Create app.py

app.py

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+import streamlit as st
+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},
+]
+
+# 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)
+
+# 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)