@ async def test_prediction():
             }
             for idx in top_5_indices
         ]
-        print("Top-5 indices & confidences:",top_5_indices
-      )
-
-        # ✅ Return or print all results
+        
         return {
-            "top_prediction": {
-                "class": predicted_class_name,
-                "confidence": confidence
-            }
-            # "top_5_predictions": top_5_predictions
+            "success": True,
+            "predicted_class": predicted_class_name,
+            "predicted_class_index": predicted_class_idx,
+            "confidence": confidence,
+            "top_5_predictions": top_5_predictions,
+            "filename": file.filename
         }
-
+        
     except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Test prediction error: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}")
 
-@app.post("/predict")
-async def predict(file: UploadFile = File(...)) -> Dict:
+@app.post("/predict-with-remedies")
+async def predict_with_remedies(
+    file: UploadFile = File(...),
+    latitude: float = Form(...),
+    longitude: float = Form(...),
+    city: Optional[str] = Form(None)
+) -> Dict:
     """
-    Predict image class using the loaded CNN model
+    Predict plant disease and provide AI-generated remedies with weather context
     
     Args:
-        file: Uploaded image file
+        file: Uploaded plant image
+        latitude: Location latitude for weather data
+        longitude: Location longitude for weather data
+        city: Optional city name for display
         
     Returns:
-        Dictionary containing prediction results
+        Dictionary containing prediction, weather data, and treatment recommendations
     """
     if model is None:
         raise HTTPException(status_code=500, detail="Model not loaded")
     
-    # Validate file type
     if not file.content_type.startswith('image/'):
         raise HTTPException(status_code=400, detail="File must be an image")
     
     try:
-        # Read image file
+        # Step 1: Make disease prediction using CNN model
         contents = await file.read()
         image = Image.open(io.BytesIO(contents))
-        
-        # Preprocess image
         processed_img = preprocess_image(image)
-        
-        # Make prediction
         prediction = model.predict(processed_img)
         
-        # Get predicted class and confidence
         predicted_class_idx = int(np.argmax(prediction, axis=1)[0])
         confidence = float(np.max(prediction))
         predicted_class_name = CLASS_LABELS[predicted_class_idx]
         
-        # Get top 5 predictions
-        top_5_indices = np.argsort(prediction[0])[-5:][::-1]
-        top_5_predictions = [
+        # Get top 3 predictions for additional context
+        top_3_indices = np.argsort(prediction[0])[-3:][::-1]
+        top_3_predictions = [
             {
                 "class": CLASS_LABELS[idx],
                 "confidence": float(prediction[0][idx])
             }
-            for idx in top_5_indices
+            for idx in top_3_indices
         ]
         
+        # Step 2: Fetch weather data for the location
+        location_name = city if city else f"Lat: {latitude}, Lon: {longitude}"
+        weather_data = await get_weather_data(latitude, longitude)
+        
+        # Step 3: Get LLM-generated remedies
+        # Only generate remedies if disease detected (not healthy)
+        remedies_data = None
+        if "Healthy" not in predicted_class_name:
+            remedies_data = get_llm_remedies(predicted_class_name, weather_data, location_name)
+        else:
+            remedies_data = {
+                "remedies": "🎉 Great news! Your plant appears to be healthy. No treatment needed.\n\n**Preventive Care Tips:**\n- Continue regular monitoring\n- Maintain proper watering schedule\n- Ensure adequate sunlight\n- Keep the area clean and weed-free\n- Monitor for any changes in plant appearance",
+                "model_used": "rule-based"
+            }
+        
+        # Prepare weather info for response
+        weather_info = None
+        if weather_data:
+            weather_info = {
+                "temperature": weather_data['main']['temp'],
+                "feels_like": weather_data['main']['feels_like'],
+                "humidity": weather_data['main']['humidity'],
+                "pressure": weather_data['main']['pressure'],
+                "conditions": weather_data['weather'][0]['description'],
+                "wind_speed": weather_data['wind']['speed']
+            }
+        
         return {
             "success": True,
-            "predicted_class": predicted_class_name,
-            "predicted_class_index": predicted_class_idx,
-            "confidence": confidence,
-            "top_5_predictions": top_5_predictions,
+            "prediction": {
+                "disease": predicted_class_name,
+                "confidence": confidence,
+                "is_healthy": "Healthy" in predicted_class_name,
+                "top_3_predictions": top_3_predictions
+            },
+            "location": {
+                "name": location_name,
+                "latitude": latitude,
+                "longitude": longitude
+            },
+            "weather": weather_info,
+            "treatment": {
+                "remedies": remedies_data["remedies"],
+                "llm_model": remedies_data["model_used"]
+            },
             "filename": file.filename
         }
         
+    except HTTPException:
+        raise
     except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}")
-
-
+        raise HTTPException(status_code=500, detail=f"Error processing request: {str(e)}")
 
 if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=7860)
-    
+