|
import streamlit as st |
|
import json |
|
import pandas as pd |
|
import streamlit.components.v1 as components |
|
|
|
|
|
if 'last_clicked_row' not in st.session_state: |
|
st.session_state['last_clicked_row'] = None |
|
|
|
|
|
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) |
|
|
|
|
|
def filter_by_keyword(df, keyword): |
|
return df[df.apply(lambda row: row.astype(str).str.contains(keyword).any(), axis=1)] |
|
|
|
|
|
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> |
|
''' |
|
|
|
|
|
st.title("AI Medical Explorer with Speech Synthesis 🎙") |
|
|
|
|
|
file_option = st.selectbox("Select file:", ["usmle_16.2MB.jsonl", "usmle_2.08MB.jsonl"]) |
|
st.write(f"You selected: {file_option}") |
|
|
|
|
|
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_terms = ['Heart', 'Lung', 'Pain', 'Memory', 'Kidney', 'Diabetes', 'Cancer', 'Infection', 'Virus', 'Bacteria', 'Gastrointestinal', 'Skin', 'Blood', 'Surgery'] |
|
|
|
|
|
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(): |
|
|
|
question_number = idx + 1 |
|
question = row.get("question", "No question field") |
|
answer = row.get("answer", "No answer field") |
|
options = row.get("options", {}) |
|
|
|
|
|
options_text = "<br>".join([f"{key}: {value}" for key, value in options.items()]) |
|
|
|
|
|
question_text = (f"QuestionNumber: {question_number}<br>" |
|
f"Question: {question}<br>" |
|
f"Options: {options_text}<br>" |
|
f"Answer: {answer}") |
|
|
|
|
|
documentHTML5 = generate_html_with_textarea(question_text) |
|
html_blocks.append(documentHTML5) |
|
|
|
|
|
|
|
all_html = ''.join(html_blocks) |
|
components.html(all_html, width=1280, height=1024) |
|
|
|
|
|
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) |
|
|
|
|
|
|
|
|
|
st.markdown(""" |
|
<style> |
|
.big-font { |
|
font-size:24px !important; |
|
} |
|
</style> |
|
""", unsafe_allow_html=True) |
|
|
|
|
|
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 |
|
""") |
|
|
|
|
|
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. |
|
""") |
|
|
|
|
|
st.markdown("## 🤔 What is the best next step in management?") |
|
|
|
|
|
options = ["Blood Test", "MRI Scan", "Ultrasound with Doppler", "Immediate Surgery"] |
|
choice = st.selectbox("", options) |
|
|
|
|
|
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**. |
|
""") |
