removed the Gradio UI

app.py

@@ -1,18 +1,14 @@
-# Final, Complete, and Corrected app.py for Hugging Face Space
+# Final, API-only app.py for Hugging Face Space
 
 import os
 import cv2
 import tempfile
 import numpy as np
 import uvicorn
-import requests
-import base64
-import io
 from PIL import Image
 from inference_sdk import InferenceHTTPClient
 from fastapi import FastAPI, File, UploadFile
 from fastapi.responses import JSONResponse
-import gradio as gr
 import tensorflow as tf
 from huggingface_hub import hf_hub_download
 
@@ -52,7 +48,6 @@ def detect_eyes_roboflow(image_path, raw_image):
             crops.append(crop)
     return crops
 
-# --- ADDED MISSING FUNCTION ---
 def get_largest_iris_prediction(eye_crop):
     is_success, buffer = cv2.imencode(".jpg", eye_crop)
     if not is_success: return None
@@ -61,7 +56,7 @@ def get_largest_iris_prediction(eye_crop):
     return max(preds, key=lambda p: p["width"] * p["height"]) if preds else None
 
 def run_leukocoria_prediction(iris_crop):
-    if leuko_model is None: return {"error": "Leukocoria model not loaded"}
+    if leuko_model is None: return {"error": "Leukocoria model not loaded"}, 0.0
     
     img_pil = Image.fromarray(cv2.cvtColor(iris_crop, cv2.COLOR_BGR2RGB))
     enh = enhance_image_unsharp_mask(np.array(img_pil))
@@ -115,33 +110,6 @@ async def full_detection_pipeline(image: UploadFile = File(...)):
     finally:
         os.remove(temp_image_path)
 
-# --- 4. Create and Mount the Gradio UI for a professional homepage ---
-def gradio_wrapper(image_array):
-    """A wrapper function to call our own FastAPI endpoint from the Gradio UI."""
-    try:
-        pil_image = Image.fromarray(image_array)
-        with io.BytesIO() as buffer:
-            pil_image.save(buffer, format="JPEG")
-            files = {'image': ('image.jpg', buffer.getvalue(), 'image/jpeg')}
-            response = requests.post("http://127.0.0.1:7860/detect/", files=files)
-        
-        if response.status_code == 200:
-            return response.json()
-        else:
-            return {"error": f"API Error {response.status_code}", "details": response.text}
-    except Exception as e:
-        return {"error": str(e)}
-
-gradio_ui = gr.Interface(
-    fn=gradio_wrapper,
-    inputs=gr.Image(type="numpy", label="Upload an eye image to test the full pipeline"),
-    outputs=gr.JSON(label="Analysis Results"),
-    title="LeukoLook Eye Detector",
-    description="A demonstration of the LeukoLook detection model pipeline."
-)
-
-app = gr.mount_gradio_app(app, gradio_ui, path="/")
-
-# --- 5. Run the server ---
+# --- 4. Run the Server ---
 if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=7860)