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import streamlit as st |
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import json |
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import pandas as pd |
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import streamlit.components.v1 as components |
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if 'last_clicked_row' not in st.session_state: |
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st.session_state['last_clicked_row'] = None |
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def load_jsonl(file_path): |
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data = [] |
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with open(file_path, 'r') as f: |
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for line in f: |
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data.append(json.loads(line)) |
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return pd.DataFrame(data) |
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def filter_by_keyword(df, keyword): |
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return df[df.apply(lambda row: row.astype(str).str.contains(keyword).any(), axis=1)] |
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def generate_html_with_textarea(text_to_speak): |
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return f''' |
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<!DOCTYPE html> |
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<html> |
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<head> |
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<title>Read It Aloud</title> |
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<script type="text/javascript"> |
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function readAloud() {{ |
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const text = document.getElementById("textArea").value; |
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const speech = new SpeechSynthesisUtterance(text); |
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window.speechSynthesis.speak(speech); |
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}} |
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</script> |
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</head> |
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<body> |
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<h1>🔊 Read It Aloud</h1> |
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<textarea id="textArea" rows="10" cols="80">{text_to_speak}</textarea> |
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<br> |
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<button onclick="readAloud()">🔊 Read Aloud</button> |
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</body> |
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</html> |
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''' |
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st.title("2🎓Med💡Speech") |
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file_option = st.selectbox("Select file:", ["usmle_16.2MB.jsonl", "usmle_2.08MB.jsonl"]) |
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st.write(f"You selected: {file_option}") |
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large_data = load_jsonl("usmle_16.2MB.jsonl") |
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small_data = load_jsonl("usmle_2.08MB.jsonl") |
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data = large_data if file_option == "usmle_16.2MB.jsonl" else small_data |
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top_20_terms = ['Melanoma', 'carcinoma','Constipation', 'Colon', 'Muscle', 'Rehabilitation', 'Heart', 'Lung', 'Pain', 'Memory', 'Kidney', 'Diabetes', 'Cancer', 'Infection', 'Virus', 'Bacteria', 'Gastrointestinal', 'Skin', 'Blood', 'Surgery'] |
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with st.expander("Search by Common Terms 📚"): |
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cols = st.columns(4) |
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for term in top_20_terms: |
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with cols[top_20_terms.index(term) % 4]: |
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if st.button(f"{term}"): |
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filtered_data = filter_by_keyword(data, term) |
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st.write(f"Filter on '{term}' 📊") |
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with st.sidebar: |
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st.dataframe(filtered_data) |
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if not filtered_data.empty: |
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html_blocks = [] |
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for idx, row in filtered_data.iterrows(): |
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question_number = idx + 1 |
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question = row.get("question", "No question field") |
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answer = row.get("answer", "No answer field") |
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options = row.get("options", {}) |
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options_text = "".join([f"{key}: {value}" for key, value in options.items()]) |
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question_text = (f"QuestionNumber: {question_number}\r\n" |
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f"Question: {question}\r\n" |
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f"Options: {options_text}\r\n" |
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f"Answer: {answer}") |
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documentHTML5 = generate_html_with_textarea(question_text) |
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html_blocks.append(documentHTML5) |
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all_html = ''.join(html_blocks) |
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components.html(all_html, width=1280, height=1024) |
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search_keyword = st.text_input("Or, enter a keyword to filter data:") |
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if st.button("Search 🕵️♀️"): |
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filtered_data = filter_by_keyword(data, search_keyword) |
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st.write(f"Filtered Dataset by '{search_keyword}' 📊") |
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st.dataframe(filtered_data) |
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if not filtered_data.empty: |
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html_blocks = [] |
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for idx, row in filtered_data.iterrows(): |
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question_text = row.get("question", "No question field") |
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documentHTML5 = generate_html_with_textarea(question_text) |
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html_blocks.append(documentHTML5) |
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all_html = ''.join(html_blocks) |
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components.html(all_html, width=1280, height=1024) |
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st.markdown(""" |
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<style> |
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.big-font { |
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font-size:24px !important; |
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} |
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</style> |
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""", unsafe_allow_html=True) |
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st.markdown("# 🏥 Case Study: 32-year-old Woman's Wellness Check") |
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st.markdown("## 📋 Patient Information") |
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st.markdown(""" |
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- **Age**: 32 |
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- **Gender**: Female |
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- **Past Medical History**: Asthma, Hypertension, Anxiety |
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- **Current Medications**: Albuterol, Fluticasone, Hydrochlorothiazide, Lisinopril, Fexofenadine |
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- **Vitals** |
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- **Temperature**: 99.5°F (37.5°C) |
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- **Blood Pressure**: 165/95 mmHg |
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- **Pulse**: 70/min |
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- **Respirations**: 15/min |
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- **Oxygen Saturation**: 98% on room air |
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""") |
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st.markdown("## 📋 Clinical Findings") |
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st.markdown(""" |
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- Cardiac exam reveals a S1 and S2 heart sound with a normal rate. |
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- Pulmonary exam is clear to auscultation bilaterally with good air movement. |
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- Abdominal exam reveals a bruit, normoactive bowel sounds, and an audible borborygmus. |
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- Neurological exam reveals cranial nerves II-XII as grossly intact with normal strength and reflexes in the upper and lower extremities. |
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""") |
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st.markdown("## 🤔 What is the best next step in management?") |
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options = ["Blood Test", "MRI Scan", "Ultrasound with Doppler", "Immediate Surgery"] |
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choice = st.selectbox("", options) |
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if st.button("Submit"): |
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if choice == "Ultrasound with Doppler": |
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st.success("Correct! 🎉") |
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st.markdown(""" |
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### Explanation |
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The patient's high blood pressure coupled with an abdominal bruit suggests the possibility of renal artery stenosis. |
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An **Ultrasound with Doppler** is the best next step for assessing blood flow and evaluating for renal artery stenosis. |
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""") |
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else: |
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st.error("Incorrect. 😞") |
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st.markdown(""" |
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The best next step is **Ultrasound with Doppler**. |
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""") |
