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import gradio as gr
import requests
# GPT-J-6B API
API_URL = "https://api-inference.huggingface.co/models/EleutherAI/gpt-j-6B"
headers = {"Authorization": "Bearer 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"}
prompt = """Oh, my life
is changing every day
Every possible way
And oh, my dreams,
it's never quite as it seems
Never quite as it seems"""
#examples = [["mind"], ["memory"], ["sleep"],["wellness"],["nutrition"]]
def poem2_generate(word):
p = word.lower() + "\n" + "poem using word: "
gr.Markdown("Prompt is :{p}")
json_ = {"inputs": p,
"parameters":
{
"top_p": 0.9,
"temperature": 1.1,
"max_new_tokens": 50,
"return_full_text": False
}}
response = requests.post(API_URL, headers=headers, json=json_)
output = response.json()
gr.Markdown("error? Reason is : {output}")
output_tmp = output[0]['generated_text']
gr.Markdown("GPTJ response without splits is: {output_tmp}")
poem = output[0]['generated_text'].split("\n\n")[0] # +"."
if "\n\n" not in output_tmp:
if output_tmp.find('.') != -1:
idx = output_tmp.find('.')
poem = output_tmp[:idx+1]
else:
idx = output_tmp.rfind('\n')
poem = output_tmp[:idx]
else:
poem = output_tmp.split("\n\n")[0] # +"."
poem = poem.replace('?','')
gr.Markdown("Returned is: {poem}")
return poem
def poem_generate(word):
p = prompt + word.lower() + "\n" + "poem using word: "
gr.Markdown("Generate - Prompt is :{p}")
json_ = {"inputs": p,
"parameters":
{
"top_p": 0.9,
"temperature": 1.1,
"max_new_tokens": 50,
"return_full_text": False
}}
response = requests.post(API_URL, headers=headers, json=json_)
output = response.json()
gr.Markdown("error? Reason is : {output}")
output_tmp = output[0]['generated_text']
gr.Markdown("Response without splits is: {output_tmp}")
poem = output[0]['generated_text'].split("\n\n")[0] # +"."
if "\n\n" not in output_tmp:
if output_tmp.find('.') != -1:
idx = output_tmp.find('.')
poem = output_tmp[:idx+1]
else:
idx = output_tmp.rfind('\n')
poem = output_tmp[:idx]
else:
poem = output_tmp.split("\n\n")[0] # +"."
poem = poem.replace('?','')
gr.Markdown("Returned is: {poem}")
return poem
def poem_to_image(poem):
gr.Markdown("toimage")
poem = " ".join(poem.split('\n'))
poem = poem + " oil on canvas."
steps, width, height, images, diversity = '50','256','256','1',15
img = gr.Interface().load("spaces/multimodalart/latentdiffusion")(poem, steps, width, height, images, diversity)[0]
return img
def set_example(example: list) -> dict:
return gr.Textbox.update(value=example[0])
demo = gr.Blocks()
with demo:
gr.Markdown("<h1><center>Few Shot Learning Text to Word Image Search</center></h1>")
gr.Markdown("https://huggingface.co/blog/few-shot-learning-gpt-neo-and-inference-api, https://github.com/EleutherAI/the-pile")
with gr.Row():
input_word = gr.Textbox(lines=7, value=prompt)
poem_txt = gr.Textbox(lines=7)
output_image = gr.Image(type="filepath", shape=(256,256))
b1 = gr.Button("Generate Text")
b2 = gr.Button("Generate Image")
b1.click(poem2_generate, input_word, poem_txt)
b2.click(poem_to_image, poem_txt, output_image)
examples=[["living, loving,"], ["I want to live. I want to give."],["Ive been to Hollywood. Ive been to Redwood"]]
example_text = gr.Dataset(components=[input_word], samples=examples)
example_text.click(fn=set_example,inputs = example_text,outputs= example_text.components)
demo.launch(enable_queue=True, debug=True) |