Update app.py

app.py

@@ -1,20 +1,16 @@
 import torch
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from transformers import pipeline
 import streamlit as st
 
-
-
-
 # Model name
-model_name = "YasirAbdali/bart-summarization" # Replace with the path to your fine-tuned model or Hugging Face model ID
+model_name = "YasirAbdali/bart-summarization"  # Replace with the path to your fine-tuned model or Hugging Face model ID
 
-# Load tokenizer and model
+# Load summarization pipeline
 try:
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-    st.write("Model and tokenizer loaded successfully.")
+    summarizer = pipeline("summarization", model=model_name)
+    st.write("Summarization pipeline loaded successfully.")
 except Exception as e:
-    st.error(f"Error loading model or tokenizer: {e}")
+    st.error(f"Error loading summarization pipeline: {e}")
     st.stop()
 
 # Streamlit app
@@ -25,38 +21,21 @@ topic = st.text_area("Enter text:")
 max_length = st.slider("Maximum length of generated text:", min_value=100, max_value=500, value=200, step=50)
 
 if topic:
-    # Tokenize input
-    try:
-        input_ids = tokenizer.encode(topic, return_tensors="pt")
-        st.write("Input text tokenized successfully.")
-    except Exception as e:
-        st.error(f"Error tokenizing input text: {e}")
-        st.stop()
-
     # Generate summary
     try:
-        with torch.no_grad():
-            output = model.generate(
-                input_ids,
-                max_length=max_length,
-                num_return_sequences=1,
-                no_repeat_ngram_size=2,
-                top_k=50,
-                top_p=0.95,
-                temperature=0.7
-            )
+        summary = summarizer(topic, max_length=max_length, min_length=50, do_sample=False)
+        generated_summary = summary[0]['summary_text']
         st.write("Summary generated successfully.")
     except Exception as e:
         st.error(f"Error generating summary: {e}")
         st.stop()
 
-    # Decode and display generated summary
+    # Display generated summary
     try:
-        generated_summary = tokenizer.decode(output[0], skip_special_tokens=True)
         st.subheader("Generated Summary:")
         st.markdown(generated_summary)
     except Exception as e:
-        st.error(f"Error decoding generated summary: {e}")
+        st.error(f"Error displaying generated summary: {e}")
 
     # Option to download the summary
     st.download_button(
@@ -65,5 +44,3 @@ if topic:
         file_name="generated_summary.txt",
         mime="text/plain"
     )
-
-