Upload 4 files

app.py

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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)

deepfake_model_best.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:190e9b2ec24a7c0945ce2e695f74331939c39bba9979404b29e563741f2062af
+size 23186048

dockerfile.txt

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+FROM python:3.9
+
+WORKDIR /app
+COPY . /app
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

requirements.txt

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+fastapi
+torch
+torchvision
+opencv-python
+pillow
+uvicorn