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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)
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