Create app.py

app.py

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+import streamlit as st
+from transformers import T5Tokenizer, AutoModelForSeq2SeqLM
+
+# Load the Hugging Face model with SentencePiece tokenizer
+@st.cache_resource
+def load_model():
+    tokenizer = T5Tokenizer.from_pretrained("Vamsi/T5_Paraphrase_Paws")
+    model = AutoModelForSeq2SeqLM.from_pretrained("Vamsi/T5_Paraphrase_Paws")
+    return tokenizer, model
+
+# Load the model and tokenizer
+tokenizer, model = load_model()
+
+# Streamlit app interface
+st.title("Paraphrasing Tool - AI to Human")
+st.write("Paste your AI-generated text below, and the tool will humanize it:")
+
+# Input text box
+input_text = st.text_area("Enter text here (no word limit):")
+
+if st.button("Paraphrase"):
+    if input_text.strip():
+        with st.spinner("Paraphrasing... Please wait."):
+            try:
+                # Prepare input for the model
+                inputs = tokenizer.encode("paraphrase: " + input_text, 
+                                          return_tensors="pt")
+                
+                # Generate paraphrased output
+                outputs = model.generate(
+                    inputs, 
+                    num_beams=5, 
+                    temperature=0.7, 
+                    early_stopping=True
+                )
+                paraphrased_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+                st.success("Here is the paraphrased text:")
+                st.write(paraphrased_text)
+            except Exception as e:
+                st.error(f"An error occurred: {e}")
+    else:
+        st.error("Please enter some text to paraphrase.")