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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() | |