import os
import openai
import gradio as gr
import transformers

openai.api_key = os.environ["api"]

generator = transformers.pipeline("text-generation", model="EleutherAI/gpt-neo-2.7B")

def generateBlogTopics(prompt1):
    response = generator("Generate blog topics on: {}. \n \n 1.  ".format(prompt1), max_length=100, do_sample=True, temperature=0.7)[0]["generated_text"]
    return response

def generateBlogSections(prompt1):
    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"]
    return response

def blogSectionExpander(prompt1):
    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"]
    return response

input_text = gr.inputs.Textbox(lines=5, label="Enter prompt text here:")

title_section_output = gr.outputs.Textbox(label="Title & Sections")
section_expander_output = gr.outputs.Textbox(label="Section Expander")

gr.Interface(
    [generateBlogTopics, generateBlogSections],
    inputs=input_text,
    outputs=title_section_output,
    title="Blog Title & Sections Generator",
    description="Generate high level sections for your blog topic",
    live=False
).launch(share=True)

gr.Interface(
    blogSectionExpander,
    inputs=input_text,
    outputs=section_expander_output,
    title="Blog Section Expander",
    description="Expand your blog sections with professional and clever explanation",
    live=False
).launch(share=True)