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Update app.py
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app.py
CHANGED
@@ -41,22 +41,20 @@ def page_trending_niche():
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# Load pre-trained model and tokenizer
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tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
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model = GPT2LMHeadModel.from_pretrained('gpt2')
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def page_social_media_generator():
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col1, col2 = st.columns([3, 1])
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with col1:
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st.title("Social Media Content Generator")
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@@ -69,9 +67,13 @@ def page_social_media_generator():
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if article_url:
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st.write(f"Selected Article URL: {article_url}")
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if st.button('Generate Social Media Post'):
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with st.spinner('Generating...'):
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post_content = generate_social_media_post(
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st.success('Generated Content:')
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st.write(post_content)
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else:
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from transformers import pipeline
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# Initialize the summarization pipeline with BART
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summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
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def generate_social_media_post(article_text):
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# Ensure the article text is not too long for the model
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article_text = article_text[:1024] # BART has a limit on the length of the text it can process
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summary = summarizer(article_text, max_length=130, min_length=30, do_sample=False)[0]['summary_text']
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return summary
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def page_social_media_generator():
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col1, col2 = st.columns([3, 1])
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with col1:
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st.title("Social Media Content Generator")
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if article_url:
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st.write(f"Selected Article URL: {article_url}")
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# Example placeholder for article text fetching
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# In a real application, replace the following line with code to fetch the article's text from `article_url`
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article_text = "The full text of the article goes here."
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if st.button('Generate Social Media Post'):
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with st.spinner('Generating...'):
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post_content = generate_social_media_post(article_text)
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st.success('Generated Content:')
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st.write(post_content)
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else:
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