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| import streamlit as st | |
| import whisper | |
| import requests | |
| import json | |
| from datetime import datetime | |
| import os | |
| from dotenv import load_dotenv | |
| import numpy as np | |
| from io import BytesIO | |
| import tempfile | |
| # Load environment variables - try both .env and system environment | |
| load_dotenv() | |
| HUGGINGFACE_TOKEN = os.getenv('HUGGINGFACE_TOKEN') | |
| if not HUGGINGFACE_TOKEN: | |
| st.error("Please set the HUGGINGFACE_TOKEN in your Space's secrets.") | |
| st.stop() | |
| # Initialize Whisper model | |
| def load_whisper_model(): | |
| return whisper.load_model("base") | |
| def process_audio(audio_file): | |
| """Process audio file and generate transcription and clinical notes""" | |
| try: | |
| with st.spinner("Transcribing audio..."): | |
| # Transcribe audio | |
| model = load_whisper_model() | |
| result = model.transcribe(audio_file) | |
| st.session_state.transcription = result["text"] | |
| with st.spinner("Generating clinical notes..."): | |
| # Generate clinical notes | |
| st.session_state.clinical_notes = get_clinical_notes(st.session_state.transcription) | |
| except Exception as e: | |
| st.error(f"Error processing audio: {str(e)}") | |
| # Mixtral API call function | |
| def get_clinical_notes(transcription): | |
| API_URL = "https://api-inference.huggingface.co/models/mistralai/Mixtral-8x7B-Instruct-v0.1" | |
| headers = {"Authorization": f"Bearer {HUGGINGFACE_TOKEN}"} | |
| messages = [ | |
| {"role": "system", "content": "You are a medical assistant helping to generate clinical notes from doctor-patient conversations. Format the notes in a clear, professional structure with the following sections: Chief Complaint, History of Present Illness, Review of Systems, Physical Examination, Assessment, and Plan."}, | |
| {"role": "user", "content": f"Generate clinical notes from this doctor-patient conversation: {transcription}"} | |
| ] | |
| payload = { | |
| "model": "mistralai/Mixtral-8x7B-Instruct-v0.1", | |
| "messages": messages, | |
| "max_tokens": 500, | |
| "stream": False | |
| } | |
| try: | |
| response = requests.post(API_URL, headers=headers, json=payload) | |
| response.raise_for_status() | |
| return response.json()['choices'][0]['message']['content'] | |
| except Exception as e: | |
| st.error(f"Error generating clinical notes: {str(e)}") | |
| return None | |
| # Main app | |
| st.set_page_config( | |
| page_title="AI Clinical Notes", | |
| page_icon="🦀", | |
| layout="wide" | |
| ) | |
| st.title("🦀 AI Clinical Notes") | |
| st.markdown("### Created by Dr. Fernando Ly") | |
| st.markdown("This application helps medical professionals automatically generate clinical notes from patient conversations.") | |
| # Create columns for better layout | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.subheader("Audio Input") | |
| # File upload section | |
| st.write("Upload your recorded conversation:") | |
| uploaded_file = st.file_uploader("Upload an audio file (WAV format)", type=['wav']) | |
| if uploaded_file: | |
| # Display the uploaded audio | |
| st.audio(uploaded_file, format="audio/wav") | |
| # Save audio temporarily | |
| with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmp_file: | |
| tmp_file.write(uploaded_file.getvalue()) | |
| process_audio(tmp_file.name) | |
| # Clean up | |
| os.unlink(tmp_file.name) | |
| # Add recording instructions | |
| st.markdown("---") | |
| st.markdown(""" | |
| ### Recording Instructions | |
| To record your conversation: | |
| 1. Use your phone's voice recorder app | |
| 2. Save the recording as a WAV file | |
| 3. Upload it here | |
| Alternatively, you can use these free tools: | |
| - Windows: Voice Recorder app | |
| - Mac: QuickTime Player | |
| - Online: [Vocaroo](https://vocaroo.com) (saves directly as WAV) | |
| """) | |
| with col2: | |
| # Display results | |
| if "transcription" in st.session_state and st.session_state.transcription: | |
| st.subheader("📝 Transcription") | |
| st.write(st.session_state.transcription) | |
| if "clinical_notes" in st.session_state and st.session_state.clinical_notes: | |
| st.subheader("🏥 Clinical Notes") | |
| st.write(st.session_state.clinical_notes) | |
| # Instructions | |
| st.markdown("---") | |
| st.markdown(""" | |
| ### How to use: | |
| 1. Record your conversation using any recording app | |
| 2. Save the recording as a WAV file | |
| 3. Upload the file using the uploader above | |
| 4. Wait for the transcription and clinical notes to be generated | |
| 5. Review the generated notes | |
| **Note**: For best results, ensure: | |
| - Clear audio quality | |
| - Minimal background noise | |
| - Proper microphone placement during recording | |
| """) | |
| # Footer | |
| st.markdown("---") | |
| st.markdown("*Note: This is an AI-assisted tool. Please review and verify all generated notes.*") | |
| st.markdown("*For issues or feedback, please visit the [GitHub repository](https://huggingface.co/spaces/fernandoly/AI-Clinical-Notes/discussions)*") |