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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 base64
from transformers import pipeline
# Set up Google Generative AI API key
genai.configure(api_key="AIzaSyBTHMbdVbhH9qacFe8yQMtAkLUo4HLGoRs")
# Initialize recognizer
recognizer = sr.Recognizer()
# Emotion Detection Model
emotion_model = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion")
# Function to detect emotion
def detect_emotion(text):
emotion = emotion_model(text)[0]['label']
return emotion
# Function to listen to customer
def listen_to_customer():
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
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:
try:
model = genai.GenerativeModel('gemini-1.5-flash') # Updated model
response = model.generate_content(customer_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?"
# Function to convert text to speech
def text_to_speech(text, voice_option, 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')
file_path = "response.mp3"
tts.save(file_path)
return file_path
# Function to autoplay audio
def autoplay_audio(file_path):
with open(file_path, "rb") as f:
data = f.read()
b64 = base64.b64encode(data).decode()
audio_html = f"""
<audio controls autoplay>
<source src="data:audio/mp3;base64,{b64}" type="audio/mp3">
</audio>
"""
st.markdown(audio_html, unsafe_allow_html=True)
# Main function
def main():
st.title("Vocacity AI Voice Agent 🎙️")
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
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))
st.write(f"**AI Response:** {ai_response} (Emotion: {emotion})")
# Convert response to speech and autoplay it
audio_file = text_to_speech(ai_response, voice_option, 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
if clear_chat:
st.session_state.chat_history = []
st.experimental_rerun()
if __name__ == "__main__":
main()
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