Update app.py

app.py

@@ -1,7 +1,11 @@
 import streamlit as st
-from transformers import pipeline
+from transformers import pipeline, TFAutoModelForSeq2SeqLM, T5Tokenizer
 
-t5_sum = pipeline("summarization", model= "t5-small")
+# Load T5 model for summarization
+model_name = "t5-small"
+model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name)
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+summarizer = pipeline("summarization", model=model, tokenizer=tokenizer)
 
 # Set the title for the Streamlit app
 st.title("T5 Summary Generator")
@@ -9,16 +13,16 @@ st.title("T5 Summary Generator")
 # Text input for the user
 text = st.text_area("Enter your text: ")
 
-def generate_text(dataset_sample):
-    article = dataset_sample
-    summary = summarizer(article, max_length=150, min_length=40, do_sample=False)
-    
+def generate_summary(input_text):
+    # Perform summarization
+    summary = summarizer(input_text, max_length=150, min_length=40, do_sample=False)
     return summary[0]['summary_text']
 
 if st.button("Generate"):
-    generated_text = generate_text(text)
-    if generated_text:
-        # Display the generated text
-        st.subheader("Generated Blog Post")
-        st.write(generated_text)
-
+    if text:
+        generated_summary = generate_summary(text)
+        # Display the generated summary
+        st.subheader("Generated Summary")
+        st.write(generated_summary)
+    else:
+        st.warning("Please enter some text to generate a summary.")