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import gradio as gr |
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import random |
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import requests |
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from PIL import Image |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline |
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translation_model = AutoModelForSeq2SeqLM.from_pretrained("KarmaCST/nllb-200-distilled-600M-dz-to-en") |
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tokenizer = AutoTokenizer.from_pretrained("KarmaCST/nllb-200-distilled-600M-dz-to-en") |
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model = gr.load("models/Purz/face-projection") |
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src_lang="dzo_Tibt" |
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tgt_lang="eng_Latn" |
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def translate_dzongkha_to_english(text): |
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translation_pipeline = pipeline("translation", |
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model=translation_model, |
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tokenizer=tokenizer, |
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src_lang=src_lang, |
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tgt_lang=tgt_lang) |
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translated_text = translation_pipeline(text)[0]['translation_text'] |
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def generate_image(translated_text, seed): |
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if seed is not None: |
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random.seed(seed) |
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if text in [example[0] for example in examples]: |
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print(f"Using example: {text}") |
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return model(text) |
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examples = [ |
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["Humanoid Cat Warrior, Full View", None], |
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["Warhammer Sisterhood", None], |
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["Future Robots war", None], |
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["Fantasy dragon", None] |
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] |
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interface = gr.Interface( |
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fn=translate_dzongkha_to_english, |
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inputs=[ |
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gr.Textbox(label="Type here your imagination:", placeholder="Type or click an example..."), |
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gr.Slider(minimum=0, maximum=10000, step=1, label="Seed (optional)") |
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], |
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outputs=gr.Image(label="Generated Image"), |
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examples=examples, |
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theme="NoCrypt/miku", |
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description="Sorry for the inconvenience. The model is currently running on the CPU, which might affect performance. We appreciate your understanding.", |
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) |
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interface.launch() |
