Update app.py

app.py

@@ -1,46 +1,71 @@
-import streamlit as st
-import transformers
+# 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)
+# # 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")
+# # Streamlit App
+# def main():
+#     st.title("Sentence Category Classifier")
     
-    # Input search sentence
-    search_query = st.text_input("Enter a sentence:")
+#     # Input search sentence
+#     search_query = st.text_input("Enter a sentence:")
     
-    result = ""
+#     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)
+#     # 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 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)
+#     # 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]
+#     # Extract the category labels from the model's output
+#     category_labels = [category['label'] for category in categories]
     
-    return category_labels
+#     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')
 
-if __name__ == "__main__":
-    main()
+# Create button to trigger categorization
+if st.button('Categorize'):
+    st.write('Categories:')
+    categories = categorize_sentence(sentence)
+    for category in categories:
+        st.success(category)