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from transformers import pipeline, set_seed |
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import gradio as grad |
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import random |
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import re |
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gpt2_pipe = pipeline('text-generation', model='succinctly/text2image-prompt-generator') |
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with open("name.txt", "r") as f: |
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line = f.readlines() |
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def generate(starting_text): |
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seed = random.randint(1, 100000) |
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set_seed(seed) |
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if starting_text == "": |
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starting_text: str = line[random.randrange(0, len(line))] |
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starting_text: str = re.sub(r"[,:\-–.!;?_]", '', starting_text) |
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print(starting_text) |
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response = gpt2_pipe(starting_text, max_length=random.randint(20, 45), num_return_sequences=random.randint(5, 15)) |
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response_list = [] |
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for x in response: |
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if x['generated_text'].strip() != starting_text: |
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response_list.append(x['generated_text']) |
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response_end = "\n".join(response_list) |
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return response_end |
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txt = grad.Textbox(lines=1, label="English", placeholder="English Text here") |
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out = grad.Textbox(lines=5, label="Generated Text") |
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grad.Interface(fn=generate, |
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inputs=txt, |
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outputs=out, |
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allow_flagging='never', |
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cache_examples=False, |
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theme="default").launch(enable_queue=True, debug=True) |
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