updated with UI

app.py

@@ -1,4 +1,4 @@
-# The Final app.py using a manual FastAPI endpoint
+# The Complete and Final app.py with both a UI and an API
 
 from fastapi import FastAPI
 from fastapi.responses import JSONResponse
@@ -12,7 +12,7 @@ import os
 import base64
 import io
 
-# --- 1. Load the Model (Stays the same) ---
+# --- 1. Load the Model ---
 model = None
 try:
     model_path = hf_hub_download(
@@ -23,9 +23,9 @@ try:
     print("--- MODEL LOADED SUCCESSFULLY! ---")
 except Exception as e:
     print(f"--- ERROR LOADING MODEL: {e} ---")
-    model = None # Ensure model is None if loading fails
+    model = None
 
-# --- 2. Prediction Logic (Stays the same) ---
+# --- 2. Core Prediction Logic ---
 def preprocess_image(img_pil):
     img = img_pil.resize((224, 224))
     img_array = np.array(img)
@@ -38,7 +38,7 @@ def preprocess_image(img_pil):
 def run_prediction(pil_image):
     if model is None:
         return {"error": "Model is not loaded on the server."}
-
+    
     processed_image = preprocess_image(pil_image)
     prediction = model.predict(processed_image)
     labels = [f"Class_{i}" for i in range(prediction.shape[1])]
@@ -48,30 +48,30 @@ def run_prediction(pil_image):
 # --- 3. Create the FastAPI app ---
 app = FastAPI()
 
-# --- 4. Define the input data structure for our new endpoint ---
+# --- 4. Define the input data structure for our API endpoint ---
 class PredictionRequest(BaseModel):
     data: list[str]
 
-# --- 5. Create our own reliable API endpoint ---
+# --- 5. Create our reliable API endpoint for the Render backend ---
 @app.post("/api/predict/")
-async def handle_prediction(request: PredictionRequest):
+async def handle_api_prediction(request: PredictionRequest):
     try:
-        # Get the Base64 string from the JSON payload
         base64_string = request.data[0].split(',', 1)[1]
         image_bytes = base64.b64decode(base64_string)
         pil_image = Image.open(io.BytesIO(image_bytes))
-
-        # Run the prediction
         result_dict = run_prediction(pil_image)
-
-        # Return the result in the same format Gradio does
         return JSONResponse(content={"data": [result_dict]})
-
     except Exception as e:
         return JSONResponse(status_code=500, content={"error": str(e)})
 
-import uvicorn
+# --- 6. Create the Gradio UI for the homepage ---
+gradio_ui = gr.Interface(
+    fn=run_prediction,
+    inputs=gr.Image(type="pil", label="Upload an eye image to test"),
+    outputs=gr.JSON(label="Prediction Results"),
+    title="LeukoLook Eye Detector",
+    description="A demonstration of the LeukoLook detection model. This UI can be used for direct testing."
+)
 
-if __name__ == "__main__":
-    # The port number (7860) is the standard port for Hugging Face Spaces
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+# --- 7. Mount the Gradio UI onto the FastAPI app's root ---
+app = gr.mount_gradio_app(app, gradio_ui, path="/")