Update app.py

app.py

@@ -39,6 +39,33 @@ class PredictionOutput(BaseModel):
     t3: List[float]
     prediction: List[float]
 
+# Define the inference function
+def inference_on_file(file_path, model, custom_test_pipeline):
+    with rasterio.open(file_path) as src:
+        img = src.read()
+
+    # Apply preprocessing using the custom pipeline
+    processed_img = apply_pipeline(custom_test_pipeline, img)
+    
+    # Run inference
+    output = model.inference(processed_img)
+    
+    # Post-process the output to get the RGB and prediction images
+    rgb1 = postprocess_output(output[0])
+    rgb2 = postprocess_output(output[1])
+    rgb3 = postprocess_output(output[2])
+    
+    return rgb1, rgb2, rgb3, output
+
+def apply_pipeline(pipeline, img):
+    # Implement your custom pipeline processing here
+    # This could include normalization, resizing, etc.
+    return img
+
+def postprocess_output(output):
+    # Convert the model's output into an RGB image or other formats as needed
+    return output
+
 @app.post("/predict/", response_model=PredictionOutput)
 async def predict(file: UploadFile = File(...)):
     # Read the uploaded file