Update src/streamlit_app.py

src/streamlit_app.py

@@ -9,20 +9,18 @@ MODEL_NAME = "AbdullahAlnemr1/flan-t5-summarizer"
 tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
 model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
 
-st.title("Text Summarizer (Encoder-Decoder)")
+st.title("Text Summarizer(Encoder-Decoder)")
 
-input_text = st.text_area("Enter text summarize:", height=200)
+input_text = st.text_area("Enter text to summarize:", height=200)
 
-# Fixed summary length
-max_new_tokens = 100  # You can adjust this number
+max_new_tokens = st.slider("Max summary length", min_value=20, max_value=200, value=100)
 
 if st.button("Generate Summary"):
     if input_text.strip() == "":
-        st.warning("Enter Text:")
+        st.warning("Please enter some text to summarize.")
     else:
         # Tokenize input
         inputs = tokenizer(input_text, return_tensors="pt", truncation=True)
-        
         # Generate summary
         outputs = model.generate(
             inputs["input_ids"],
@@ -30,8 +28,6 @@ if st.button("Generate Summary"):
             num_beams=4,
             early_stopping=True
         )
-        
         summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
-        
         st.subheader("Summary:")
         st.write(summary)