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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 plotly.express as px |
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import seaborn as sns |
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import matplotlib.pyplot as plt |
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import streamlit.components.v1 as components |
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selected_row_index = None |
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filtered_data = pd.DataFrame() |
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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(question_text, answer_text): |
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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(id) {{ |
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const text = document.getElementById(id).innerText; |
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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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<p id="questionArea">{question_text}</p> |
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<button onclick="readAloud('questionArea')">🔊 Read Question Aloud</button> |
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<p id="answerArea">{answer_text}</p> |
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<button onclick="readAloud('answerArea')">🔊 Read Answer Aloud</button> |
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</body> |
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</html> |
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''' |
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st.title("Medical Licensing Exam Explorer with Speech Synthesis, Plotly and Seaborn 📊") |
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file_option = st.selectbox("Select file:", ["small_file.jsonl", "large_file.jsonl"]) |
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st.write(f"You selected: {file_option}") |
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small_data = load_jsonl("usmle_16.2MB.jsonl") |
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large_data = load_jsonl("usmle_2.08MB.jsonl") |
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if file_option == "small_file.jsonl": |
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data = small_data |
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else: |
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data = large_data |
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file_option = st.selectbox("Select file:", ["small_file.jsonl", "large_file.jsonl"]) |
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st.write(f"You selected: {file_option}") |
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if file_option == "small_file.jsonl": |
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data = small_data |
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else: |
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data = large_data |
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search_keyword = st.text_input("Enter a keyword to filter data (e.g., Heart, Lung, Pain, Memory):") |
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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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selected_data = st.dataframe(filtered_data) |
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if st.button("Read Selected Row"): |
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global selected_row_index |
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if selected_row_index is not None: |
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selected_row = filtered_data.loc[selected_row_index] |
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question_text = selected_row.get("question", "No question field") |
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answer_text = selected_row.get("answer", "No answer field") |
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documentHTML5 = generate_html(question_text, answer_text) |
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components.html(documentHTML5, width=1280, height=1024) |
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else: |
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st.warning("Please select a row first.") |
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if st.button("Generate Charts"): |
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st.subheader("Plotly Charts 📈") |
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fig = px.scatter(data, x=data.columns[0], y=data.columns[1]) |
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st.plotly_chart(fig) |
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fig = px.line(data, x=data.columns[0], y=data.columns[1]) |
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st.plotly_chart(fig) |
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fig = px.bar(data, x=data.columns[0], y=data.columns[1]) |
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st.plotly_chart(fig) |
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fig = px.histogram(data, x=data.columns[0]) |
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st.plotly_chart(fig) |
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fig = px.box(data, x=data.columns[0], y=data.columns[1]) |
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st.plotly_chart(fig) |
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st.subheader("Seaborn Charts 📊") |
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fig, ax = plt.subplots() |
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sns.violinplot(x=data.columns[0], y=data.columns[1], data=data) |
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st.pyplot(fig) |
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fig, ax = plt.subplots() |
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sns.swarmplot(x=data.columns[0], y=data.columns[1], data=data) |
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st.pyplot(fig) |
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fig = sns.pairplot(data) |
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st.pyplot(fig) |
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fig, ax = plt.subplots() |
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sns.heatmap(data.corr(), annot=True) |
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st.pyplot(fig) |
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fig, ax = plt.subplots() |
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sns.regplot(x=data.columns[0], y=data.columns[1], data=data) |
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st.pyplot(fig) |
