app.py

from fastapi import FastAPI, File, UploadFile, Request
import tensorflow as tf
import numpy as np
from PIL import Image
import cv2
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
import logging

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Load your trained model
model = tf.keras.models.load_model('recyclebot.keras')




# Define class names for predictions (this should be the same as in your local code)
CLASSES = ['Glass', 'Metal', 'Paperboard', 'Plastic-Polystyrene', 'Plastic-Regular']

# Create FastAPI app
app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Allow all origins (or specify specific origins)
    allow_credentials=True,
    allow_methods=["*"],  # Allow all HTTP methods
    allow_headers=["*"],  # Allow all headers
)

# Preprocess the image (resize, reshape without normalization)
def preprocess_image(image_file):
    try:
        # Load image using PIL
        image = Image.open(image_file)
        
        # Convert image to numpy array
        image = np.array(image)
        
        # Resize to the input shape expected by the model
        image = cv2.resize(image, (240, 240))  # Resize image to match model input
        
        # Reshape the image (similar to your local code)
        image = image.reshape(-1, 240, 240, 3)  # Add the batch dimension for inference
        
        return image
    except Exception as e:
        logger.error(f"Error in preprocess_image: {str(e)}")
        raise

# Background removal function

@app.post("/predict")
async def predict(file: UploadFile = File(...)):
    try:
        logger.info("Received request for /predict")
        img_array = preprocess_image(file.file)  # Preprocess the image
        prediction1 = model.predict(img_array)  # Get predictions
        
        predicted_class_idx = np.argmax(prediction1, axis=1)[0]  # Get predicted class index
        predicted_class = CLASSES[predicted_class_idx]  # Convert to class name
        
        return JSONResponse(content={"prediction": predicted_class})
    
    except Exception as e:
        logger.error(f"Error in /predict: {str(e)}")
        return JSONResponse(content={"error": str(e)}, status_code=400)


@app.get("/working")
async def working():
    return JSONResponse(content={"Status": "Working"})

# To manually run FastAPI
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)