Create app.py

app.py

@@ -0,0 +1,54 @@
+import streamlit as st
+import openai
+import whisper
+import threading
+import time
+from gtts import gTTS
+from IPython.display import Audio
+
+# Streamlit UI setup
+st.title("Real-Time Video-Calling AI Avatar Chatbot")
+
+# Load Whisper model for speech-to-text
+whisper_model = whisper.load_model("base")
+
+# OpenAI API setup
+openai.api_key = 'YOUR_OPENAI_API_KEY'
+
+# Text-to-Speech function using gTTS
+def text_to_speech(text):
+    tts = gTTS(text=text, lang='en')
+    audio_fp = '/tmp/response.mp3'
+    tts.save(audio_fp)
+    return audio_fp
+
+# Play the audio in Colab
+def play_audio(audio_fp):
+    return Audio(audio_fp)
+
+# Get AI response using OpenAI API
+def get_ai_response(text):
+    prompt = f'User: {text}\nAI:'
+    response = openai.Completion.create(
+        engine='text-davinci-003', prompt=prompt, max_tokens=150
+    )
+    return response.choices[0].text.strip()
+
+# Function to handle video call
+def video_call():
+    # Placeholder for webcam capture
+    # For demonstration, we use text as input (you can integrate actual webcam input)
+    user_input = "Hello AI, how are you?"
+
+    # Get response from AI
+    ai_response = get_ai_response(user_input)
+
+    # Convert the response to speech
+    audio_fp = text_to_speech(ai_response)
+
+    # Play the response
+    play_audio(audio_fp)
+
+# Start video call in Streamlit
+if st.button("Start Video Call"):
+    video_call()