V3
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app.py
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import os
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import openai
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openai.api_key = os.environ["api"]
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return response['content'][0]['text']
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iface = gr.Interface(fn=generate_blog, inputs='text', outputs='text')
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iface.launch()
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import os
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import openai
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import gradio as gr
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import transformers
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openai.api_key = os.environ["api"]
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generator = transformers.pipeline("text-generation", model="EleutherAI/gpt-neo-2.7B")
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def generateBlogTopics(prompt1):
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response = generator("Generate blog topics on: {}. \n \n 1. ".format(prompt1), max_length=100, do_sample=True, temperature=0.7)[0]["generated_text"]
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return response
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def generateBlogSections(prompt1):
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response = generator("Expand the blog title in to high level blog sections: {} \n\n- Introduction: ".format(prompt1), max_length=100, do_sample=True, temperature=0.6)[0]["generated_text"]
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return response
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def blogSectionExpander(prompt1):
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response = generator("Expand the blog section in to a detailed professional, witty and clever explanation.\n\n {}".format(prompt1), max_length=400, do_sample=True, temperature=0.7)[0]["generated_text"]
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return response
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input_text = gr.inputs.Textbox(lines=5, label="Enter prompt text here:")
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title_section_output = gr.outputs.Textbox(label="Title & Sections")
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section_expander_output = gr.outputs.Textbox(label="Section Expander")
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gr.Interface(
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[generateBlogTopics, generateBlogSections],
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inputs=input_text,
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outputs=title_section_output,
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title="Blog Title & Sections Generator",
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description="Generate high level sections for your blog topic",
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live=False
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).launch(share=True)
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gr.Interface(
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blogSectionExpander,
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inputs=input_text,
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outputs=section_expander_output,
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title="Blog Section Expander",
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description="Expand your blog sections with professional and clever explanation",
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live=False
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).launch(share=True)
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