Fixed gr.Error bug not showing error message.

app.py

@@ -39,105 +39,125 @@ model.eval()
 
 
 def age_image(image_path: str, source_age: int, target_age: int) -> Image.Image:
-    image = Image.open(image_path)
-    if image.mode not in ["RGB", "L"]:
-        print(f"Converting image from {image.mode} to RGB")
-        image = image.convert("RGB")
-    processed_image = process_image(model, image, source_age, target_age)
-    imagine(image_path, source_age)
-    return processed_image
+    try:
+        image = Image.open(image_path)
+        if image.mode not in ["RGB", "L"]:
+            print(f"Converting image from {image.mode} to RGB")
+            image = image.convert("RGB")
+        processed_image = process_image(model, image, source_age, target_age)
+        imagine(image_path, source_age)
+        return processed_image
+    except ValueError as e:
+        if "No faces detected" in str(e):
+            raise gr.Error("No faces detected in the image. Please upload an image with a clear, visible face.")
+        else:
+            raise gr.Error(f"Error processing image: {str(e)}")
+    except Exception as e:
+        raise gr.Error(f"Unexpected error: {str(e)}")
 
 
 def age_video(image_path: str, source_age: int, target_age: int, duration: int, fps: int) -> str:
-    image = Image.open(image_path)
-
-    orig_tmp = tempfile.NamedTemporaryFile(suffix=".jpg", delete=False)
-    orig_path = orig_tmp.name
-    image.save(orig_path)
-    orig_tmp.close()
-
-    aged_img = age_image(image_path, source_age, target_age)
-    aged_tmp = tempfile.NamedTemporaryFile(suffix=".jpg", delete=False)
-    aged_path = aged_tmp.name
-    aged_img.save(aged_path)
-    aged_tmp.close()
-    imagine(image_path, source_age)
-
-    client = Client("Robys01/Face-Morphing")
     try:
-        result = client.predict(
-            image_files=[handle_file(orig_path), handle_file(aged_path)],
-            duration=duration,
-            fps=fps,
-            method="Dlib",
-            align_resize=False,
-            order_images=False,
-            guideline=False,
-            api_name="/predict"
-        )
+        image = Image.open(image_path)
+
+        orig_tmp = tempfile.NamedTemporaryFile(suffix=".jpg", delete=False)
+        orig_path = orig_tmp.name
+        image.save(orig_path)
+        orig_tmp.close()
+
+        aged_img = age_image(image_path, source_age, target_age)
+        aged_tmp = tempfile.NamedTemporaryFile(suffix=".jpg", delete=False)
+        aged_path = aged_tmp.name
+        aged_img.save(aged_path)
+        aged_tmp.close()
+        imagine(image_path, source_age)
+
+        client = Client("Robys01/Face-Morphing")
+        try:
+            result = client.predict(
+                image_files=[handle_file(orig_path), handle_file(aged_path)],
+                duration=duration,
+                fps=fps,
+                method="Dlib",
+                align_resize=False,
+                order_images=False,
+                guideline=False,
+                api_name="/predict"
+            )
+        except Exception as e:
+            raise gr.Error(f"Error during video generation: {e}")
+       
+         # Unpack response for video path
+        video_path = None
+        # handle (data, msg) tuple
+        if isinstance(result, tuple):
+            data, msg = result
+            video_path = data.get('video') if isinstance(data, dict) else None
+            print(f"Response message: {msg}")
+
+        if not video_path or not os.path.exists(video_path):
+            raise gr.Error(f"Video file not found: {video_path}")
+
+        return video_path
+    except gr.Error:
+        # Re-raise Gradio errors as-is
+        raise
     except Exception as e:
-        raise gr.Error(f"Error during video generation: {e}")
-   
-     # Unpack response for video path
-    video_path = None
-    # handle (data, msg) tuple
-    if isinstance(result, tuple):
-        data, msg = result
-        video_path = data.get('video') if isinstance(data, dict) else None
-        print(f"Response message: {msg}")
-
-    if not video_path or not os.path.exists(video_path):
-        raise gr.Error(f"Video file not found: {video_path}")
-
-    return video_path
+        raise gr.Error(f"Unexpected error in video generation: {str(e)}")
 
