Update app.py

app.py

@@ -2,50 +2,68 @@ import torch
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 import streamlit as st
 
+
+
+
 # Model name
-model_name = "YasirAbdali/bart-summarization"
+model_name = "YasirAbdali/bart-summarization" # Replace with the path to your fine-tuned model or Hugging Face model ID
 
 # Load tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+try:
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    st.write("Model and tokenizer loaded successfully.")
+except Exception as e:
+    st.error(f"Error loading model or tokenizer: {e}")
+    st.stop()
 
 # Streamlit app
 st.title("Summary Generator")
 
 # User input
 topic = st.text_area("Enter text:")
-max_length = st.slider("Maximum length of generated text:", min_value=100, max_value=500, value=500, step=50)
+max_length = st.slider("Maximum length of generated text:", min_value=100, max_value=500, value=200, step=50)
 
 if topic:
-    # Construct a detailed prompt
-    prompt = f"Summarize the following text: {topic}"
-    
     # Tokenize input
-    input_ids = tokenizer.encode(prompt, return_tensors="pt")
-
-    # Generate text
-    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
-        )
-
-    # Decode and display generated text
-    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
-    
-    st.subheader("Generated Summary:")
-    st.markdown(generated_text)
+    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
+            )
+        st.write("Summary generated successfully.")
+    except Exception as e:
+        st.error(f"Error generating summary: {e}")
+        st.stop()
+
+    # Decode and 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}")
 
     # Option to download the summary
     st.download_button(
         label="Download Summary",
-        data=generated_text,
-        file_name="generated_summary.md",
-        mime="text/markdown"
+        data=generated_summary,
+        file_name="generated_summary.txt",
+        mime="text/plain"
     )
 
+