Update app.py

app.py

@@ -2,14 +2,13 @@ import os
 import streamlit as st
 import speech_recognition as sr
 from gtts import gTTS
-import google.generativeai as genai  # Import Google Generative AI
+import google.generativeai as genai
 import base64
 from transformers import pipeline
 
+# Set up Google AI API key
 genai.configure(api_key=os.getenv("GENAI_API_KEY"))
-
-
-# Initialize recognizer
+# Initialize speech recognizer
 recognizer = sr.Recognizer()
 
 # Emotion Detection Model
@@ -17,98 +16,88 @@ emotion_model = pipeline("text-classification", model="bhadresh-savani/distilber
 
 # Function to detect emotion
 def detect_emotion(text):
-    emotion = emotion_model(text)[0]['label']
-    return emotion
+    return emotion_model(text)[0]['label']
 
-# Function to listen to customer
-def listen_to_customer():
+# Function to listen to the user
+def listen_to_user():
     with sr.Microphone() as source:
         st.write("Listening...")
         audio = recognizer.listen(source)
         try:
-            text = recognizer.recognize_google(audio)
-            st.write(f"Customer said: {text}")
-            return text
+            return recognizer.recognize_google(audio)
         except Exception as e:
             st.error(f"Speech Recognition Error: {str(e)}")
             return None
 
-# Function to process text
-def process_text(customer_input):
-    if customer_input:
+# AI Response Generation
+def generate_ai_response(user_input):
+    if user_input:
         try:
-            model = genai.GenerativeModel('gemini-1.5-flash')  # Updated model
-            response = model.generate_content(customer_input)
+            model = genai.GenerativeModel('gemini-1.5-flash')
+            response = model.generate_content(user_input)
             return response.text
         except Exception as e:
             return f"Error in AI response: {str(e)}"
-    else:
-        return "Sorry, I didn't catch that. Could you please repeat?"
+    return "Sorry, I didn't catch that."
 
-# Function to convert text to speech
-def text_to_speech(text, voice_option, language):
+# Convert Text to Speech
+def text_to_speech(text, language):
     lang_code = {"English": "en", "Spanish": "es", "French": "fr", "Hindi": "hi"}.get(language, "en")
-    tts = gTTS(text=text, lang=lang_code, tld='com' if voice_option == "Male" else 'co.uk')
+    tts = gTTS(text=text, lang=lang_code)
     file_path = "response.mp3"
     tts.save(file_path)
     return file_path
 
-# Function to autoplay audio
+# Autoplay Audio in Streamlit
 def autoplay_audio(file_path):
     with open(file_path, "rb") as f:
-        data = f.read()
-        b64 = base64.b64encode(data).decode()
-        audio_html = f"""
+        b64 = base64.b64encode(f.read()).decode()
+        st.markdown(f"""
             <audio controls autoplay>
             <source src="data:audio/mp3;base64,{b64}" type="audio/mp3">
             </audio>
-            """
-        st.markdown(audio_html, unsafe_allow_html=True)
+        """, unsafe_allow_html=True)
 
-# Main function
+# Streamlit UI
 def main():
-    st.title("Vocacity AI Voice Agent 🎙️")
+    st.title("🎙️ Vocacity AI Voice Assistant")
     st.sidebar.header("Settings")
     
     # User settings
     language = st.sidebar.selectbox("Choose Language:", ["English", "Spanish", "French", "Hindi"])
-    voice_option = st.sidebar.selectbox("Choose AI Voice:", ["Male", "Female"])
     clear_chat = st.sidebar.button("🗑️ Clear Chat")
 
     if "chat_history" not in st.session_state:
         st.session_state.chat_history = []
-    
-    # Text Input
-    user_text_input = st.text_input("Type your query here:", "")
-    
-    # Voice Input Button
+
+    # User Input
+    user_input = st.text_input("Type your query here:", "")
+
     if st.button("🎙️ Speak"):
-        customer_input = listen_to_customer()
-    else:
-        customer_input = user_text_input if user_text_input.strip() else None
-    
-    if customer_input:
-        emotion = detect_emotion(customer_input)
-        ai_response = process_text(customer_input)
-        st.session_state.chat_history.append((customer_input, ai_response))
+        user_input = listen_to_user()
+
+    if user_input:
+        emotion = detect_emotion(user_input)
+        ai_response = generate_ai_response(user_input)
+        st.session_state.chat_history.append((user_input, ai_response))
         
-        st.write(f"**AI Response:** {ai_response} (Emotion: {emotion})")
+        st.write(f"**AI:** {ai_response} (Emotion: {emotion})")
         
-        # Convert response to speech and autoplay it
-        audio_file = text_to_speech(ai_response, voice_option, language)
+        # Convert AI response to speech
+        audio_file = text_to_speech(ai_response, language)
         autoplay_audio(audio_file)
         os.remove(audio_file)
-    
+
     # Display chat history
     st.write("### Chat History")
     for user, ai in st.session_state.chat_history[-5:]:
         st.write(f"👤 {user}")
         st.write(f"🤖 {ai}")
-    
-    # Clear chat
+
+    # Clear Chat
     if clear_chat:
         st.session_state.chat_history = []
         st.experimental_rerun()
 
 if __name__ == "__main__":
-    main()
+    main()