app.py

import gradio as gr
import os
import openai
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
import torch 

model_id = "stabilityai/stable-diffusion-2-1"
pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
# pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
# pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
# pipe = pipe.to("cuda")

openai.api_key = os.getenv("OPENAI_API_KEY")

def generate_prompt(radio,word1,word2):
    #prompt = 'Create an analogy for this phrase:\n\n{word1}'
    # 50/50 in that/because
    # pluralize singluar words

    if radio == "normal":
        prompt_in = f'Create an analogy for this phrase:\n\n{word1} is like {word2} in that:'
    else:
        prompt_in = f'Create a {radio} analogy for this phrase:\n\n{word1} is like {word2} in that:'
    
    response = openai.Completion.create(
      model="text-davinci-003",
      prompt=prompt_in,
      temperature=0.5,
      max_tokens=60,
      top_p=1.0,
      frequency_penalty=0.0,
      presence_penalty=0.0
    )['choices'][0]['text']

    response_txt = response.replace('\n','')
    diffusion_in = f'a dramatic painting of: {response_txt.split(".")[0]}'
    image = pipe(diffusion_in).images[0]
    return response_txt, image

demo = gr.Interface(
    generate_prompt,
    [
        gr.Radio(["normal", "very insulting"],value='normal',label="Flavor"),
        gr.Textbox(label="Thing 1"),
        gr.Textbox(label="Thing 2")
        # gr.Dropdown(team_list, value=[team_list[random.randint(1,30)]], multiselect=True),
        # gr.Checkbox(label="Is it the morning?"),
    ],
    ["text","image"],
    # "image",
    allow_flagging="never",
    title="GPT-3 Analogy Lab 🧪",
    description="Enter two things you want to connect.",
    css="footer {visibility: hidden}"
)



demo.launch()
#openai.api_key = os.getenv("OPENAI_API_KEY")
# openai.api_key = "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"

# response = openai.Completion.create(
#   model="text-davinci-003",
#   prompt="Create an analogy for this phrase:\n\nQuestions are arrows in that:",
#   temperature=0.5,
#   max_tokens=60,
#   top_p=1.0,
#   frequency_penalty=0.0,
#   presence_penalty=0.0
# )