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| # 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 | |
| import plotly.express as px | |
| st.title("Sentiment Analysis and Hospital Data Visualization") | |
| # Sentiment analysis section | |
| st.header("Sentiment Analysis") | |
| text = st.text_input("Enter some text:") | |
| if text: | |
| sentiment = analyze_sentiment(text) | |
| st.write("Sentiment:", sentiment) | |
| # Hospital data section | |
| st.header("Hospital Data") | |
| df = px.data.tips() | |
| fig = px.treemap(hospital_data, path=["name"], values="beds", color="beds") | |
| st.plotly_chart(fig) | |