Update app.py

app.py

@@ -1,14 +1,18 @@
 import streamlit as st
 import pandas as pd
-from transformers import pipeline
+from transformers import pipeline, AutoTokenizer
 import base64
 
 # Load the EasyTerms/legalSummerizerET model from Hugging Face
-summarizer = pipeline("summarization", model="EasyTerms/legalSummerizerET")
+summarizer = pipeline("summarization", model="EasyTerms/legalSummerizerET", num_labels=1)
+
+# Increase the maximum token limit
+tokenizer = AutoTokenizer.from_pretrained("EasyTerms/legalSummerizerET")
+summarizer.model.config.max_position_embeddings = tokenizer.model_max_length
 
 # Function to generate summary using the EasyTerms/legalSummerizerET model
 def generate_summary(contract_text):
-    summary = summarizer(contract_text, max_length=150, min_length=50, length_penalty=2.0, num_beams=4, early_stopping=True)
+    summary = summarizer(contract_text, max_length=512, min_length=50, length_penalty=2.0, num_beams=4, early_stopping=True)
     return summary[0]['summary_text']
 
 # Function to handle feedback and store it in a CSV file
@@ -37,7 +41,7 @@ def main():
     st.title("Legal Contract Summarizer with Feedback")
 
     # Input area for legal contract
-    contract_text = st.text_area("Enter the legal contract:")
+    contract_text = st.text_area("Enter the legal contract:", height=200)  # Increase the height to handle larger contracts
 
     # Button to generate summary
     if st.button("Generate Summary"):