app.py

import streamlit as st
import requests
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from gtts import gTTS
import os
import tempfile
import speech_recognition as sr

# Set your Hugging Face API key
HUGGING_FACE_API_KEY = "voicebot"

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("declare-lab/tango-full")
model = AutoModelForCausalLM.from_pretrained("declare-lab/tango-full")

# Function to get a response from the chatbot
def get_response(input_text):
    inputs = tokenizer.encode(input_text, return_tensors='pt')
    response_ids = model.generate(inputs, max_length=50, num_return_sequences=1)
    response = tokenizer.decode(response_ids[0], skip_special_tokens=True)
    return response

# Function to convert text to speech
def text_to_speech(text):
    tts = gTTS(text=text, lang='en')
    with tempfile.NamedTemporaryFile(delete=True) as fp:
        tts.save(f"{fp.name}.mp3")
        os.system(f"start {fp.name}.mp3")  # For Windows, use 'open' for macOS

# Speech Recognition Function
def recognize_speech():
    r = sr.Recognizer()
    with sr.Microphone() as source:
        st.write("Listening...")
        audio = r.listen(source)
        st.write("Recognizing...")
        try:
            text = r.recognize_google(audio)
            st.success(f"You said: {text}")
            return text
        except sr.UnknownValueError:
            st.error("Sorry, I could not understand the audio.")
            return None
        except sr.RequestError:
            st.error("Could not request results from Google Speech Recognition service.")
            return None

# Streamlit Interface
st.title("Voice-to-Text Chatbot")

# Recognize speech
if st.button("Speak"):
    user_input = recognize_speech()
else:
    user_input = st.text_input("Type your message here:")

# Display response and convert to speech
if user_input:
    st.write("You: ", user_input)
    chatbot_response = get_response(user_input)
    st.write("Chatbot: ", chatbot_response)
    text_to_speech(chatbot_response)