Initial commit with large models

.gitattributes

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+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text

Dockerfile

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+# Use Python 3.9
+FROM python:3.9
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements and install
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application
+COPY . .
+
+# Create a non-root user (Security requirement for HF Spaces)
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+# Expose the port (Hugging Face expects port 7860)
+EXPOSE 7860
+
+# Command to run the app
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+import os
+import json
+import uvicorn
+import joblib
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+import google.generativeai as genai
+from io import BytesIO
+from PIL import Image
+from fastapi import FastAPI, File, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from dotenv import load_dotenv
+
+# --- CONFIGURATION & SETUP ---
+load_dotenv() # Loads environment variables from .env file
+
+app = FastAPI()
+
+# CORS Setup
+origins = [
+    "http://localhost:3000",
+    "https://LeafCompass.onrender.com"
+]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# --- LOAD MODELS ---
+print("--- Starting Server & Loading Models ---")
+
+# Global variables for models
+disease_model = None
+yield_model = None
+crop_model = None
+fertilizer_model = None
+class_names = {}
+
+# 1. Load Plant Disease Model
+try:
+    disease_model = tf.keras.models.load_model("./models/plant_disease_prediction_model.h5")
+    with open("./models/class_indices.json", "r") as f:
+        class_indices = json.load(f)
+    # Map indices to names
+    class_names = {int(k): v for k, v in class_indices.items()}
+    print("✅ Disease Model Loaded.")
+except Exception as e:
+    print(f"❌ Error loading disease model: {e}")
+
+# 2. Load Yield Prediction Model
+try:
+    yield_model = joblib.load("./models/yield_prediction_model.pkl")
+    print("✅ Yield Model Loaded.")
+except Exception as e:
+    print(f"❌ Error loading yield model: {e}")
+
+# 3. Load Crop Recommendation Model
+try:
+    crop_model = joblib.load("./models/crop_recommendation_model.pkl")
+    print("✅ Crop Model Loaded.")
+except Exception as e:
+    print(f"❌ Error loading crop model: {e}")
+
+# 4. Load Fertilizer Recommendation Model
+try:
+    fertilizer_model = joblib.load("./models/fertilizer_recommendation_model.pkl")
+    print("✅ Fertilizer Model Loaded.")
+except Exception as e:
+    print(f"❌ Error loading fertilizer model: {e}")
+
+# 5. Configure Gemini AI
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+
+if GEMINI_API_KEY:
+    genai.configure(api_key=GEMINI_API_KEY)
+    try:
+        # Using standard flash model
+        model = genai.GenerativeModel(
+            model_name="gemini-1.5-flash", 
+            system_instruction="""
+            You are AgroBot, an intelligent agricultural assistant integrated into the 'AgroAI' web application.
+            Your capabilities:
+            1. Diagnose plant diseases based on symptoms described by the user.
+            2. Explain crop yield predictions.
+            3. Recommend fertilizers for specific soil types.
+            4. Suggest crops based on NPK values and climate.
+            Guidelines:
+            - Keep answers concise (under 3-4 sentences).
+            - Use emojis (🌾, 🚜, 🍃).
+            - If asked about app features, guide them: Disease -> 'Disease' tab, Yield -> 'Yield' tab.
+            """
+        )
+        chat_session = model.start_chat(history=[])
+        print("✅ Gemini AI Connected.")
+    except Exception as e:
+        print(f"❌ Error configuring Gemini: {e}")
+        chat_session = None
+else:
+    print("⚠️  Warning: GEMINI_API_KEY not found in .env file.")
+    chat_session = None
+
+
+# --- DATA STRUCTURES (Pydantic Models) ---
+
+class YieldInput(BaseModel):
+    Rainfall_mm: float
+    Temperature_Celsius: float
+    Days_to_Harvest: int
+    Region: str
+    Soil_Type: str
+    Crop: str
+    Weather_Condition: str
+    Fertilizer_Used: bool
+    Irrigation_Used: bool
+
+class CropInput(BaseModel):
+    N: float
+    P: float
+    K: float
+    temperature: float
+    humidity: float
+    ph: float
+    rainfall: float
+    state: str 
+
+class FertilizerInput(BaseModel):
+    Temperature: float
+    Humidity: float
+    Moisture: float
+    Soil_Type: str 
+    Crop_Type: str
+    Nitrogen: float
+    Potassium: float
+    Phosphorous: float
+
+class ChatInput(BaseModel):
+    message: str
+
+
+# --- ENDPOINTS ---
+
+@app.get("/")
+def ping():
+    return {"message": "AgroAI Server is running 🚀"}
+
+@app.post("/predict-disease")
+async def predict_disease(file: UploadFile = File(...)):
+    if not disease_model:
+        return {"error": "Disease model is not loaded."}
+    
+    try:
+        # Process Image
+        image_data = await file.read()
+        image = Image.open(BytesIO(image_data))
+        image = image.resize((224, 224))
+        image = np.array(image).astype('float32') / 255.0
+        img_batch = np.expand_dims(image, 0)
+        
+        # Predict
+        predictions = disease_model.predict(img_batch)
+        predicted_index = np.argmax(predictions[0])
+        confidence = float(np.max(predictions[0]))
+        
+        return {
+            "class": class_names.get(predicted_index, "Unknown"),
+            "confidence": confidence
+        }
+    except Exception as e:
+        return {"error": str(e)}
+
+@app.post("/predict-yield")
+def predict_yield(data: YieldInput):
+    if not yield_model:
+        return {"error": "Yield model is not loaded."}
+
+    input_data = pd.DataFrame([data.dict()])
+    
+    # Ensure categorical variables (Region, Soil, Crop) are handled 
+    # if your model pipeline expects them encoded, ensure input_data is processed here.
+    
+    prediction = yield_model.predict(input_data)
+    return {"predicted_yield": float(prediction[0])}
+
+@app.post("/recommend-crop")
+def recommend_crop(data: CropInput):
+    if not crop_model:
+        return {"error": "Crop model is not loaded."}
+    
+    # Note: Ensure 'data.state' is encoded if your model expects a number!
+    features = pd.DataFrame([[
+        data.N, data.P, data.K, 
+        data.temperature, data.humidity, data.ph, 
+        data.rainfall, data.state
+    ]], columns=['N_SOIL', 'P_SOIL', 'K_SOIL', 'TEMPERATURE', 'HUMIDITY', 'ph', 'RAINFALL', 'STATE'])
+    
+    prediction = crop_model.predict(features)
+    return {"recommended_crop": prediction[0]}
+
+@app.post("/recommend-fertilizer")
+def recommend_fertilizer(data: FertilizerInput):
+    if not fertilizer_model:
+        return {"error": "Fertilizer model is not loaded."}
+
+    input_df = pd.DataFrame([[
+        data.Temperature, data.Humidity, data.Moisture, 
+        data.Soil_Type, data.Crop_Type, 
+        data.Nitrogen, data.Potassium, data.Phosphorous
+    ]], columns=[
+        'Temperature', 'Humidity', 'Moisture', 'Soil_Type', 
+        'Crop_Type', 'Nitrogen', 'Potassium', 'Phosphorous'
+    ])
+    
+    prediction = fertilizer_model.predict(input_df)
+    return {"recommended_fertilizer": prediction[0]}
+
+@app.post("/chat")
+def chat_endpoint(data: ChatInput):
+    if not chat_session:
+        return {"response": "AI service is currently unavailable. Check API Key."}
+    
+    try:
+        response = chat_session.send_message(data.message)
+        return {"response": response.text}
+    except Exception as e:
+        print(f"Gemini Error: {e}")
+        return {"response": "I'm having trouble connecting to the network. 📡 Try again later!"}
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="localhost", port=8000)

