Create app.py

app.py

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+import fastapi
+from transformers import pipeline
+import pickle
+
+# Load the model from the pickle file
+with open("model_finetuned.pkl", "rb") as f:
+    model = pickle.load(f)
+
+# Define a function to preprocess the image
+def preprocess_image(image):
+    # Resize the image to a fixed size
+    image = image.resize((224, 224))
+
+    # Convert the image to a NumPy array
+    image = np.array(image)
+
+    # Normalize the image
+    image = image / 255.0
+
+    # Return the image
+    return image
+
+# Define an endpoint to predict the output
+@app.post("/predict")
+async def predict_endpoint(image: fastapi.File):
+    # Preprocess the image
+    image = preprocess_image(image)
+
+    # Make a prediction
+    prediction = model(image)
+
+    # Return the prediction
+    return {"prediction": prediction}
+
+# Start the FastAPI app
+if _name_ == "_main_":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=8000)