app.py

import plotly.graph_objects as go
import streamlit as st
import pandas as pd
from transformers import AutoModelWithLMHead

# Create a list of the top five largest hospitals in the state of Minnesota
hospitals_dict = [
    {'name': 'Mayo Clinic',
     'number_beds': 1569,
     'latitude': 44.074,
     'longitude': -92.4591},
    {'name': 'United Hospital',
     'number_beds': 663,
     'latitude': 44.8794,
     'longitude': -93.1748},
    {'name': 'Allina Health',
     'number_beds': 600,
     'latitude': 44.9352,
     'longitude': -93.0614},
    {'name': 'Fairview Southdale Hospital',
     'number_beds': 446,
     'latitude': 44.8552,
     'longitude': -93.3455},
    {'name': 'St. Cloud Hospital',
     'number_beds': 437,
     'latitude': 45.5517,
     'longitude': -94.1613}
]

# Create the dataframe from the list of dictionaries
df = pd.DataFrame(hospitals_dict)

# Create a treemap with plotly graph objects
fig = go.Figure(data=go.Treemap(
    labels=df['name'],
    parents=['Minnesota'] * len(df),
    values=df['number_beds'],
))

# Create the Huggingface model
model = AutoModelWithLMHead.from_pretrained("distilbert-base-uncased")

# Streamlit app
st.title("Minnesota's Largest Hospitals")

# Display the treemap
st.plotly_chart(fig)

# Display hospital information
st.subheader("Hospital Information")
st.write(df)

# Save the dataframe as a CSV file
st.subheader("Save CSV File")
st.markdown("Click the button below to save the dataframe as a CSV file.")
st.write(df.to_csv(index=False))