models/class_indices.json

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+{
+  "0": "Apple___Apple_scab",
+  "1": "Apple___Black_rot",
+  "2": "Apple___Cedar_apple_rust",
+  "3": "Apple___healthy",
+  "4": "Blueberry___healthy",
+  "5": "Cherry_(including_sour)___Powdery_mildew",
+  "6": "Cherry_(including_sour)___healthy",
+  "7": "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot",
+  "8": "Corn_(maize)___Common_rust_",
+  "9": "Corn_(maize)___Northern_Leaf_Blight",
+  "10": "Corn_(maize)___healthy",
+  "11": "Grape___Black_rot",
+  "12": "Grape___Esca_(Black_Measles)",
+  "13": "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)",
+  "14": "Grape___healthy",
+  "15": "Orange___Haunglongbing_(Citrus_greening)",
+  "16": "Peach___Bacterial_spot",
+  "17": "Peach___healthy",
+  "18": "Pepper,_bell___Bacterial_spot",
+  "19": "Pepper,_bell___healthy",
+  "20": "Potato___Early_blight",
+  "21": "Potato___Late_blight",
+  "22": "Potato___healthy",
+  "23": "Raspberry___healthy",
+  "24": "Soybean___healthy",
+  "25": "Squash___Powdery_mildew",
+  "26": "Strawberry___Leaf_scorch",
+  "27": "Strawberry___healthy",
+  "28": "Tomato___Bacterial_spot",
+  "29": "Tomato___Early_blight",
+  "30": "Tomato___Late_blight",
+  "31": "Tomato___Leaf_Mold",
+  "32": "Tomato___Septoria_leaf_spot",
+  "33": "Tomato___Spider_mites Two-spotted_spider_mite",
+  "34": "Tomato___Target_Spot",
+  "35": "Tomato___Tomato_Yellow_Leaf_Curl_Virus",
+  "36": "Tomato___Tomato_mosaic_virus",
+  "37": "Tomato___healthy"
+}

models/crop_recommendation_model.pkl

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+oid sha256:d3443bd71057465e5c3f5e5fd2d80fabba589fe595a41a8eb822fa679c6acc66
+size 4005122

models/fertilizer_recommendation_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a1d55618b485c6a98b0e5e69abd59f9f6d7a4a305567b3c148a96d1f18e271d9
+size 1455442

models/plant_disease_prediction_model.h5

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+oid sha256:bc931f8b2c783adde26ab96f3ab2aea620bf6020794562642a6afcf0d52fdb0d
+size 573701104

models/yield_prediction_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4ac881356666d9ffa79857b79b2b0be767c16d662a2f2ab779060f4de224ff71
+size 418501402