Update app.py

app.py

@@ -11,64 +11,54 @@ emotions = ['HAPPY', 'SAD', 'SURPRISED']
 with open('model.pkl', 'rb') as f:
     model = pickle.load(f)
 
-def predict_emotion(image, draw_option):
-    if image is None:
-        return {"Error": 1.0}, None, "⚠ Please upload an image."
-
+def predict_emotion(image, show_landmarks):
     # Convert PIL image to OpenCV format
     img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
 
     # Extract landmarks
-    face_landmarks = get_face_landmarks(img, draw=(draw_option == "Show Landmarks"), static_image_mode=True)
+    face_landmarks = get_face_landmarks(img, draw=show_landmarks, static_image_mode=True)
 
     if face_landmarks is None or len(face_landmarks) == 0:
         return {"No face detected": 1.0}, None, "⚠ No face detected in the image."
 
-    # Predict emotion
+    # Predict emotion and probabilities
     output = model.predict([face_landmarks])
     predicted_emotion = emotions[int(output[0])]
 
-    # Confidence scores
     if hasattr(model, "predict_proba"):
         probs = model.predict_proba([face_landmarks])[0]
         confidence_dict = {emotions[i]: float(probs[i]) for i in range(len(emotions))}
     else:
         confidence_dict = {predicted_emotion: 1.0}
 
-    # Annotate image only if user wants and face is detected
-    # if draw_option == "Show Landmarks" or draw_option == "Show Emotion":
-    #     if draw_option == "Show Emotion":
-    #     #     cv2.putText(img, predicted_emotion, (10, img.shape[0] - 10),
-    #     #                 cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
-    #     annotated_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-    # else:
-    #     annotated_img = None  # No annotation if user chooses "None"
+    # If user wants landmarks, `get_face_landmarks` draws them, otherwise original image
+    if show_landmarks:
+        annotated_img = img
+    else:
+        annotated_img = None  # Don't show any image if landmarks not requested
 
-    # return confidence_dict, annotated_img, f" Detected emotion: {predicted_emotion}"
+    return confidence_dict, cv2.cvtColor(annotated_img, cv2.COLOR_BGR2RGB) if annotated_img is not None else None, f"✅ Detected emotion: {predicted_emotion}"
 
 # Example images
-examples = [["examples/happy.png"], ["examples/sad.png"], ["examples/surprised.png"]]
-
-# Custom UI with Radio Toggle
-with gr.Blocks(theme="default") as demo:
-    gr.Markdown("## 🧠 Emotion Detector")
-    gr.Markdown("Upload an image or use webcam to detect emotions (HAPPY, SAD, SURPRISED).")
-
-    with gr.Row():
-        with gr.Column():
-            image_input = gr.Image(type="pil", label="Upload Image or Use Webcam", sources=["upload", "webcam"])
-            draw_option = gr.Radio(choices=["None", "Show Landmarks", "Show Emotion"], value="Show Emotion",
-                                   label="Annotation Mode", interactive=True)
-        with gr.Column():
-            label_output = gr.Label(num_top_classes=3, label="Predicted Emotion & Confidence")
-            image_output = gr.Image(type="numpy", label="Annotated Image")
-            status_output = gr.Textbox(label="Status", interactive=False)
-
-    # Real-time update with both image and annotation mode
-    image_input.change(fn=predict_emotion, inputs=[image_input, draw_option], outputs=[label_output, image_output, status_output])
-    draw_option.change(fn=predict_emotion, inputs=[image_input, draw_option], outputs=[label_output, image_output, status_output])
-
-    gr.Examples(examples=examples, inputs=[image_input])
+examples = [["examples/happy.png", True], ["examples/sad.png", True], ["examples/surprised.png", True]]
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=predict_emotion,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image or Use Webcam", sources=["upload", "webcam"]),
+        gr.Checkbox(label="Show Landmarks", value=False)
+    ],
+    outputs=[
+        gr.Label(num_top_classes=3, label="Predicted Emotion & Confidence"),
+        gr.Image(type="numpy", label="Landmark Image"),  # Only shown if checkbox is True
+        gr.Textbox(label="Status", interactive=False)
+    ],
+    title="Emotion Detector",
+    description="Upload an image or use webcam to detect emotions (HAPPY, SAD, SURPRISED). Check 'Show Landmarks' to visualize facial landmarks.",
+    examples=examples,
+    theme="default"
+)
 
 if __name__ == "__main__":
     demo.launch()