Create app.py

app.py

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+from tensorflow.keras.models import load_model
+from fastapi import FastAPI, UploadFile, File
+import numpy as np
+from PIL import Image
+import io
+
+app = FastAPI()
+
+# Load the model with custom mse
+model = load_model("super_resolution_model.h5", custom_objects={"mse": "mse"})
+
+def preprocess_image(image: Image.Image):
+    # Adjust based on your model's input requirements
+    # Example: Resize to 64x64, normalize to [0, 1]
+    image = image.resize((64, 64))  # Replace with your model's input size
+    image_array = np.array(image) / 255.0
+    return np.expand_dims(image_array, axis=0)
+
+def postprocess_image(output_array: np.ndarray):
+    # Adjust based on your model's output
+    # Example: Clip values, convert to uint8
+    output_array = np.clip(output_array[0] * 255.0, 0, 255).astype("uint8")
+    return Image.fromarray(output_array)
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    # Read and preprocess image
+    image = Image.open(io.BytesIO(await file.read())).convert("RGB")
+    input_array = preprocess_image(image)
+    
+    # Run inference
+    high_res = model.predict(input_array)
+    
+    # Postprocess output
+    high_res_image = postprocess_image(high_res)
+    
+    # Save to bytes
+    output = io.BytesIO()
+    high_res_image.save(output, format="PNG")
+    output.seek(0)
+    
+    return {"image": output.getvalue()}
+
+@app.get("/")
+def root():
+    return {"message": "Super-resolution API"}