debugged

app.py

@@ -5,6 +5,9 @@ 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
@@ -90,8 +93,11 @@ async def full_detection_pipeline(image: UploadFile = File(...)):
             return JSONResponse(status_code=400, content={"error": "Exactly two eyes not detected."})
         
         results = {}
-        for i, eye_crop in enumerate(eye_crops):
-            side = f"eye_{i+1}"
+        # Sort eye crops by x-coordinate to have a consistent left-right order
+        sorted_eye_crops = sorted(eye_crops, key=lambda c: cv2.boundingRect(c)[0])
+        
+        for i, eye_crop in enumerate(sorted_eye_crops):
+            side = "left_eye" if i == 0 else "right_eye"
             iris_crop = detect_iris_roboflow(eye_crop)
             if iris_crop is None:
                 results[side] = {"status": "No iris detected", "prediction": None}
@@ -105,13 +111,11 @@ async def full_detection_pipeline(image: UploadFile = File(...)):
     finally:
         os.remove(temp_image_path)
 
-# --- 4. Create the Gradio UI for the homepage ---
-# This UI will call our own FastAPI endpoint, ensuring consistent logic.
-def gradio_wrapper(image):
+# --- 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:
-        # Save the numpy array from Gradio to a temporary file to send to our API
-        pil_image = Image.fromarray(image)
+        pil_image = Image.fromarray(image_array.astype('uint8'), 'RGB')
         with tempfile.NamedTemporaryFile(mode="wb", suffix=".jpg", delete=False) as tmp:
             pil_image.save(tmp, format="JPEG")
             tmp_path = tmp.name
@@ -121,27 +125,25 @@ def gradio_wrapper(image):
             # The API is running on the same server, so we call it locally
             response = requests.post("http://127.0.0.1:7860/api/detect/", files=files)
         
-        os.remove(tmp_path) # Clean up the temp file
+        os.remove(tmp_path)
         
         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"),
-    outputs=gr.JSON(label="Prediction Results"),
+    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. This UI calls the same API endpoint that the main application uses."
+    description="A demonstration of the LeukoLook detection model pipeline."
 )
 
-# --- 5. Mount the Gradio UI onto the FastAPI app's root ---
 app = gr.mount_gradio_app(app, gradio_ui, path="/")
 
-# --- 6. Run the server ---
+# --- 5. Run the server ---
 if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=7860)