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# app.py
import gradio as gr
from transformers import pipeline, MarianMTModel, MarianTokenizer
from diffusers import StableDiffusionPipeline
import torch
import os
# Get Hugging Face token from environment (in Hugging Face Spaces this is auto-populated from secrets)
hf_token = os.getenv("HUGGINGFACE_TOKEN")
# Load Translation Pipeline: Tamil → English using MarianMT
translation_model_name = "Helsinki-NLP/opus-mt-ta-en"
translator = pipeline("translation", model=translation_model_name)
# Load Stable Diffusion Pipeline: English → Image
device = "cuda" if torch.cuda.is_available() else "cpu"
image_pipe = StableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
use_auth_token=hf_token,
torch_dtype=torch.float16 if device == "cuda" else torch.float32
).to(device)
# Core function
def translate_and_generate(tamil_text):
# Step 1: Translate Tamil to English
english_output = translator(tamil_text)[0]['translation_text']
# Step 2: Generate Image from English Text
image = image_pipe(english_output).images[0]
return english_output, image
# Gradio UI
iface = gr.Interface(
fn=translate_and_generate,
inputs=gr.Textbox(lines=2, label="Enter Tamil Text"),
outputs=[
gr.Textbox(label="Translated English Text"),
gr.Image(label="Generated Image")
],
title="Tamil-to-Image Generator 🌸",
description="Enter Tamil text. It will be translated to English and visualized using Stable Diffusion."
)
iface.launch()
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