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RT-MLE

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awacke1 commited on Dec 5, 2023

Commit

415e3b3

1 Parent(s): 2e511c0

Update app.py

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Files changed (1) hide show

app.py +36 -22

app.py CHANGED Viewed

@@ -3,6 +3,10 @@ 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 = []
@@ -16,8 +20,7 @@ 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(row):
-    first_three_columns_text = ' '.join([f"{col}: {row[col]}" for col in row.index[:3]])
     return f'''
     <!DOCTYPE html>
     <html>
@@ -34,7 +37,7 @@ def generate_html_with_textarea(row):
     <body>
         <h1>🔊 Read It Aloud</h1>
         <textarea id="textArea" rows="10" cols="80">
-    {first_three_columns_text}
         </textarea>
         <br>
         <button onclick="readAloud()">🔊 Read Aloud</button>
@@ -58,16 +61,6 @@ 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 clicked row in DataFrame
-if 'clicked_row' not in st.session_state:
-    st.session_state['clicked_row'] = None
-# Function to display the DataFrame and capture clicks
-def display_clickable_dataframe(df):
-    for idx, row in df.iterrows():
-        if st.button(f"Row {idx}", key=f"row_{idx}"):
-            st.session_state['clicked_row'] = row
 # Create Expander and Columns UI for terms
 with st.expander("Search by Common Terms 📚"):
     cols = st.columns(4)
@@ -76,21 +69,42 @@ with st.expander("Search by Common Terms 📚"):
             if st.button(f"{term}"):
                 filtered_data = filter_by_keyword(data, term)
                 st.write(f"Filter on '{term}' 📊")
-                display_clickable_dataframe(filtered_data)
-# Display the HTML content for the clicked row
-if st.session_state['clicked_row'] is not None:
-    html_content = generate_html_with_textarea(st.session_state['clicked_row'])
-    components.html(html_content, width=1280, height=1024)
 # Text input for search keyword
 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}' 📊")
-    display_clickable_dataframe(filtered_data)
 # Markdown and emojis for the case presentation
 st.markdown("# 🏥 Case Study: 32-year-old Woman's Wellness Check")
@@ -137,4 +151,4 @@ if st.button("Submit"):
         st.error("Incorrect. 😞")
         st.markdown("""
         The best next step is **Ultrasound with Doppler**.
-        """)

 import pandas as pd
 import streamlit.components.v1 as components
+# 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
 # Function to load JSONL file into a DataFrame
 def load_jsonl(file_path):
     data = []
     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>
     <body>
         <h1>🔊 Read It Aloud</h1>
         <textarea id="textArea" rows="10" cols="80">
+    {text_to_speak}
         </textarea>
         <br>
         <button onclick="readAloud()">🔊 Read Aloud</button>
 # Top 20 healthcare terms for USMLE
 top_20_terms = ['Heart', 'Lung', 'Pain', 'Memory', 'Kidney', 'Diabetes', 'Cancer', 'Infection', 'Virus', 'Bacteria', 'Gastrointestinal', 'Skin', 'Blood', 'Surgery']
 # Create Expander and Columns UI for terms
 with st.expander("Search by Common Terms 📚"):
     cols = st.columns(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_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)
 # Text input for search keyword
 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)
+# 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.error("Incorrect. 😞")
         st.markdown("""
         The best next step is **Ultrasound with Doppler**.
+        """)