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| import streamlit as st | |
| from transformers import pipeline | |
| import whisper | |
| from gtts import gTTS | |
| import tempfile | |
| import os | |
| import logging | |
| from pydub import AudioSegment | |
| import openai | |
| # Set up logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| # Load Hugging Face model for text generation (instead of Google Cloud) | |
| def load_hf_model(): | |
| # Load a model for heart health-related questions | |
| return pipeline("text-generation", model="gpt2") | |
| # Load Whisper model for transcription | |
| def load_whisper_model(): | |
| return whisper.load_model("base") | |
| # Function to generate response using Hugging Face model | |
| def generate_hf_response(model, prompt): | |
| result = model(prompt, max_length=100, num_return_sequences=1) | |
| return result[0]["generated_text"] | |
| # Function to process audio input using Whisper and Hugging Face | |
| def process_audio(audio_file, hf_model, whisper_model): | |
| try: | |
| # Transcribe audio using Whisper | |
| result = whisper_model.transcribe(audio_file) | |
| user_text = result['text'] | |
| logger.info(f"Transcription successful: {user_text}") | |
| except Exception as e: | |
| logger.error(f"Error in transcribing audio: {e}") | |
| return "Error in transcribing audio.", None | |
| try: | |
| # Generate response using Hugging Face model | |
| response_text = generate_hf_response(hf_model, user_text) | |
| logger.info(f"Generated response: {response_text}") | |
| except Exception as e: | |
| logger.error(f"Error in generating response: {e}") | |
| return "Error in generating response.", None | |
| try: | |
| # Convert the response text to speech | |
| tts = gTTS(text=response_text, lang='en') | |
| audio_file = tempfile.NamedTemporaryFile(delete=False, suffix='.mp3') | |
| tts.save(audio_file.name) | |
| logger.info("Text-to-speech conversion successful.") | |
| except Exception as e: | |
| logger.error(f"Error in text-to-speech conversion: {e}") | |
| return "Error in text-to-speech conversion.", None | |
| return response_text, audio_file.name | |
| # Main application layout | |
| def main(): | |
| st.title("Heart Health & Audio Processing App 🫀🎙️ (Hugging Face Edition)") | |
| # Load models | |
| hf_model = load_hf_model() | |
| whisper_model = load_whisper_model() | |
| # Two tabs: one for the chatbot and one for audio processing | |
| tab1, tab2 = st.tabs(["Heart Health Chatbot", "Audio Processing"]) | |
| # Tab 1: Heart Health Chatbot | |
| with tab1: | |
| st.header("Chat with Heart Health Specialist") | |
| if "history" not in st.session_state: | |
| st.session_state.history = [] | |
| user_input = st.text_input("Ask about heart health:", placeholder="Type here...") | |
| if st.button("Send") and user_input: | |
| bot_response = generate_hf_response(hf_model, user_input) | |
| st.session_state.history.append({"role": "user", "content": user_input}) | |
| st.session_state.history.append({"role": "bot", "content": bot_response}) | |
| for chat in st.session_state.history: | |
| if chat["role"] == "user": | |
| st.write(f"**You:** {chat['content']}") | |
| else: | |
| st.write(f"**Bot:** {chat['content']}") | |
| # Tab 2: Audio Processing | |
| with tab2: | |
| st.header("Audio Processing with Whisper and Hugging Face") | |
| uploaded_audio = st.file_uploader("Upload an audio file for transcription and response", type=["mp3", "wav", "ogg"]) | |
| if uploaded_audio: | |
| with st.spinner("Processing audio..."): | |
| response_text, audio_file_path = process_audio(uploaded_audio, hf_model, whisper_model) | |
| if response_text: | |
| st.write(f"**Response:** {response_text}") | |
| st.audio(audio_file_path) | |
| # Run the app | |
| if __name__ == "__main__": | |
| main() | |