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# import streamlit as st
# import transformers
# # Load the pre-trained language model
# model_name = "bert-base-uncased"
# model = transformers.pipeline("text-classification", model=model_name)
# # Streamlit App
# def main():
# st.title("Sentence Category Classifier")
# # Input search sentence
# search_query = st.text_input("Enter a sentence:")
# result = ""
# # Process the search sentence when the user clicks the Search button
# if st.button("Search"):
# if search_query:
# # Classify the sentence using the pre-trained model
# categories = classify_sentence(search_query)
# # Display the categories as output
# if categories:
# result = f"The sentence belongs to the following categories:\n\n"
# for category in categories:
# result += f"• {category}\n"
# else:
# result = "No categories found for the sentence."
# # Display the result
# st.text(result)
# # Function to classify the sentence using the pre-trained language model
# @st.cache(allow_output_mutation=True)
# def classify_sentence(query):
# # Classify the sentence using the pre-trained model
# categories = model(query)
# # Extract the category labels from the model's output
# category_labels = [category['label'] for category in categories]
# return category_labels
# if __name__ == "__main__":
# main()
import streamlit as st
# Function to categorize input sentences
def categorize_sentence(sentence):
# Replace this function with your own logic to categorize sentences
categories = ['Restaurants', 'Food', 'Travel', 'New York City']
return categories
# Configure Streamlit layout
st.set_page_config(page_title='Sentence Categorizer', layout='wide')
# Add title and description
st.title('Welcome to Sentence Categorizer')
st.write('Enter a sentence and discover relevant categories!')
# Create input box
sentence = st.text_input('Enter a sentence')
# Create button to trigger categorization
if st.button('Categorize'):
st.write('Categories:')
categories = categorize_sentence(sentence)
for category in categories:
st.success(category) |