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README.md

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+---
+title: AI Clinical Notes
+emoji: 🦀
+colorFrom: yellow
+colorTo: yellow
+sdk: streamlit
+sdk_version: 1.42.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# AI Clinical Notes
+
+Created by Dr. Fernando Ly
+
+## Description
+This application helps medical professionals automatically generate clinical notes from doctor-patient conversations. It uses:
+- Whisper for speech-to-text transcription
+- Mixtral-8x7B for clinical note generation
+- Streamlit for the user interface
+
+## Features
+- Real-time audio recording
+- Automatic speech transcription
+- AI-powered clinical note generation
+- Clean and intuitive interface
+
+## Setup
+
+### System Dependencies
+First, install the required system dependencies:
+
+For Ubuntu/Debian:
+```bash
+sudo apt-get update
+sudo apt-get install portaudio19-dev python3-pyaudio libasound2-dev
+```
+
+For other systems, check the PortAudio documentation for installation instructions.
+
+### Python Dependencies
+1. Install the required Python dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+2. Make sure you have a `.env` file with your Huggingface API token:
+```
+HUGGINGFACE_TOKEN=your_token_here
+```
+
+3. Run the application:
+```bash
+streamlit run app.py
+```
+
+## Usage
+1. Open the application in your web browser
+2. Set the desired recording duration using the slider
+3. Click "Start Recording" to begin capturing the conversation
+4. Wait for the transcription and clinical notes to be generated
+5. Review and verify the generated notes
+
+## Important Notes
+- This is an AI-assisted tool. Always review and verify the generated notes
+- Ensure you have proper consent before recording conversations
+- Keep patient privacy and HIPAA compliance in mind when using this tool
+
+## License
+This project is licensed under the MIT License. 

app.py

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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
+
+# 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
+@st.cache_resource
+def load_whisper_model():
+    return whisper.load_model("base")
+
+# 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("Recording Controls")
+    # Audio recorder
+    audio_bytes = st.audio_recorder(
+        text="Click to record",
+        recording_color="#e87676",
+        neutral_color="#6aa36f",
+        icon_name="microphone"
+    )
+    
+    if audio_bytes:
+        try:
+            # Save audio temporarily
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            filename = f"recording_{timestamp}.wav"
+            with open(filename, "wb") as f:
+                f.write(audio_bytes)
+            
+            with st.spinner("Transcribing audio..."):
+                # Transcribe audio
+                model = load_whisper_model()
+                result = model.transcribe(filename)
+                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)
+            
+            # Clean up audio file
+            os.remove(filename)
+            
+        except Exception as e:
+            st.error(f"Error processing audio: {str(e)}")
+
+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)
+
+# 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)*") 

requirements.txt

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+streamlit==1.42.0
+openai-whisper==20231117
+transformers==4.38.2
+torch==2.2.0
+requests==2.31.0
+numpy==1.26.4
+python-dotenv==1.0.1