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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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src_lang="dzo_Tibt" |
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tgt_lang="eng_Latn" |
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model = gr.load("models/Purz/face-projection") |
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def generate_image(text, seed): |
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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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text = translation_pipeline(text)[0]['translation_text'] |
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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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["བྱི་ཅུང་ཚུ་གངས་རི་གི་ཐོག་ཁར་འཕུར།", None], |
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["པཱ་རོ་ཁྲོམ་གྱི་ཐོག་ཁར་གནམ་གྲུ་འཕུར།",None], |
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["པཱ་རོ་ཁྲོམ་གྱི་ཐོག་ཁར་ ཤིང་ཚུ་གི་བར་ན་ གནམ་གྲུ་འཕུར་བའི་འཐོང་གནང་།",None], |
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["སློབ་ཕྲུག་ཚུ་ ཆརཔ་ནང་རྐང་རྩེད་རྩེ་དེས།",None] |
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] |
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interface = gr.Interface( |
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fn=generate_image, |
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inputs=[ |
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gr.Textbox(label="Text to Image:", placeholder="Dzongkha text..."), |
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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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title="Dzongkha Text to Image Generation", |
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examples=examples, |
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article="<h1>Created By:</h1>Mr. Karma Wangchuk<br>Lecturer<br>Information Technology Department<br>College of Science and Technology<br>Rinchending Phuentsholing<br>Chhukha Bhutan<br>", |
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description="The model is currently running on the CPU, which might affect performance.", |
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) |
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interface.launch() |
