Update app.py

app.py

@@ -1,132 +1,121 @@
-import gradio as gr
-import cv2
-import numpy as np
-from model import EmotionPredictor
-
-
-# Initialize the predictor
-predictor = EmotionPredictor()
-
-# Load Haar Cascade for face detection
-face_cascade = cv2.CascadeClassifier(
-    cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
-)
-
-if face_cascade.empty():
-    raise RuntimeError("Failed to load Haar Cascade")
-
-
-def predict_emotion(image):
-    """
-    Predict emotion from an image.
-    
-    Args:
-        image: PIL Image or numpy array
-        
-    Returns:
-        annotated image and emotion prediction
-    """
-    if image is None:
-        return None, "No image provided"
-    
-    # Convert PIL Image to numpy array if needed
-    if isinstance(image, np.ndarray):
-        frame = image
-    else:
-        frame = np.array(image)
-    
-    # Convert RGB to BGR for OpenCV
-    if len(frame.shape) == 3 and frame.shape[2] == 3:
-        frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
-    else:
-        frame_bgr = frame
-    
-    # Convert to grayscale for face detection
-    gray = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2GRAY)
-    
-    # Detect faces
-    detected = face_cascade.detectMultiScale(
-        gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)
-    )
-    
-    if len(detected) == 0:
-        return frame, "No face detected"
-    
-    # Get the largest face
-    faces = [max(detected, key=lambda r: r[2]*r[3])]
-    
-    # Process the face
-    output_frame = frame_bgr.copy()
-    emotions = []
-    
-    for (x, y, w, h) in faces:
-        # Extract face region
-        face_rgb = cv2.cvtColor(frame_bgr[y:y+h, x:x+w], cv2.COLOR_BGR2RGB)
-        
-        # Predict emotion
-        emotion = predictor.predict(face_rgb)
-        emotions.append(emotion)
-        
-        # Draw rectangle and label
-        cv2.rectangle(output_frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
-        cv2.putText(
-            output_frame, emotion, (x, y - 10),
-            cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2
-        )
-    
-    # Convert back to RGB for display
-    output_frame_rgb = cv2.cvtColor(output_frame, cv2.COLOR_BGR2RGB)
-    
-    # Return annotated image and detected emotion
-    emotion_text = ", ".join(emotions) if emotions else "No emotion detected"
-    
-    return output_frame_rgb, f"Detected emotion(s): {emotion_text}"
-
-
-# Create Gradio interface
-with gr.Blocks(title="Smilo😃 - Real-Time Emotion Detection") as demo:
-    gr.HTML("""
-    <div style="background: linear-gradient(90deg, #FF9933 0%, #D0B264 50%, #469F93 100%); 
-                padding: 40px; 
-                border-radius: 12px; 
-                text-align: center; 
-                color: white; 
-                font-family: 'Helvetica Neue', Arial, sans-serif;
-                margin-bottom: 20px;">
-        <h1 style="color: white; margin: 0; margin-bottom: 10px; font-weight: 900; font-size: 3.5em; display: flex; align-items: center; justify-content: center; gap: 10px;">
-            Smilo <span style="font-size: 0.9em;">😃</span>
-        </h1>
-        <p style="color: #f0f0f0; font-size: 1.2em; margin: 0; font-weight: 400; letter-spacing: 0.5px;">Real-Time Emotion Detection powered by PyTorch</p>
-    </div>
-    """)
-    
-    with gr.Row():
-        with gr.Column():
-            image_input = gr.Image(
-                label="Input Image",
-                type="pil",
-                sources=["upload", "webcam"]
-            )
-            submit_btn = gr.Button("Predict Emotion", variant="primary")
-        
-        with gr.Column():
-            image_output = gr.Image(label="Annotated Image")
-            emotion_output = gr.Textbox(label="Prediction Result", interactive=False)
-    
-    # Connect the function to the button
-    submit_btn.click(
-        fn=predict_emotion,
-        inputs=[image_input],
-        outputs=[image_output, emotion_output]
-    )
-    
-    # Also run prediction when image is uploaded
-    image_input.change(
-        fn=predict_emotion,
-        inputs=[image_input],
-        outputs=[image_output, emotion_output]
-    )
-
-
-if __name__ == "__main__":
+import gradio as gr
+import cv2
+import numpy as np
+from model import EmotionPredictor
+
+# Initialize the predictor
+predictor = EmotionPredictor()
+
+# Load Haar Cascade for face detection
+face_cascade = cv2.CascadeClassifier(
+    cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
+)
+
+if face_cascade.empty():
+    raise RuntimeError("Failed to load Haar Cascade")
+
+def predict_emotion(image):
+    """
+    Predict emotion from an image.
