from transformers import pipeline, set_seed import gradio as grad import random import re gpt2_pipe = pipeline('text-generation', model='succinctly/text2image-prompt-generator') with open("name.txt", "r") as f: line = f.readlines() def generate(starting_text): for count in range(6): seed = random.randint(100, 1000000) set_seed(seed) # If the text field is empty if starting_text == "": starting_text: str = line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize() starting_text: str = re.sub(r"[,:\-–.!;?_]", '', starting_text) print(starting_text) response = gpt2_pipe(starting_text, max_length=random.randint(60, 90), num_return_sequences=8) response_list = [] for x in response: resp = x['generated_text'].strip() if resp != starting_text and len(resp) > (len(starting_text) + 4) and resp.endswith((":", "-", "—")) is False: response_list.append(resp) response_end = "\n".join(response_list) response_end = re.sub('[^ ]+\.[^ ]+','', response_end) response_end = response_end.replace("<", "").replace(">", "") if response_end != "": return response_end if count == 5: return response_end txt = grad.Textbox(lines=1, label="English", placeholder="English Text here") out = grad.Textbox(lines=6, label="Generated Text") examples = [["mythology of the Slavs"], ["All-seeing eye monitors these world"], ["astronaut dog"], ["A monochrome forest of ebony trees"], ["sad view of worker in office,"], ["Headshot photo portrait of John Lennon"], ["wide field with thousands of blue nemophila,"]] title = "Aiconvert.online" description = "Ai Image Prompt Generator." article = "
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" grad.Interface(fn=generate, inputs=txt, outputs=out, examples=examples, title=title, description=description, article=article, allow_flagging='never', cache_examples=False).queue(concurrency_count=1, api_open=False).launch(show_api=False, show_error=True)