app.py

# 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)