Update app.py

app.py

@@ -3,6 +3,7 @@ import cv2
 import numpy as np
 from model import EmotionPredictor
 
+
 # Initialize the predictor
 predictor = EmotionPredictor()
 
@@ -14,6 +15,7 @@ face_cascade = cv2.CascadeClassifier(
 if face_cascade.empty():
     raise RuntimeError("Failed to load Haar Cascade")
 
+
 def predict_emotion(image):
     """
     Predict emotion from an image.
@@ -80,6 +82,7 @@ def predict_emotion(image):
     
     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("""
@@ -93,9 +96,7 @@ with gr.Blocks(title="Smilo😃 - Real-Time Emotion Detection") as demo:
         <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 & OpenCV</p>
-        <img src="https://skillicons.dev/icons?i=pytorch,opencv"/>
-</p>
+        <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>
     """)
     
@@ -112,12 +113,20 @@ with gr.Blocks(title="Smilo😃 - Real-Time Emotion Detection") as demo:
             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
+    # 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()