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| import streamlit as st | |
| import json | |
| import pandas as pd | |
| import plotly.express as px | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| import streamlit.components.v1 as components | |
| # Global variable to hold selected row index | |
| selected_row_index = None | |
| # Initialize an empty DataFrame | |
| filtered_data = pd.DataFrame() | |
| # 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 HTML5 code with embedded text | |
| def generate_html(question_text, answer_text, idx): | |
| return f''' | |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <title>Read It Aloud</title> | |
| <script type="text/javascript"> | |
| function readAloud{id}() {{ | |
| const text = document.getElementById("questionArea{id}").innerText; | |
| const speech = new SpeechSynthesisUtterance(text); | |
| window.speechSynthesis.speak(speech); | |
| }} | |
| </script> | |
| </head> | |
| <body> | |
| <h1>🔊 Read It Aloud - Row {idx}</h1> | |
| <p id="questionArea{id}">{question_text}</p> | |
| <button onclick="readAloud{id}()">🔊 Read Question Aloud</button> | |
| </body> | |
| </html> | |
| ''' | |
| # Streamlit App | |
| st.title("Medical Licensing Exam Explorer with Speech Synthesis, Plotly and Seaborn 📊") | |
| # Dropdown for file selection | |
| file_option = st.selectbox("Select file:", ["small_file.jsonl", "large_file.jsonl"]) | |
| st.write(f"You selected: {file_option}") | |
| # Load the data | |
| small_data = load_jsonl("usmle_16.2MB.jsonl") | |
| large_data = load_jsonl("usmle_2.08MB.jsonl") | |
| # Show filtered data grid | |
| if file_option == "small_file.jsonl": | |
| data = small_data | |
| else: | |
| data = large_data | |
| # Text input for search keyword | |
| search_keyword = st.text_input("Enter a keyword to filter data (e.g., Heart, Lung, Pain, Memory):") | |
| # Button to trigger search | |
| if st.button("Search"): | |
| filtered_data = filter_by_keyword(data, search_keyword) | |
| st.write(f"Filtered Dataset by '{search_keyword}'") | |
| selected_data = st.dataframe(filtered_data) | |
| # Button to read all filtered rows | |
| if st.button("Read All Rows"): | |
| 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(question_text, "", idx) | |
| html_blocks.append(documentHTML5) | |
| all_html = ''.join(html_blocks) | |
| components.html(all_html, width=1280, height=1024) | |
| else: | |
| st.warning("No rows to read.") | |
| # Plotly and Seaborn charts for EDA | |
| if st.button("Generate Charts"): | |
| st.subheader("Plotly Charts 📈") | |
| # 1. Scatter Plot | |
| fig = px.scatter(data, x=data.columns[0], y=data.columns[1]) | |
| st.plotly_chart(fig) | |
| # 2. Line Plot | |
| fig = px.line(data, x=data.columns[0], y=data.columns[1]) | |
| st.plotly_chart(fig) | |
| # 3. Bar Plot | |
| fig = px.bar(data, x=data.columns[0], y=data.columns[1]) | |
| st.plotly_chart(fig) | |
| # 4. Histogram | |
| fig = px.histogram(data, x=data.columns[0]) | |
| st.plotly_chart(fig) | |
| # 5. Box Plot | |
| fig = px.box(data, x=data.columns[0], y=data.columns[1]) | |
| st.plotly_chart(fig) | |
| st.subheader("Seaborn Charts 📊") | |
| # 6. Violin Plot | |
| fig, ax = plt.subplots() | |
| sns.violinplot(x=data.columns[0], y=data.columns[1], data=data) | |
| st.pyplot(fig) | |
| # 7. Swarm Plot | |
| fig, ax = plt.subplots() | |
| sns.swarmplot(x=data.columns[0], y=data.columns[1], data=data) | |
| st.pyplot(fig) | |
| # 8. Pair Plot | |
| fig = sns.pairplot(data) | |
| st.pyplot(fig) | |
| # 9. Heatmap | |
| fig, ax = plt.subplots() | |
| sns.heatmap(data.corr(), annot=True) | |
| st.pyplot(fig) | |
| # 10. Regplot (Regression Plot) | |
| fig, ax = plt.subplots() | |
| sns.regplot(x=data.columns[0], y=data.columns[1], data=data) | |
| st.pyplot(fig) |