Create app.py

app.py

@@ -0,0 +1,132 @@
+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__":
+    demo.launch(share=True)