Update app.py

app.py

@@ -1,3 +1,13 @@
+# 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
@@ -7,6 +17,7 @@ import numpy as np
 import tensorflow as tf
 from tensorflow.keras.models import load_model
 from moviepy.editor import VideoFileClip
+import socketIO_client as sio
 
 def convert_video_to_audio_moviepy(video_file, output_ext="wav"):
     """Converts video to audio using MoviePy library that uses `ffmpeg` under the hood"""
@@ -29,7 +40,8 @@ def process_video_audio(video_path):
     
     face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
 
-    if len(wav[0]) > 261540:
+    if
+ len(wav[0]) > 261540:
         print(wav.shape)
         train_audio_wave[0, :] = wav[0][:261540]
     else:
@@ -79,24 +91,91 @@ 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'}
+# 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()
+
+def run_chat_server(app):
+    """Runs a chat server using socket.IO"""
+    clients = []
+    messages = []
+
+    @app.route('/chat', methods=['GET', 'POST'])
+    def chat():
+        return app.socketio.send(messages)
+
+    @app.socketio.on('message')
+    def handle_message(message):
+        clients.append(message['client'])
+        messages.append(message)
+        app.logger.info(f'Received message: {message}')
+        app.socketio.emit('message', message, skip_sid=True)
+
+    @app.socketio.on('connect')
+    def handle_connect():
+        app.logger.info('Client connected')
+
+    @app.socketio.on('disconnect')
+    def handle_disconnect():
+        app.logger.info('Client disconnected')
+
+    if __name__ == '__main__':
+        app.run(debug=True)
+
+def predict_emotion_with_chat(video_path):
     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
+    client = sio.Client()
+    client.connect('http://localhost:5000/chat')
+
+    # Send the predicted emotion to the chat server
+    client.emit('message', {'client': 'Emotion Recognition', 'message': f'Predicted emotion: {predicted_emotion}'})
+
+    # Receive messages from the chat server
+    for msg in client.events:
+        print(msg)
+
+    return last_frame, audio_path, predicted_emotion, messages
 
 iface = gr.Interface(
-    fn=predict_emotion_gradio, 
+    fn=predict_emotion_with_chat,
     inputs=[
         gr.Video(label="Upload a video")
     ],
     outputs=[
         gr.Image(label="Last Frame"),
-        gr.Audio(label = "Audio"),
-        gr.Textbox(label="Predicted Emotion")
+        gr.Audio(label="Audio"),
+        gr.Textbox(label="Predicted Emotion"),
+        gr.Chatbox(label="Chat")
     ],
-    title="Emotion Recognition from Video",
-    description="Upload a video and get the predicted emotion."
+    title="Emotion Recognition with Chat",
+    description="Upload a video and get the predicted emotion. Chat with others in real-time."
 )
 
+# Start the Gradio interface and the chat server
+from flask import Flask
+app = Flask(__name__)
+app.config['SECRET_KEY'] = 'secret'
+app.socketio = sio.SocketIO(app)
+run_chat_server(app)
 iface.launch()