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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>
'''
# 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
# 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']
# 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
# Expander UI for common 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:
# Display each row with a button
for idx, row in filtered_data.iterrows():
if st.button(f"Row {idx}", key=f"row_{idx}"):
st.session_state['last_clicked_row'] = idx
# Check if 'last_clicked_row' is set and in the filtered data
if st.session_state['last_clicked_row'] is not None and st.session_state['last_clicked_row'] in filtered_data.index:
selected_row = filtered_data.loc[st.session_state['last_clicked_row']]
# Extract only the first three columns of the selected row
first_three_columns = selected_row.iloc[:3]
# Display these columns in the sidebar
st.sidebar.write("Selected Row Details:")
for col in first_three_columns.index:
st.sidebar.write(f"{col}: {first_three_columns[col]}")
# Concatenate these column values with the question_text
additional_info = ' '.join([f"{col}: {val}" for col, val in first_three_columns.items()])
question_text = selected_row.get("question", "No question field")
full_text = f"{additional_info} Question: {question_text}"
documentHTML5 = generate_html_with_textarea(full_text)
components.html(documentHTML5, 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**.
""")
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