Update app.py

app.py

@@ -1,56 +1,53 @@
-import plotly.graph_objects as go
 import streamlit as st
+import plotly.express as px
+import plotly.graph_objects as go
 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}
+from transformers import pipeline
+
+# Define the Hugging Face model pipeline
+nlp = pipeline("sentiment-analysis")
+
+# Define the hospital data as a Python list of dictionaries
+hospital_data = [
+    {"name": "Mayo Clinic", "beds": 1392, "latitude": 44.0205, "longitude": -92.4630},
+    {"name": "University of Minnesota Medical Center", "beds": 908, "latitude": 44.9737, "longitude": -93.2278},
+    {"name": "Abbott Northwestern Hospital", "beds": 631, "latitude": 44.9482, "longitude": -93.2616},
+    {"name": "St. Cloud Hospital", "beds": 489, "latitude": 45.5563, "longitude": -94.1672},
+    {"name": "Fairview Southdale Hospital", "beds": 342, "latitude": 44.8788, "longitude": -93.3521}
 ]
 
-# 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))
+# Save the hospital data as a CSV file
+hospital_df = pd.DataFrame(hospital_data)
+hospital_df.to_csv("hospital_data.csv", index=False)
+
+# Define the Streamlit app
+def app():
+    # Set the app title
+    st.title("Minnesota Hospital Data")
+    
+    # Load the hospital data from the CSV file
+    hospital_df = pd.read_csv("hospital_data.csv")
+    
+    # Display the hospital data as a table
+    st.write("Hospital Data:", hospital_df)
+    
+    # Analyze the sentiment of the hospital names using the Hugging Face model
+    sentiment_scores = [nlp(hospital["name"])[0]["score"] for hospital in hospital_data]
+    sentiment_colors = ["red" if score < 0.5 else "green" for score in sentiment_scores]
+    hospital_df["sentiment_score"] = sentiment_scores
+    
+    # Create a treemap chart of the hospital data
+    treemap_fig = px.treemap(hospital_df, path=["name"], values="beds",
+                             color="sentiment_score", color_continuous_scale=["red", "green"],
+                             hover_data=["latitude", "longitude"])
+    treemap_fig.update_traces(hovertemplate="<b>%{label}</b><br>Beds: %{value}<br>Latitude: %{customdata[0]}<br>Longitude: %{customdata[1]}")
+    treemap_fig.update_layout(margin=dict(t=25, b=25, r=25, l=25))
+    st.plotly_chart(treemap_fig)
+    
+    # Display the top five largest hospitals in Minnesota
+    st.subheader("Top 5 Largest Hospitals in Minnesota")
+    largest_hospitals = hospital_df.nlargest(5, "beds")
+    st.write(largest_hospitals)
+    
+if __name__ == "__main__":
+    app()