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| import streamlit as st | |
| import json | |
| import pandas as pd | |
| import streamlit.components.v1 as components | |
| # Function to load JSONL file into a DataFrame | |
| def load_jsonl(file_path): | |
| data = [] | |
| with open(file_path, 'r') as f: | |
| for line in f: | |
| data.append(json.loads(line)) | |
| return pd.DataFrame(data) | |
| # Function to filter DataFrame by keyword | |
| def filter_by_keyword(df, keyword): | |
| return df[df.apply(lambda row: row.astype(str).str.contains(keyword).any(), axis=1)] | |
| # Function to generate HTML with textarea | |
| def generate_html_with_textarea(text_to_speak): | |
| return f''' | |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <title>Read It Aloud</title> | |
| <script type="text/javascript"> | |
| function readAloud() {{ | |
| const text = document.getElementById("textArea").value; | |
| const speech = new SpeechSynthesisUtterance(text); | |
| window.speechSynthesis.speak(speech); | |
| }} | |
| </script> | |
| </head> | |
| <body> | |
| <h1>🔊 Read It Aloud</h1> | |
| <textarea id="textArea" rows="10" cols="80"> | |
| {text_to_speak} | |
| </textarea> | |
| <br> | |
| <button onclick="readAloud()">🔊 Read Aloud</button> | |
| </body> | |
| </html> | |
| ''' | |
| # Streamlit App 🚀 | |
| st.title("USMLE Medical Questions Explorer with Speech Synthesis 🎙") | |
| # Dropdown for file selection | |
| file_option = st.selectbox("Select file:", ["usmle_16.2MB.jsonl", "usmle_2.08MB.jsonl"]) | |
| st.write(f"You selected: {file_option}") | |
| # Load data | |
| large_data = load_jsonl("usmle_16.2MB.jsonl") | |
| small_data = load_jsonl("usmle_2.08MB.jsonl") | |
| data = small_data if file_option == "usmle_16.2MB.jsonl" else small_data | |
| # Top 20 healthcare terms for USMLE | |
| top_20_terms = ['Heart', 'Lung', 'Pain', 'Memory', 'Kidney', 'Diabetes', 'Cancer', 'Infection', 'Virus', 'Bacteria', 'Neurology', 'Psychiatry', 'Gastrointestinal', 'Pediatrics', 'Oncology', 'Skin', 'Blood', 'Surgery', 'Epidemiology', 'Genetics'] | |
| # Create Expander and Columns UI for terms | |
| with st.expander("Search by Common Terms 📚"): | |
| cols = st.columns(4) | |
| for term in top_20_terms: | |
| with cols[top_20_terms.index(term) % 4]: | |
| if st.button(f"{term}"): | |
| filtered_data = filter_by_keyword(data, term) | |
| st.write(f"Filtered Dataset by '{term}' 📊") | |
| st.dataframe(filtered_data) | |
| # Text input for search keyword | |
| search_keyword = st.text_input("Or, enter a keyword to filter data:") | |
| if st.button("Search 🕵️♀️"): | |
| filtered_data = filter_by_keyword(data, search_keyword) | |
| st.write(f"Filtered Dataset by '{search_keyword}' 📊") | |
| st.dataframe(filtered_data) | |
| # Button to read all filtered rows | |
| if st.button("Read All Rows 📖"): | |
| if not filtered_data.empty: | |
| html_blocks = [] | |
| for idx, row in filtered_data.iterrows(): | |
| question_text = row.get("question", "No question field") | |
| documentHTML5 = generate_html_with_textarea(question_text) | |
| html_blocks.append(documentHTML5) | |
| all_html = ''.join(html_blocks) | |
| components.html(all_html, width=1280, height=1024) | |
| else: | |
| st.warning("No rows to read. 🚨") | |