Update app.py

app.py

@@ -1,13 +1,88 @@
+# Step 1: Install the required libraries
+!pip install streamlit plotly transformers
+
+# Step 2: Load the Huggingface model for sentiment analysis
+import transformers
+import torch
+
+model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
+tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
+model = transformers.AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Step 3: Create a function to analyze the sentiment of text using the Huggingface model
+def analyze_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt")
+    outputs = model(**inputs)
+    scores = torch.nn.functional.softmax(outputs.logits, dim=1).detach().numpy()[0]
+    sentiment = scores.argmax()
+    return sentiment
+
+# Step 4: Define a Python list dictionary of the top five largest hospitals in the state of Minnesota
+hospital_data = [
+    {
+        "name": "Mayo Clinic",
+        "beds": 1500,
+        "latitude": 44.023501,
+        "longitude": -92.465032,
+        "url": "https://www.mayoclinic.org/appointments"
+    },
+    {
+        "name": "University of Minnesota Medical Center",
+        "beds": 1077,
+        "latitude": 44.969478,
+        "longitude": -93.236351,
+        "url": "https://www.mhealth.org/ummc"
+    },
+    {
+        "name": "Abbott Northwestern Hospital",
+        "beds": 1034,
+        "latitude": 44.952221,
+        "longitude": -93.266389,
+        "url": "https://www.allinahealth.org/locations/abbott-northwestern-hospital"
+    },
+    {
+        "name": "St. Cloud Hospital",
+        "beds": 489,
+        "latitude": 45.554935,
+        "longitude": -94.171829,
+        "url": "https://www.centracare.com/locations/st-cloud-hospital/"
+    },
+    {
+        "name": "Essentia Health-St. Mary's Medical Center",
+        "beds": 391,
+        "latitude": 46.783839,
+        "longitude": -92.103965,
+        "url": "https://www.essentiahealth.org/find-facility/profile/st-marys-medical-center-duluth/"
+    }
+]
+
+# Step 5: Save the Python list dictionary as a CSV file
+import csv
+
+with open("hospital_data.csv", mode="w", newline="") as file:
+    writer = csv.DictWriter(file, fieldnames=["name", "beds", "latitude", "longitude", "url"])
+    writer.writeheader()
+    for hospital in hospital_data:
+        writer.writerow(hospital)
+
+# Step 6: Create a Streamlit app that uses Plotly graph objects like treemap to visualize the sentiment analysis results and the hospital data
 import streamlit as st
-from transformers import pipeline
+import plotly.express as px
+
+st.title("Sentiment Analysis and Hospital Data Visualization")
+
+# Sentiment analysis section
+st.header("Sentiment Analysis")
 
-st.title("Text Generation with Hugging Face")
+text = st.text_input("Enter some text:")
+if text:
+    sentiment = analyze_sentiment(text)
+    st.write("Sentiment:", sentiment)
 
-model_name = "gpt2"
-generator = pipeline("text-generation", model=model_name, device=0)
+# Hospital data section
+st.header("Hospital Data")
 
-prompt = st.text_input("Enter text prompt:", "Once upon a time")
+df = px.data.tips()
+fig = px.treemap(hospital_data, path=["name"], values="beds", color="beds")
+st.plotly_chart(fig)
 
-if st.button("Generate Text"):
-    output = generator(prompt, max_length=100, do_sample=True)
-    st.write(output[0]['generated_text'])