 
 def age_timelapse(image_path: str, source_age: int) -> str:
-    image = Image.open(image_path)
-
-    target_ages = [10, 20, 30, 50, 70]
-    # Filter out ages too close to source
-    filtered = [age for age in target_ages if abs(age - source_age) >= 4]
-    # Combine with source and sort
-    ages = sorted(set(filtered + [source_age]))
-    temp_handles = []
-
-    for age in ages:
-        if age == source_age:
-            img = image
-        else:
-            img = age_image(image_path, source_age, age)
-        tmp = tempfile.NamedTemporaryFile(suffix=".jpg", delete=False)
-        path = tmp.name
-        img.save(path)
-        tmp.close()
-        temp_handles.append(handle_file(path))
-    imagine(image_path, source_age)
-
-    client = Client("Robys01/Face-Morphing")
     try:
-        result = client.predict(
-            image_files=temp_handles,
-            duration=3,
-            fps=20,
-            method="Dlib",
-            align_resize=False,
-            order_images=False,
-            guideline=False,
-            api_name="/predict"
-        )
+        image = Image.open(image_path)
+
+        target_ages = [10, 20, 30, 50, 70]
+        # Filter out ages too close to source
+        filtered = [age for age in target_ages if abs(age - source_age) >= 4]
+        # Combine with source and sort
+        ages = sorted(set(filtered + [source_age]))
+        temp_handles = []
+
+        for age in ages:
+            if age == source_age:
+                img = image
+            else:
+                img = age_image(image_path, source_age, age)
+            tmp = tempfile.NamedTemporaryFile(suffix=".jpg", delete=False)
+            path = tmp.name
+            img.save(path)
+            tmp.close()
+            temp_handles.append(handle_file(path))
+        imagine(image_path, source_age)
+
+        client = Client("Robys01/Face-Morphing")
+        try:
+            result = client.predict(
+                image_files=temp_handles,
+                duration=3,
+                fps=20,
+                method="Dlib",
+                align_resize=False,
+                order_images=False,
+                guideline=False,
+                api_name="/predict"
+            )
+        except Exception as e:
+            raise gr.Error(f"Error generating timelapse video: {e}")
+
+        video_path = None
+        if isinstance(result, tuple):
+            data, msg = result
+            video_path = data.get('video') if isinstance(data, dict) else None
+            print(f"Response message: {msg}")
+
+        if not video_path or not os.path.exists(video_path):
+            raise gr.Error(f"Timelapse video not found: {video_path}")
+        return video_path
+    except gr.Error:
+        # Re-raise Gradio errors as-is
+        raise
     except Exception as e:
-        raise gr.Error(f"Error generating timelapse video: {e}")
-
-    video_path = None
-    if isinstance(result, tuple):
-        data, msg = result
-        video_path = data.get('video') if isinstance(data, dict) else None
-        print(f"Response message: {msg}")
-
-    if not video_path or not os.path.exists(video_path):
-        raise gr.Error(f"Timelapse video not found: {video_path}")
-    return video_path
+        raise gr.Error(f"Unexpected error in timelapse generation: {str(e)}")
 
 
 demo_age_image = gr.Interface(
@@ -190,11 +210,12 @@ demo_age_timelapse = gr.Interface(
     description="Generate a timelapse video showing the aging process at different ages."
 )
 
-iface = gr.TabbedInterface(
-    [demo_age_image, demo_age_video, demo_age_timelapse],
-    tab_names=["Face Aging", "Aging Video", "Aging Timelapse"],
-    title="Face Aging Demo",
-)
 
 if __name__ == "__main__":
+
+    iface = gr.TabbedInterface(
+        [demo_age_image, demo_age_video, demo_age_timelapse],
+        tab_names=["Face Aging", "Aging Video", "Aging Timelapse"],
+        title="Face Aging Demo",
+    ).queue()
     iface.launch(server_name="0.0.0.0", server_port=7000)

test_functions.py

@@ -57,7 +57,15 @@ def process_image(your_model, image, source_age, target_age=0,
     # image = face_recognition.load_image_file(filename)
     image = np.array(image)
 
-    fl = face_recognition.face_locations(image)[0]
+    # Detect faces in the image
+    face_locations = face_recognition.face_locations(image)
+    
+    # Check if any faces were detected
+    if not face_locations:
+        raise ValueError("No faces detected in the image. Please ensure the image contains a clear, visible face.")
+    
+    # Use the first detected face
+    fl = face_locations[0]
 
     # calculate margins
     margin_y_t = int((fl[2] - fl[0]) * .63 * .85)  # larger as the forehead is often cut off