from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.middleware.cors import CORSMiddleware
import tensorflow as tf
import numpy as np
from PIL import Image
import io
import uvicorn
import tempfile
import cv2


# Initialize FastAPI app
app = FastAPI(title="Plant Disease Detection API", version="1.0.0")

# Add CORS middleware to allow requests from your frontend
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # In production, replace with your frontend URL
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Load your model
try:
    model = tf.keras.models.load_model("trained_modela.keras")
except Exception as e:
    raise RuntimeError(f"Failed to load model: {e}")

# Define your class names (update with your actual classes)
class_name = ['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',
    'Corn_(maize)___healthy',
    'Grape___Black_rot',
    'Grape___Esca_(Black_Measles)',
    'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)',
    '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',
    'Tomato___Bacterial_spot',
    'Tomato___Early_blight',
    'Tomato___Late_blight',
    '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.get("/")
async def root():
    print("dfhkjfdshu")
    return {"message": "Plant Disease Detection API", "version": "1.0.0"}

@app.post("/predict")
async def predict_disease(file: UploadFile = File(...)):
    
    if not file.content_type.startswith('image/'):
            raise HTTPException(status_code=400, detail="File must be an image")

    try:
        # Validate file type
        # Validate file type
        
        # Save uploaded file temporarily
        with tempfile.NamedTemporaryFile(suffix=".jpeg", delete=False) as tmp:
            temp_path = tmp.name
            tmp.write(await file.read())
            tmp.flush()  # Ensure data is written
        
        # Read image using OpenCV
        # img = cv2.imread(temp_path)
        # if img is None:
        #     raise HTTPException(status_code=400, detail="Invalid image file")
        # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        
        image = tf.keras.preprocessing.image.load_img(temp_path,target_size=(128, 128))
        
        input_arr = tf.keras.preprocessing.image.img_to_array(image)
        input_arr = np.array([input_arr])  # Convert single image to batch

        # Predict
        prediction = model.predict(input_arr)
        result_index = np.argmax(prediction)
        confidence = float(prediction[0][result_index])
        disease_name = class_name[result_index]

        

        return {
            "success": True,
            "disease": disease_name,
            "confidence": confidence
        }

    
    
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}")

@app.get("/health")
async def health_check():
    return {"status": "healthy"}

@app.get("/classes")
async def get_classes():
    """Get all available disease classes"""
    return {"classes": class_name}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)