Update app.py
Browse files
app.py
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import streamlit as st
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from transformers import GPT2Tokenizer, GPT2LMHeadModel
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# Initialize the tokenizer and model
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tokenizer = GPT2Tokenizer.from_pretrained(model_name)
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model = GPT2LMHeadModel.from_pretrained(model_name)
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# Set the title for the Streamlit app
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st.title("GPT-2 Blog Post Generator")
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# Text input for the user
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text =
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# Display the generated text
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st.subheader("Generated Blog Post")
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st.write(generated_text)
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except Exception as e:
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st.error(f"An error occurred: {e}")
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# Add instructions
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st.write("""
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Enter a topic or a starting sentence in the text area above, and the GPT-2 model will generate a blog post for you.
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""")
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# Streamlit instructions
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st.write("To run this app, use the command: `streamlit run <script_name>.py`")
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from transformers import GPT2Tokenizer, GPT2LMHeadModel
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# Initialize the tokenizer and model
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tokenizer = GPT2Tokenizer.from_pretrained(model_name)
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model = GPT2LMHeadModel.from_pretrained(model_name)
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# Text input for the user
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text = "my cat"
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# Encode input text
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encoded_input = tokenizer(text, return_tensors='pt', padding=True, truncation=True)
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# Generate text
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output = model.generate(
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input_ids=encoded_input['input_ids'],
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attention_mask=encoded_input['attention_mask'],
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max_length=200, # Adjust length as needed
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num_return_sequences=1,
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no_repeat_ngram_size=2,
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top_p=0.95,
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top_k=50,
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pad_token_id=tokenizer.eos_token_id # Set pad_token_id to eos_token_id
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)
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# Decode generated text
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generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
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generated_text
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