+    
+    Args:
+        image: PIL Image or numpy array
+        
+    Returns:
+        annotated image and emotion prediction
+    """
+    if image is None:
+        return None, "No image provided"
+    
+    # Convert PIL Image to numpy array if needed
+    if isinstance(image, np.ndarray):
+        frame = image
+    else:
+        frame = np.array(image)
+    
+    # Convert RGB to BGR for OpenCV
+    if len(frame.shape) == 3 and frame.shape[2] == 3:
+        frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+    else:
+        frame_bgr = frame
+    
+    # Convert to grayscale for face detection
+    gray = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2GRAY)
+    
+    # Detect faces
+    detected = face_cascade.detectMultiScale(
+        gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)
+    )
+    
+    if len(detected) == 0:
+        return frame, "No face detected"
+    
+    # Get the largest face
+    faces = [max(detected, key=lambda r: r[2]*r[3])]
+    
+    # Process the face
+    output_frame = frame_bgr.copy()
+    emotions = []
+    
+    for (x, y, w, h) in faces:
+        # Extract face region
+        face_rgb = cv2.cvtColor(frame_bgr[y:y+h, x:x+w], cv2.COLOR_BGR2RGB)
+        
+        # Predict emotion
+        emotion = predictor.predict(face_rgb)
+        emotions.append(emotion)
+        
+        # Draw rectangle and label
+        cv2.rectangle(output_frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
+        cv2.putText(
+            output_frame, emotion, (x, y - 10),
+            cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2
+        )
+    
+    # Convert back to RGB for display
+    output_frame_rgb = cv2.cvtColor(output_frame, cv2.COLOR_BGR2RGB)
+    
+    # Return annotated image and detected emotion
+    emotion_text = ", ".join(emotions) if emotions else "No emotion detected"
+    
+    return output_frame_rgb, f"Detected emotion(s): {emotion_text}"
+
+# Create Gradio interface
+with gr.Blocks(title="Smilo😃 - Real-Time Emotion Detection") as demo:
+    gr.HTML("""
+    <div style="background: linear-gradient(90deg, #FF9933 0%, #D0B264 50%, #469F93 100%); 
+                padding: 40px; 
+                border-radius: 12px; 
+                text-align: center; 
+                color: white; 
+                font-family: 'Helvetica Neue', Arial, sans-serif;
+                margin-bottom: 20px;">
+        <h1 style="color: white; margin: 0; margin-bottom: 10px; font-weight: 900; font-size: 3.5em; display: flex; align-items: center; justify-content: center; gap: 10px;">
+            Smilo <span style="font-size: 0.9em;">😃</span>
+        </h1>
+        <p style="color: #f0f0f0; font-size: 1.2em; margin: 0; font-weight: 400; letter-spacing: 0.5px;">Real-Time Emotion Detection powered by PyTorch</p>
+    </div>
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(
+                label="Input Image",
+                type="pil",
+                sources=["upload", "webcam"]
+            )
+            submit_btn = gr.Button("Predict Emotion", variant="primary")
+        
+        with gr.Column():
+            image_output = gr.Image(label="Annotated Image")
+            emotion_output = gr.Textbox(label="Prediction Result", interactive=False)
+    
+    # Connect the function strictly to the button to prevent webcam API spam
+    submit_btn.click(
+        fn=predict_emotion,
+        inputs=[image_input],
+        outputs=[image_output, emotion_output]
+    )
+
+if __name__ == "__main__":
     demo.launch()