app.py

import gradio as gr
import random

import requests
from PIL import Image
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
# from dotenv import load_dotenv


# Load the translation model
translation_model = AutoModelForSeq2SeqLM.from_pretrained("KarmaCST/nllb-200-distilled-600M-dz-to-en")
tokenizer = AutoTokenizer.from_pretrained("KarmaCST/nllb-200-distilled-600M-dz-to-en")


model = gr.load("models/Purz/face-projection")


src_lang="dzo_Tibt"
tgt_lang="eng_Latn"

def translate_dzongkha_to_english(text):
    translation_pipeline = pipeline("translation",
                                model=translation_model,
                                tokenizer=tokenizer,
                                src_lang=src_lang,
                                tgt_lang=tgt_lang)
    
    translated_text = translation_pipeline(text)[0]['translation_text']

    def generate_image(translated_text, seed):
        if seed is not None:
            random.seed(seed)
        
        if text in [example[0] for example in examples]:
            print(f"Using example: {text}")

        # return model(text)
    
    return model(text)

# model = gr.load("models/Purz/face-projection")

# def generate_image(text, seed):
#     if seed is not None:
#         random.seed(seed)

#     if text in [example[0] for example in examples]:
#         print(f"Using example: {text}")

#     return model(text)

examples = [
    ["Humanoid Cat Warrior, Full View", None],
    ["Warhammer Sisterhood", None],
    ["Future Robots war", None],
    ["Fantasy dragon", None]
]

interface = gr.Interface(
    fn=translate_dzongkha_to_english,
    inputs=[
        gr.Textbox(label="Type here your imagination:", placeholder="Type or click an example..."),
        gr.Slider(minimum=0, maximum=10000, step=1, label="Seed (optional)")
    ],
    outputs=gr.Image(label="Generated Image"),
    examples=examples,
    theme="NoCrypt/miku",
    description="Sorry for the inconvenience. The model is currently running on the CPU, which might affect performance. We appreciate your understanding.",
)

interface.launch()