Spaces:
Sleeping
Sleeping
| import torch | |
| try: | |
| print(f"Is CUDA available: {torch.cuda.is_available()}") | |
| if torch.cuda.is_available(): | |
| try: | |
| print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}") | |
| except Exception as e: | |
| print(f"Error getting CUDA device name: {str(e)}") | |
| else: | |
| print("No CUDA device available - using CPU") | |
| except Exception as e: | |
| print(f"Error checking CUDA availability: {str(e)}") | |
| print("Continuing with CPU...") | |
| import streamlit as st | |
| import os | |
| from huggingface_hub import login | |
| from datetime import datetime | |
| from modules.auth import validate_login | |
| from modules.utils import create_excel, clean_text, extract_predicted_labels, predict_category, process_data | |
| from modules.logging_config import setup_logging | |
| setup_logging() | |
| import logging | |
| from io import BytesIO | |
| logger = logging.getLogger(__name__) | |
| # Local | |
| # from dotenv import load_dotenv | |
| # load_dotenv() | |
| # Main app logic | |
| def main(): | |
| # Temporarily set authentication to True for testing | |
| if 'authenticated' not in st.session_state: | |
| st.session_state['authenticated'] = False | |
| if st.session_state['authenticated']: | |
| # Remove login success message for testing | |
| hf_token = os.environ["HF_TOKEN"] | |
| login(token=hf_token, add_to_git_credential=True) | |
| # Initialize session state variables | |
| if 'data_processed' not in st.session_state: | |
| st.session_state['data_processed'] = False | |
| st.session_state['df'] = None | |
| # Main Streamlit app | |
| st.title('Application Pre-Filtering Tool') | |
| # Sidebar (filters) | |
| with st.sidebar: | |
| with st.expander("ℹ️ - Instructions", expanded=False): | |
| st.markdown( | |
| """ | |
| 1. **Download the Excel Template file (below)** | |
| 2. **[OPTIONAL]: Select the desired filtering sensitivity level (below)** | |
| 3. **Copy/paste the requisite application data in the template file. Best practice is to 'paste as values'** | |
| 4. **Upload the template file in the area to the right (or click browse files)** | |
| 5. **Click 'Start Analysis'** | |
| The tool will start processing the uploaded application data. This can take some time | |
| depending on the number of applications and the length of text in each. For example, a file with 1000 applications | |
| could be expected to take approximately 5 minutes. | |
| ***NOTE** - you can also simply rename the column headers in your own file. The headers must match the column names in the template for the tool to run properly.* | |
| """ | |
| ) | |
| # Excel file download | |
| st.download_button( | |
| label="Download Excel Template", | |
| data=create_excel(), | |
| file_name="upload_template.xlsx", | |
| mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" | |
| ) | |
| # get sensitivity level for use in review / reject (ref. process_data function) | |
| sens_options = { | |
| "Low": 4, | |
| "Medium": 5, | |
| "High": 6, | |
| } | |
| sens_input = st.sidebar.radio(label = 'Select the Sensitivity Level [OPTIONAL]', | |
| help = 'Decreasing the level of sensitivity results in less \ | |
| applications filtered out. This also \ | |
| reduces the probability of false negatives (FNs). The rate of \ | |
| FNs at the lowest setting is approximately 6 percent, and \ | |
| approaches 13 percent at the highest setting. \ | |
| NOTE: changing this setting does not affect the raw data in the CSV output file (only the labels)', | |
| options = list(sens_options.keys()), | |
| index = list(sens_options.keys()).index("High"), | |
| horizontal = False) | |
| sens_level = sens_options[sens_input] | |
| with st.expander("ℹ️ - About this app", expanded=False): | |
| st.write( | |
| """ | |
| This tool provides an interface for running an automated preliminary assessment of applications for a call for applications. | |
| The tool functions by running selected text fields from the application through a series of LLMs fine-tuned for text classification (ref. diagram below). | |
| The resulting output classifications are used to compute a score and a suggested pre-filtering action. The tool has been tested against | |
| human assessors and exhibits an extremely low false negative rate (<6%) at a Sensitivity Level of 'Low' (i.e. rejection threshold for predicted score < 4). | |
| """) | |
| st.image('images/pipeline.png') | |
| uploaded_file = st.file_uploader("Select a file containing application pre-filtering data (see instructions in the sidebar)") | |
| # Add session state variables if they don't exist | |
| if 'show_button' not in st.session_state: | |
| st.session_state['show_button'] = True | |
| if 'processing' not in st.session_state: | |
| st.session_state['processing'] = False | |
| if 'data_processed' not in st.session_state: | |
| st.session_state['data_processed'] = False | |
| # Only show the button if show_button is True and file is uploaded and not processing | |
| if uploaded_file is not None and st.session_state['show_button'] and not st.session_state['processing']: | |
| if st.button("Start Analysis", key="start_analysis"): | |
| st.session_state['show_button'] = False | |
| st.session_state['processing'] = True | |
| st.rerun() | |
| # If we're processing, show the processing logic | |
| if st.session_state['processing']: | |
| try: | |
| logger.info(f"File uploaded: {uploaded_file.name}") | |
| if not st.session_state['data_processed']: | |
| logger.info("Starting data processing") | |
| try: | |
| st.session_state['df'] = process_data(uploaded_file, sens_level) | |
| logger.info("Data processing completed successfully") | |
| st.session_state['data_processed'] = True | |
| except ValueError as e: | |
| # Handle specific validation errors | |
| logger.error(f"Validation error: {str(e)}") | |
| st.error(str(e)) | |
| st.session_state['show_button'] = True | |
| st.session_state['processing'] = False | |
| st.rerun() | |
| except Exception as e: | |
| # Handle other unexpected errors | |
| logger.error(f"Error in process_data: {str(e)}") | |
| st.error("An unexpected error occurred. Please check your input file and try again.") | |
| st.session_state['show_button'] = True | |
| st.session_state['processing'] = False | |
| st.rerun() | |
| df = st.session_state['df'] | |
| def reset_button_state(): | |
| st.session_state['show_button'] = True | |
| st.session_state['processing'] = False | |
| st.session_state['data_processed'] = False | |
| # Create Excel buffer | |
| excel_buffer = BytesIO() | |
| df.to_excel(excel_buffer, index=False, engine='openpyxl') | |
| excel_buffer.seek(0) | |
| current_datetime = datetime.now().strftime('%d-%m-%Y_%H-%M-%S') | |
| output_filename = f'processed_applications_{current_datetime}.xlsx' | |
| st.download_button( | |
| label="Download Analysis Data File", | |
| data=excel_buffer, | |
| file_name=output_filename, | |
| mime='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', | |
| on_click=reset_button_state | |
| ) | |
| except Exception as e: | |
| logger.error(f"Error processing file: {str(e)}") | |
| st.error("Failed to process the file. Please ensure your column names match the template file.") | |
| st.session_state['show_button'] = True | |
| st.session_state['processing'] = False | |
| st.rerun() | |
| # Comment out for testing | |
| else: | |
| username = st.text_input("Username") | |
| password = st.text_input("Password", type="password") | |
| if st.button("Login"): | |
| if validate_login(username, password): | |
| st.session_state['authenticated'] = True | |
| st.rerun() | |
| else: | |
| st.error("Incorrect username or password") | |
| main() | |