from fastapi import FastAPI, File, UploadFile
import uvicorn
import numpy as np
import tensorflow as tf
from PIL import Image
import io

# Load the Keras model (assuming model.h5 is in the same directory)
model = tf.keras.models.load_model("deepfake_model_best.h5")

app = FastAPI()

# Preprocessing function
def preprocess_image(image: Image.Image):
    image = image.resize((224, 224))  # Resize to model's expected input size
    image = np.array(image) / 255.0  # Normalize pixel values
    image = np.expand_dims(image, axis=0)  # Add batch dimension
    return image

@app.post("/predict")
async def predict(file: UploadFile = File(...)):
    try:
        # Read image file
        contents = await file.read()
        image = Image.open(io.BytesIO(contents)).convert("RGB")
        
        # Preprocess image
        image = preprocess_image(image)
        
        # Make prediction
        prediction = model.predict(image)
        
        # Return result
        return {"prediction": prediction.tolist()}
    except Exception as e:
        return {"error": str(e)}

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