start

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import nltk
+
+# Download NLTK data
+nltk.download('punkt')
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("fabiochiu/t5-base-tag-generation")
+model = AutoModelForSeq2SeqLM.from_pretrained("fabiochiu/t5-base-tag-generation")
+
+# Streamlit app title
+st.title("Text to Tags Generation")
+
+# Text area for user input
+text = st.text_area("Enter the text for tag extraction:", height=200)
+
+# Button to generate tags
+if st.button("Generate Tags"):
+    if text:
+        try:
+            # Tokenize and encode the input text
+            inputs = tokenizer([text], max_length=512, truncation=True, return_tensors="pt")
+            
+            # Generate tags
+            output = model.generate(**inputs, num_beams=8, do_sample=True, min_length=10, max_length=64)
+            
+            # Decode the output
+            decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
+            
+            # Extract unique tags
+            tags = list(set(decoded_output.strip().split(", ")))
+            
+            # Display the tags
+            st.write("**Generated Tags:**")
+            st.write(tags)
+        except Exception as e:
+            st.error(f"An error occurred: {e}")
+    else:
+        st.warning("Please enter some text to generate tags.")
+
+# To run this app, save this code to a file (e.g., `app.py`) and run `streamlit run app.py` in your terminal.