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