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| # chatbot_app.py | |
| import streamlit as st | |
| import openai | |
| import requests | |
| from gtts import gTTS | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import os | |
| from io import BytesIO | |
| from pydub import AudioSegment | |
| from pydub.playback import play | |
| import tempfile | |
| # Configure API keys | |
| HUGGING_FACE_API_KEY = "voicebot" | |
| OPENAI_API_KEY = "your_openai_api_key" | |
| openai.api_key = OPENAI_API_KEY | |
| # Initialize the Hugging Face model and tokenizer | |
| def load_model(): | |
| model_name = "facebook/musicgen-small" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=HUGGING_FACE_API_KEY) | |
| model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=HUGGING_FACE_API_KEY) | |
| return model, tokenizer | |
| model, tokenizer = load_model() | |
| # Function to convert voice to text using OpenAI's Whisper API | |
| def voice_to_text(audio_file): | |
| with open(audio_file, "rb") as file: | |
| transcript = openai.Audio.transcribe("whisper-1", file) | |
| return transcript["text"] | |
| # Function to generate chatbot response using Hugging Face's model | |
| def generate_response(prompt): | |
| inputs = tokenizer.encode(prompt, return_tensors="pt") | |
| outputs = model.generate(inputs, max_length=100, num_return_sequences=1) | |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return response | |
| # Function to convert text to voice using gTTS | |
| def text_to_speech(text): | |
| tts = gTTS(text, lang="en") | |
| temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") | |
| tts.save(temp_file.name) | |
| return temp_file.name | |
| # Streamlit app layout | |
| st.title("Voice-to-Text Chatbot") | |
| st.write("Speak to the chatbot and get responses in both text and audio!") | |
| # Upload audio file | |
| audio_file = st.file_uploader("Upload your voice input", type=["mp3", "wav", "ogg"]) | |
| if audio_file is not None: | |
| # Convert voice to text | |
| with open("input_audio.wav", "wb") as f: | |
| f.write(audio_file.read()) | |
| st.audio("input_audio.wav", format="audio/wav") | |
| # Get text from audio | |
| with st.spinner("Transcribing your voice..."): | |
| user_input = voice_to_text("input_audio.wav") | |
| st.write(f"**You said:** {user_input}") | |
| # Generate chatbot response | |
| with st.spinner("Generating response..."): | |
| response_text = generate_response(user_input) | |
| st.write(f"**Chatbot:** {response_text}") | |
| # Convert response to audio | |
| with st.spinner("Converting response to audio..."): | |
| response_audio = text_to_speech(response_text) | |
| audio_data = AudioSegment.from_mp3(response_audio) | |
| # Display audio response | |
| st.audio(response_audio, format="audio/mp3") | |
| # Play audio | |
| play(audio_data) | |
| # Clean up temporary files | |
| os.remove("input_audio.wav") | |
| os.remove(response_audio) | |
| else: | |
| st.write("Please upload an audio file to get started.") | |