removed enhance_image_unsharp_mask function

app.py

@@ -1,5 +1,4 @@
 # Final, Complete, and Working app.py for Hugging Face Space
-
 import os
 import cv2
 import tempfile
@@ -18,6 +17,7 @@ import gradio as gr
 
 # --- 1. Configuration and Model Loading ---
 ROBOFLOW_API_KEY = os.environ.get("ROBOFLOW_API_KEY")
+
 CLIENT_FACE = InferenceHTTPClient(api_url="https://detect.roboflow.com", api_key=ROBOFLOW_API_KEY)
 CLIENT_EYES = InferenceHTTPClient(api_url="https://detect.roboflow.com", api_key=ROBOFLOW_API_KEY)
 CLIENT_IRIS = InferenceHTTPClient(api_url="https://detect.roboflow.com", api_key=ROBOFLOW_API_KEY)
@@ -32,9 +32,8 @@ except Exception as e:
     raise RuntimeError(f"Could not load leukocoria model: {e}")
 
 # --- 2. All Helper Functions ---
-def enhance_image_unsharp_mask(image, strength=0.5, radius=5):
-    blur = cv2.GaussianBlur(image, (radius, radius), 0)
-    return cv2.addWeighted(image, 1.0 + strength, blur, -strength, 0)
+
+# NOTE: The 'enhance_image_unsharp_mask' function has been removed.
 
 def detect_faces_roboflow(image_path):
     return CLIENT_FACE.infer(image_path, model_id="face-detector-v4liw/2").get("predictions", [])
@@ -52,41 +51,32 @@ def detect_eyes_roboflow(image_path, raw_image):
             crop = raw_image[y1:y2, x1:x2]
             if crop.size > 0:
                 crops.append(crop)
-        # On success, return the crops and None for the error message
         return crops, None
     except Exception as e:
-        # If Roboflow fails, return an empty list and the error message
         print(f"Error in Roboflow eye detection: {e}")
         return [], str(e)
 
-# In app.py, replace the existing function with this one
-
 def get_largest_iris_prediction(eye_crop):
     "Calls Roboflow to find the largest iris using a temporary file for reliability."
-    
-    # --- NEW: Enhance the eye crop before saving it ---
-    enhanced_eye_crop = enhance_image_unsharp_mask(eye_crop)
-
     with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
-        # Save the ENHANCED version, not the original
-        cv2.imwrite(tmp.name, enhanced_eye_crop)
+        # Save the original eye crop, not an enhanced version
+        cv2.imwrite(tmp.name, eye_crop)
         temp_iris_path = tmp.name
-
     try:
-        # Use the file path for inference, which is more robust
         resp = CLIENT_IRIS.infer(temp_iris_path, model_id="iris_120_set/7")
         preds = resp.get("predictions", [])
         return max(preds, key=lambda p: p["width"] * p["height"]) if preds else None
     finally:
-        # Ensure the temporary file is always deleted
         os.remove(temp_iris_path)
 
 def run_leukocoria_prediction(iris_crop):
     if leuko_model is None: return {"error": "Leukocoria model not loaded"}, 0.0
+    # Convert crop to PIL Image
     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
+    # Resize the original image array
+    img_resized = cv2.resize(np.array(img_pil), (224, 224))
+    # Normalize and expand dimensions for the model
+    img_array = np.array(img_resized) / 255.0
     img_array = np.expand_dims(img_array, axis=0)
     prediction = leuko_model.predict(img_array)
     confidence = float(prediction[0][0])
@@ -102,22 +92,18 @@ async def full_detection_pipeline(image: UploadFile = File(...)):
         contents = await image.read()
         tmp.write(contents)
         temp_image_path = tmp.name
-
     try:
         raw_image = cv2.imread(temp_image_path)
         if raw_image is None:
             return JSONResponse(status_code=400, content={"error": "Could not read uploaded image."})
-
         if not detect_faces_roboflow(temp_image_path):
             return JSONResponse(status_code=400, content={"error": "No face detected."})
-
         image_to_process = raw_image
         was_mirrored = False
         
