MedSpeech-012324

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awacke1 commited on Oct 2, 2023

Commit

8927802

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1 Parent(s): e21a683

Update backupapp.py

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backupapp.py +115 -122

backupapp.py CHANGED Viewed

@@ -1,18 +1,8 @@
 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 = []
@@ -25,132 +15,135 @@ def load_jsonl(file_path):
 def filter_by_keyword(df, keyword):
     return df[df.apply(lambda row: row.astype(str).str.contains(keyword).any(), axis=1)]
-# 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)
-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>
-    '''
-# Define your text passage
-text_passage = "A 60-year-old man is brought to the emergency department by police officers because he was acting strangely in public. The patient was found talking nonsensically to characters on cereal boxes in the store. Past medical history is significant for multiple hospitalizations for alcohol-related injuries and seizures. The patient’s vital signs are within normal limits. Physical examination shows a disheveled male who is oriented to person, but not time or place. Neurologic examination shows nystagmus and severe gait ataxia. A T1/T2 MRI is performed and demonstrates evidence of damage to the mammillary bodies. The patient is given the appropriate treatment for recovering most of his cognitive functions. However, significant short-term memory deficits persist. The patient remembers events from his past such as the school and college he attended, his current job, and the names of family members quite well. Which of the following is the most likely diagnosis in this patient?"
-# Generate HTML code
-documentHTML5 = generate_html_with_textarea(text_passage)
-# 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.")
-# Insert the HTML into Streamlit
-# Button to read all filtered rows
-if st.button("Read Aloud Text"):
-    components.html(documentHTML5, width=1280, height=1024)
-# 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)

 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 = []
 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>
+    '''
+# Streamlit App 🚀
+st.title("USMLE Medical Questions 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', 'Neurology', 'Psychiatry', 'Gastrointestinal', 'Pediatrics', 'Oncology', 'Skin', 'Blood', 'Surgery', 'Epidemiology', 'Genetics']
+# Create Expander and Columns UI for 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"Filtered Dataset by '{term}' 📊")
+                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.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**.
+        """)