Delete app.py

app.py

@@ -1,160 +0,0 @@
-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
-@st.cache_resource
-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")
-    
-    # Create tabs for different input methods
-    tab1, tab2 = st.tabs(["🎤 Record", "📁 Upload"])
-    
-    with tab1:
-        st.write("Record your conversation directly:")
-        st.write("⚠️ Please allow microphone access when prompted")
-        
-        if "audio_recorder_key" not in st.session_state:
-            st.session_state["audio_recorder_key"] = 0
-            
-        audio_bytes = st.audio_recorder(
-            text="Click to record",
-            recording_color="#e87676",
-            neutral_color="#6aa36f",
-            key=f"audio_recorder_{st.session_state.audio_recorder_key}"
-        )
-        
-        if st.button("Clear Recording"):
-            st.session_state.audio_recorder_key += 1
-            st.experimental_rerun()
-        
-        if audio_bytes:
-            # Display the recorded audio
-            st.audio(audio_bytes, format="audio/wav")
-            
-            # Save audio temporarily and process
-            with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmp_file:
-                tmp_file.write(audio_bytes)
-                process_audio(tmp_file.name)
-                # Clean up
-                os.unlink(tmp_file.name)
-    
-    with tab2:
-        st.write("Or upload a pre-recorded file:")
-        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)
-
-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. Choose your preferred input method:
-   - **Record**: Click the microphone button to start/stop recording directly in your browser
-   - **Upload**: Upload a pre-recorded WAV file
-2. Wait for the transcription and clinical notes to be generated
-3. Review the generated notes
-
-**Note**: For best results, ensure:
-- Clear audio quality
-- Minimal background noise
-- Proper microphone placement
-- Allow microphone access in your browser when prompted
-""")
-
-# 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)*")