Update app.py

app.py

@@ -1,43 +1,46 @@
 import streamlit as st
+import plotly.express as px
 import pandas as pd
-import plotly.graph_objects as go
-from transformers import pipeline
-
-# Define the Hugging Face model pipeline
-nlp = pipeline("sentiment-analysis")
-
-# Define a Python list dictionary of the top five largest hospitals in Minnesota
-hospital_data = [
-    {'name': 'Mayo Clinic Hospital - Rochester', 'beds': 2147, 'latitude': 44.022, 'longitude': -92.466},
-    {'name': 'St. Cloud Hospital', 'beds': 489, 'latitude': 45.570, 'longitude': -94.173},
-    {'name': 'Abbott Northwestern Hospital', 'beds': 632, 'latitude': 44.952, 'longitude': -93.262},
-    {'name': 'Mercy Hospital - Coon Rapids', 'beds': 426, 'latitude': 45.157, 'longitude': -93.316},
-    {'name': 'United Hospital', 'beds': 460, 'latitude': 44.941, 'longitude': -93.105},
+
+# Define the Hugging Face model
+@st.cache(allow_output_mutation=True)
+def load_model():
+    model = <Hugging Face Model> # Replace with your own Hugging Face model
+    return model
+
+# Define the top five largest hospitals in NJ
+hospitals = [
+    {'name': 'Hackensack University Medical Center', 'beds': 974, 'latitude': 40.8875, 'longitude': -74.0344},
+    {'name': 'Robert Wood Johnson University Hospital', 'beds': 965, 'latitude': 40.4842, 'longitude': -74.4323},
+    {'name': 'Jersey Shore University Medical Center', 'beds': 621, 'latitude': 40.1876, 'longitude': -74.0575},
+    {'name': 'St. Joseph\'s University Medical Center', 'beds': 525, 'latitude': 40.9235, 'longitude': -74.1645},
+    {'name': 'Morristown Medical Center', 'beds': 506, 'latitude': 40.7878, 'longitude': -74.4873}
 ]
 
-# Convert the hospital data to a Pandas DataFrame and save it as a CSV file
-hospital_df = pd.DataFrame(hospital_data)
-hospital_df.to_csv('hospital_data.csv', index=False)
+# Save the hospital data as a CSV file
+df = pd.DataFrame(hospitals)
+df.to_csv('hospitals.csv', index=False)
 
 # Define the Streamlit app
-st.title('Minnesota Hospital Data')
-
-# Display the hospital data as a table
-st.write(hospital_df)
-
-# Analyze the hospital names using the Hugging Face model
-sentiments = nlp([h['name'] for h in hospital_data])
-
-# Create a dictionary of hospital names and their sentiment scores
-sentiment_scores = {h['name']: s['score'] for h, s in zip(hospital_data, sentiments)}
-
-# Create a Plotly treemap of hospital beds by name and sentiment score
-fig = go.Figure(go.Treemap(
-    labels=[h['name'] for h in hospital_data],
-    parents=[''] * len(hospital_data),
-    values=[h['beds'] for h in hospital_data],
-    text=[f"Sentiment Score: {sentiment_scores[h['name']]:.2f}" for h in hospital_data],
-    hovertemplate='<b>%{label}</b><br>%{text}<br>Number of Beds: %{value}',
-))
-fig.update_layout(title='Minnesota Hospital Beds and Sentiment Scores')
-st.plotly_chart(fig)
+def app():
+    st.title('Hospital Data')
+
+    # Load the Hugging Face model
+    model = load_model()
+
+    # Get user input
+    text = st.text_input('Enter text to summarize')
+
+    # Generate summary using the Hugging Face model
+    summary = model(text)
+
+    # Display the summary
+    st.write('Summary:', summary)
+
+    # Create a treemap of the hospital data
+    fig = px.treemap(df, path=['name'], values='beds', color='beds', hover_data=['latitude', 'longitude'])
+    st.plotly_chart(fig)
+
+# Run the Streamlit app
+if __name__ == '__main__':
+    app()