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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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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"> |
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{text_to_speak} |
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</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("USMLE Medical Questions Explorer with Speech Synthesis 🎙") |
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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 = small_data if file_option == "usmle_16.2MB.jsonl" else small_data |
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top_20_terms = ['Heart', 'Lung', 'Pain', 'Memory', 'Kidney', 'Diabetes', 'Cancer', 'Infection', 'Virus', 'Bacteria', 'Neurology', 'Psychiatry', 'Gastrointestinal', 'Pediatrics', 'Oncology', 'Skin', 'Blood', 'Surgery', 'Epidemiology', 'Genetics'] |
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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"Filtered Dataset by '{term}' 📊") |
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st.dataframe(filtered_data) |
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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 st.button("Read All Rows 📖"): |
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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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else: |
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st.warning("No rows to read. 🚨") |
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