debugged the vercel, render, and huggingface connection

app.py

@@ -1,16 +1,19 @@
-# Final, API-only app.py for Hugging Face Space
+# Final, Complete, and Working app.py for Hugging Face Space
 
 import os
 import cv2
 import tempfile
 import numpy as np
 import uvicorn
+import requests
+import io
 from PIL import Image
 from inference_sdk import InferenceHTTPClient
 from fastapi import FastAPI, File, UploadFile
 from fastapi.responses import JSONResponse
 import tensorflow as tf
 from huggingface_hub import hf_hub_download
+import gradio as gr
 
 # --- 1. Configuration and Model Loading ---
 ROBOFLOW_API_KEY = os.environ.get("ROBOFLOW_API_KEY")
@@ -39,10 +42,8 @@ def detect_eyes_roboflow(image_path, raw_image):
     resp = CLIENT_EYES.infer(image_path, model_id="eye-detection-kso3d/3")
     crops = []
     for p in resp.get("predictions", []):
-        x1 = int(p['x'] - p['width'] / 2)
-        y1 = int(p['y'] - p['height'] / 2)
-        x2 = int(p['x'] + p['width'] / 2)
-        y2 = int(p['y'] + p['height'] / 2)
+        x1, y1 = int(p['x'] - p['width'] / 2), int(p['y'] - p['height'] / 2)
+        x2, y2 = int(p['x'] + p['width'] / 2), int(p['y'] + p['height'] / 2)
         crop = raw_image[y1:y2, x1:x2]
         if crop.size > 0:
             crops.append(crop)
@@ -57,30 +58,19 @@ def get_largest_iris_prediction(eye_crop):
 
 def run_leukocoria_prediction(iris_crop):
     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))
     enh_rs = cv2.resize(enh, (224, 224))
     img_array = np.array(enh_rs) / 255.0
     img_array = np.expand_dims(img_array, axis=0)
-    
     prediction = leuko_model.predict(img_array)
     confidence = float(prediction[0][0])
     has_leuko = confidence > 0.5
-    
     return has_leuko, confidence
 
 # --- 3. FastAPI Application ---
 app = FastAPI()
 
-# --- remove display error ---
-@app.get("/")
-def read_root():
-    return {
-        "status": "LeukoLook API is running",
-        "documentation": "Send a POST request to the /detect/ endpoint with an image file."
-    }
-
 @app.post("/detect/")
 async def full_detection_pipeline(image: UploadFile = File(...)):
     with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
@@ -118,6 +108,33 @@ async def full_detection_pipeline(image: UploadFile = File(...)):
     finally:
         os.remove(temp_image_path)
 
-# --- 4. Run the Server ---
+# --- 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 ---
 if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=7860)