         print("--- 1. Attempting detection on original image... ---")
         eye_crops, error_msg = detect_eyes_roboflow(temp_image_path, image_to_process)
         print(f"--- 2. Found {len(eye_crops)} eyes in original image. ---")
-
         if len(eye_crops) != 2:
             print("--- 3. Original failed. Attempting detection on mirrored image... ---")
             mirrored_image = cv2.flip(raw_image, 1)
@@ -132,16 +118,16 @@ async def full_detection_pipeline(image: UploadFile = File(...)):
                 print(f"--- 4. Found {len(eye_crops)} eyes in mirrored image. ---")
             finally:
                 os.remove(temp_mirrored_image_path)
-
+        
         if error_msg or len(eye_crops) != 2:
             return JSONResponse(
                 status_code=400,
                 content={"error": "Could not detect exactly two eyes. Please try another photo."}
             )
-
+        
         initial_boxes = [cv2.boundingRect(cv2.cvtColor(c, cv2.COLOR_BGR2GRAY)) for c in eye_crops]
         print(f"--- 5. Initial eye coordinates (x,y,w,h): {initial_boxes} ---")
-
+        
         eye_crops.sort(key=lambda c: cv2.boundingRect(cv2.cvtColor(c, cv2.COLOR_BGR2GRAY))[0])
         
         sorted_boxes = [cv2.boundingRect(cv2.cvtColor(c, cv2.COLOR_BGR2GRAY)) for c in eye_crops]
@@ -152,7 +138,7 @@ async def full_detection_pipeline(image: UploadFile = File(...)):
             eye_crops.reverse()
             reversed_boxes = [cv2.boundingRect(cv2.cvtColor(c, cv2.COLOR_BGR2GRAY)) for c in eye_crops]
             print(f"--- 8. Reversed eye coordinates (x,y,w,h): {reversed_boxes} ---")
-
+        
         flags = {}
         eye_images_b64 = {}
         for i, eye_crop in enumerate(eye_crops):
@@ -162,7 +148,7 @@ async def full_detection_pipeline(image: UploadFile = File(...)):
             is_success, buffer = cv2.imencode(".jpg", eye_crop)
             if is_success:
                 eye_images_b64[side] = "data:image/jpeg;base64," + base64.b64encode(buffer).decode("utf-8")
-
+            
             pred = get_largest_iris_prediction(eye_crop)
             if pred:
                 x1, y1 = int(pred['x'] - pred['width'] / 2), int(pred['y'] - pred['height'] / 2)
@@ -172,27 +158,24 @@ async def full_detection_pipeline(image: UploadFile = File(...)):
                 flags[side] = has_leuko
             else:
                 flags[side] = None
-        
-        # --- THIS BLOCK IS NOW CORRECTLY UN-INDENTED ---
-        # It runs AFTER the 'for' loop is complete.
+                
         print("--- 10. Final generated flags:", flags, "---")
         
         is_success_main, buffer_main = cv2.imencode(".jpg", image_to_process)
         analyzed_image_b64 = ""
         if is_success_main:
             analyzed_image_b64 = "data:image/jpeg;base64," + base64.b64encode(buffer_main).decode("utf-8")
-
+            
         return JSONResponse(content={
             "leukocoria": flags,
             "warnings": [],
             "two_eyes": eye_images_b64,
             "analyzed_image": analyzed_image_b64
         })
-
     finally:
         os.remove(temp_image_path)
-        
-# --- 4. Create and Mount the Gradio UI for a professional homepage ---
+
+# --- 4. Create and Mount the Gradio UI ---
 def gradio_wrapper(image_array):
     """A wrapper function to call our own FastAPI endpoint from the Gradio UI."""
     try:
@@ -200,8 +183,9 @@ def gradio_wrapper(image_array):
         with io.BytesIO() as buffer:
             pil_image.save(buffer, format="JPEG")
             files = {'image': ('image.jpg', buffer.getvalue(), 'image/jpeg')}
+            # The URL points to the local FastAPI server running within the Hugging Face Space
             response = requests.post("http://127.0.0.1:7860/detect/", files=files)
-        
+            
         if response.status_code == 200:
             return response.json()
         else:
@@ -214,8 +198,7 @@ gradio_ui = gr.Interface(
     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."
-)
+    description="A demonstration of the LeukoLook detection model pipeline.")
 
 app = gr.mount_gradio_app(app, gradio_ui, path="/")