Upload vocacity.py

vocacity.py

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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()