Update app.py

app.py

@@ -1,38 +1,42 @@
-from transformers import GPT2Tokenizer, GPT2LMHeadModel
 import streamlit as st
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+
+# Set the title of the Streamlit app
+st.title("GPT-2 Blog Post Generator")
 
 # Initialize the tokenizer and model
 model_name = 'gpt2-large'
 tokenizer = GPT2Tokenizer.from_pretrained(model_name)
 model = GPT2LMHeadModel.from_pretrained(model_name)
 
-text= st.text_area("Enter your Topic: ")
-
+# Text input for the user
+text = st.text_area("Enter your Topic: ")
 
 if text:
-    # Encode input text
-    encoded_input = tokenizer(text, return_tensors='pt')
-
-    # Generate text
-    output = model.generate(
-        input_ids=encoded_input['input_ids'],
-        max_length=50,
-        num_return_sequences=1,
-        no_repeat_ngram_size=2,
-        top_p=0.95,
-        top_k=50
-    )
-
-    # Decode generated text
-    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
-
-    st.json(generated_text)
-
-# import streamlit as st
-# from transformers import pipeline
-# pipe = pipeline("sentiment-analysis")
-# text= st.text_area("Enter your text")
-
-# if text:
-#     output = pipe(text)
-#     st.json(output)
+    try:
+        # Encode input text
+        encoded_input = tokenizer(text, return_tensors='pt')
+
+        # Generate text
+        output = model.generate(
+            input_ids=encoded_input['input_ids'],
+            max_length=200,  # Adjust length as needed
+            num_return_sequences=1,
+            no_repeat_ngram_size=2,
+            top_p=0.95,
+            top_k=50
+        )
+
+        # Decode generated text
+        generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+
+        # Display generated text
+        st.subheader("Generated Blog Post")
+        st.write(generated_text)
+    except Exception as e:
+        st.error(f"An error occurred: {e}")
+
+# Add instructions
+st.write("""
+    Enter a topic or a starting sentence in the text area above, and the GPT-2 model will generate a blog post for you.
+    """)