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import gradio as gr |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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import requests |
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from diffusers import DiffusionPipeline |
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image = gr.outputs.Image(type="pil", label="Your result") |
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css = ".output-image{height: 528px !important} .output-carousel .output-image{height:272px !important} a{text-decoration: underline}" |
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def translate(hindi_sentence): |
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inputs = tokenizer.encode( |
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hindi_sentence, return_tensors="pt",padding=True,max_length=512,truncation=True) |
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outputs = model.generate( |
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inputs, max_length=128, num_beams=None, early_stopping=True) |
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translated = tokenizer.decode(outputs[0]).replace('<pad>',"").strip().lower() |
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model_id = "CompVis/ldm-text2im-large-256" |
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ldm = DiffusionPipeline.from_pretrained(model_id) |
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images = ldm([translated], num_inference_steps=50, eta=0.3, guidance_scale=6)["sample"] |
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return images |
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tokenizer = AutoTokenizer.from_pretrained("salesken/translation-hi-en") |
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model = AutoModelForSeq2SeqLM.from_pretrained("salesken/translation-hi-en") |
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exp = [["पानी पे चलती रेलगाड़ी"]] |
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iface = gr.Interface(fn=translate, inputs="text",outputs=gr.Gallery(), examples=exp) |
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iface.launch() |
