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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 = """
word: risk
poem using word: And then the day came,
when the risk
to remain tight
in a bud
was more painful
than the risk
it took
to blossom.
word: bird
poem using word: She sights a bird, she chuckles
She flattens, then she crawls
She runs without the look of feet
Her eyes increase to Balls.
word: """
examples = [["river"], ["night"], ["trees"],["table"],["laughs"]]
def poem_generate(word):
p = prompt + word.lower() + "\n" + "poem using word: "
print(f"*****Inside poem_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()
print(f"Was there an error? Reason is : {output}")
output_tmp = output[0]['generated_text']
print(f"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] # +"."
print(f"Poem being returned is: {poem}")
return poem
def poem_to_image(poem):
print("*****Inside Poem_to_image")
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
demo = gr.Blocks()
with demo:
gr.Markdown("<h1><center>Generate Short Poem along with an Illustration</center></h1>")
gr.Markdown(
"<div>Enter a single word you would want GPTJ-6B to write Poetry π€ on.</div>"
"<div>Generate an illustration π¨ provided by Latent Diffusion model.</div><div>GPJ-6B is a 6 Billion parameter autoregressive language model. It generates the Poem based on how it has been 'prompt-engineered' π€ The complete text of generated poem then goes in as a prompt to the amazing Latent Diffusion Art space by <a href='https://huggingface.co/spaces/multimodalart/latentdiffusion' target='_blank'>Multimodalart</a>.</div>Please note that some of the Poems/Illustrations might not look at par, and well, this is what happens when you can't 'cherry-pick' and post π <div> Some of the example words that you can use are 'river', 'night', 'trees', 'table', 'laughs' or maybe on similar lines to get best results!"
)
with gr.Row():
input_word = gr.Textbox(placeholder="Enter a word here to create a Poem on..")
poem_txt = gr.Textbox(lines=7)
output_image = gr.Image(type="filepath", shape=(256,256))
b1 = gr.Button("Generate Poem")
b2 = gr.Button("Generate Image")
b1.click(poem_generate, input_word, poem_txt)
b2.click(poem_to_image, poem_txt, output_image)
#examples=examples
demo.launch(enable_queue=True, debug=True) |