Create app.py

app.py

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+import streamlit as st
+import transformers
+import torch
+
+# 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()