Create app.py

app.py

@@ -0,0 +1,61 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load the pre-trained model and tokenizer
+model_name = "Vamsi/T5_Paraphrase_Paws"  # Replace with your desired paraphrasing model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Function to perform paraphrasing
+def paraphrase_text(input_text, max_length=200, num_beams=5):
+    """
+    Paraphrases the input text using a Hugging Face T5 model.
+
+    Args:
+        input_text (str): The text to paraphrase.
+        max_length (int): Maximum length of the paraphrased text.
+        num_beams (int): Number of beams for beam search.
+
+    Returns:
+        str: The paraphrased text.
+    """
+    # Add the paraphrasing prefix
+    input_text = f"paraphrase: {input_text}"
+    
+    # Tokenize the input text
+    inputs = tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
+    
+    # Generate paraphrased text
+    outputs = model.generate(
+        inputs,
+        max_length=max_length,
+        num_beams=num_beams,
+        early_stopping=True
+    )
+    
+    # Decode and return the paraphrased text
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Streamlit UI
+st.title("Paraphrasing Tool")
+st.write("Enter a paragraph below, and this tool will provide a human-like paraphrase.")
+
+# Input text box
+input_paragraph = st.text_area("Input Text:", placeholder="Type your text here...")
+
+# Paraphrasing options
+max_length = st.slider("Maximum Length of Paraphrased Text:", min_value=50, max_value=300, value=200)
+num_beams = st.slider("Beam Search Width (Quality vs Speed):", min_value=1, max_value=10, value=5)
+
+# Paraphrase button
+if st.button("Paraphrase"):
+    if input_paragraph.strip():  # Check if input is not empty
+        with st.spinner("Paraphrasing in progress..."):
+            try:
+                paraphrased_text = paraphrase_text(input_paragraph, max_length=max_length, num_beams=num_beams)
+                st.subheader("Paraphrased Output:")
+                st.write(paraphrased_text)
+            except Exception as e:
+                st.error(f"An error occurred: {e}")
+    else:
+        st.warning("Please enter some text to paraphrase.")