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| import streamlit as st | |
| import json | |
| import pandas as pd | |
| import streamlit.components.v1 as components | |
| # Initialize session state for tracking the last clicked row | |
| if 'last_clicked_row' not in st.session_state: | |
| st.session_state['last_clicked_row'] = None | |
| # 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("AI Medical 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 = large_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', 'Gastrointestinal', 'Skin', 'Blood', 'Surgery'] | |
| # 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"Filter on '{term}' 📊") | |
| with st.sidebar: | |
| st.dataframe(filtered_data) | |
| if not filtered_data.empty: | |
| html_blocks = [] | |
| for idx, row in filtered_data.iterrows(): | |
| # Extracting fields from the row | |
| question_number = idx + 1 # Assuming idx represents the question number | |
| question = row.get("question", "No question field") | |
| answer = row.get("answer", "No answer field") | |
| options = row.get("options", {}) | |
| # Formatting options dictionary into a string | |
| options_text = "<br>".join([f"{key}: {value}" for key, value in options.items()]) | |
| # Concatenating with labels | |
| question_text = (f"QuestionNumber: {question_number}<br>" | |
| f"Question: {question}<br>" | |
| f"Options: {options_text}<br>" | |
| f"Answer: {answer}") | |
| # Generating HTML content | |
| documentHTML5 = generate_html_with_textarea(question_text) | |
| html_blocks.append(documentHTML5) | |
| all_html = ''.join(html_blocks) | |
| components.html(all_html, width=1280, height=1024) | |
| # 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) | |
| 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) | |
| # Inject HTML5 and JavaScript for styling | |
| st.markdown(""" | |
| <style> | |
| .big-font { | |
| font-size:24px !important; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # Markdown and emojis for the case presentation | |
| st.markdown("# 🏥 Case Study: 32-year-old Woman's Wellness Check") | |
| st.markdown("## 📋 Patient Information") | |
| st.markdown(""" | |
| - **Age**: 32 | |
| - **Gender**: Female | |
| - **Past Medical History**: Asthma, Hypertension, Anxiety | |
| - **Current Medications**: Albuterol, Fluticasone, Hydrochlorothiazide, Lisinopril, Fexofenadine | |
| - **Vitals** | |
| - **Temperature**: 99.5°F (37.5°C) | |
| - **Blood Pressure**: 165/95 mmHg | |
| - **Pulse**: 70/min | |
| - **Respirations**: 15/min | |
| - **Oxygen Saturation**: 98% on room air | |
| """) | |
| # Clinical Findings | |
| st.markdown("## 📋 Clinical Findings") | |
| st.markdown(""" | |
| - Cardiac exam reveals a S1 and S2 heart sound with a normal rate. | |
| - Pulmonary exam is clear to auscultation bilaterally with good air movement. | |
| - Abdominal exam reveals a bruit, normoactive bowel sounds, and an audible borborygmus. | |
| - Neurological exam reveals cranial nerves II-XII as grossly intact with normal strength and reflexes in the upper and lower extremities. | |
| """) | |
| # Next Step Options | |
| st.markdown("## 🤔 What is the best next step in management?") | |
| # Multiple Choice | |
| options = ["Blood Test", "MRI Scan", "Ultrasound with Doppler", "Immediate Surgery"] | |
| choice = st.selectbox("", options) | |
| # Explanation | |
| if st.button("Submit"): | |
| if choice == "Ultrasound with Doppler": | |
| st.success("Correct! 🎉") | |
| st.markdown(""" | |
| ### Explanation | |
| The patient's high blood pressure coupled with an abdominal bruit suggests the possibility of renal artery stenosis. | |
| An **Ultrasound with Doppler** is the best next step for assessing blood flow and evaluating for renal artery stenosis. | |
| """) | |
| else: | |
| st.error("Incorrect. 😞") | |
| st.markdown(""" | |
| The best next step is **Ultrasound with Doppler**. | |
| """) |