Update app.py

app.py

@@ -1,10 +1,14 @@
 import streamlit as st
-from transformers import pipeline, AutoModelForSeq2SeqLM, T5Tokenizer
 
-# Load T5 model for summarization using PyTorch
-model_name = "t5-small"
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-tokenizer = T5Tokenizer.from_pretrained(model_name)
+from transformers import T5Tokenizer, TFAutoModelForSeq2SeqLM, pipeline
+
+# Define the path to the saved model
+model_path = '/T5_samsum-20240723T171755Z-001.zip'
+
+# Load the tokenizer and model
+tokenizer = T5Tokenizer.from_pretrained(model_path)
+model = TFAutoModelForSeq2SeqLM.from_pretrained(model_path)
+
 summarizer = pipeline("summarization", model=model, tokenizer=tokenizer)
 
 # Set the title for the Streamlit app
@@ -15,7 +19,7 @@ text = st.text_area("Enter your text: ")
 
 def generate_summary(input_text):
     # Perform summarization
-    summary = summarizer(input_text, max_length=150, min_length=40, do_sample=False)
+    summary = summarizer(input_text, max_length=200, min_length=40, do_sample=False)
     return summary[0]['summary_text']
 
 if st.button("Generate"):