Update app.py

app.py

@@ -1,13 +1,3 @@
-# # import gradio as gr
-# # import torch as pt
-# # import torchaudio
-# # import cv2
-# # import os
-# # import numpy as np
-# # import tensorflow as tf
-# # from tensorflow.keras.models import load_model
-# # from moviepy.editor import VideoFileClip
-
 import gradio as gr
 import torch as pt
 import torchaudio
@@ -17,8 +7,6 @@ import numpy as np
 import tensorflow as tf
 from tensorflow.keras.models import load_model
 from moviepy.editor import VideoFileClip
-from flask import Flask
-from flask_socketio import SocketIO, emit
 
 def convert_video_to_audio_moviepy(video_file, output_ext="wav"):
     """Converts video to audio using MoviePy library that uses `ffmpeg` under the hood"""
@@ -92,68 +80,25 @@ def predict_emotion(video_path):
     predicted_label = np.argmax(predictions)
     return last_frame, audio_path, predicted_label
 
-# def predict_emotion_gradio(video_path):
-#     emotion_dict = {0: 'neutral', 1: 'calm', 2: 'happy', 3: 'sad', 4: 'angry', 5: 'fearful'}
-#     last_frame, audio_path, predicted_label = predict_emotion(video_path)
-#     predicted_emotion = emotion_dict[predicted_label]
-#     return last_frame, audio_path, predicted_emotion
-
-# iface = gr.Interface(
-#     fn=predict_emotion_gradio, 
-#     inputs=[
-#         gr.Video(label="Upload a video")
-#     ],
-#     outputs=[
-#         gr.Image(label="Last Frame"),
-#         gr.Audio(label = "Audio"),
-#         gr.Textbox(label="Predicted Emotion")
-#     ],
-#     title="Emotion Recognition from Video",
-#     description="Upload a video and get the predicted emotion."
-# )
-
-# iface.launch()
-
-
-# Integrate chat functionality with emotion prediction
-def predict_emotion_with_chat(video_path):
+def predict_emotion_gradio(video_path):
     emotion_dict = {0: 'neutral', 1: 'calm', 2: 'happy', 3: 'sad', 4: 'angry', 5: 'fearful'}
     last_frame, audio_path, predicted_label = predict_emotion(video_path)
     predicted_emotion = emotion_dict[predicted_label]
+    return last_frame, audio_path, predicted_emotion
 
-    # Connect to the chat server
-    socketio.emit('message', {'client': 'Emotion Recognition', 'message': f'Predicted emotion: {predicted_emotion}'})
-    
-    # Get messages from the chat server
-    messages = []  # This should be updated with real messages from the server
-
-    return last_frame, audio_path, predicted_emotion, messages
-
-# Gradio Interface
 iface = gr.Interface(
-    fn=predict_emotion_with_chat,
-    inputs=[gr.Video(label="Upload a video")],
+    fn=predict_emotion_gradio, 
+    inputs=[
+        gr.Video(label="Upload a video")
+    ],
     outputs=[
         gr.Image(label="Last Frame"),
-        gr.Audio(label="Audio"),
-        gr.Textbox(label="Predicted Emotion"),
-        gr.Chatbot(label="Chat")
+        gr.Audio(label = "Audio"),
+        gr.Textbox(label="Predicted Emotion")
     ],
-    title="Emotion Recognition with Chat",
-    description="Upload a video and get the predicted emotion. Chat with others in real-time."
+    title="Emotion Recognition from Video",
+    description="Upload a video and get the predicted emotion."
 )
 
-# Flask app setup
-app = Flask(__name__)
-app.config['SECRET_KEY'] = 'secret'
-socketio = SocketIO(app)
-
-# Run the chat server
-@socketio.on('message')
-def handle_message(message):
-    emit('message', message, broadcast=True)
+iface.launch()
 
-if __name__ == '__main__':
-    iface.launch(share=True)
-    socketio.run(app, debug=True)
-