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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 = """
Tweet: I hate it when my phone battery dies.
Sentiment: Negative
###
Tweet: My day has been 👍
Sentiment: Positive
###
Tweet: This is the link to the article
Sentiment: Neutral
###
Tweet: This new music video was incredibile
Sentiment:
"""
examples = [["river"], ["night"], ["trees"],["table"],["laughs"]]
def poem2_generate(word):
p = 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"If there was 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] # +"."
poem = poem.replace('?','')
print(f"Poem being returned is: {poem}")
return poem
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"If there was 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] # +"."
poem = poem.replace('?','')
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>Few Shot Learning for Text - Word Image Search</center></h1>")
gr.Markdown(
"<div>This example uses prompt engineering to search for answers in EleutherAI large language model and follows the pattern of Few Shot Learning where you supply A 1) Task Description, 2) a Set of Examples, and 3) a Prompt. Then few shot learning can show the answer given the pattern of the examples. More information on how it works is here: https://huggingface.co/blog/few-shot-learning-gpt-neo-and-inference-api Also the Eleuther AI was trained on texts called The Pile which is documented here on its github. Review this to find what types of language patterns it can generate text for as answers: https://github.com/EleutherAI/the-pile"
)
with gr.Row():
input_word = gr.Textbox(lines=7, placeholder=prompt)
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(poem2_generate, input_word, poem_txt)
b2.click(poem_to_image, poem_txt, output_image)
#examples=examples
demo.launch(enable_queue=True, debug=True) |