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import os
import gradio as gr
from transformers import pipeline
from diffusers import StableDiffusionPipeline
import torch
HF_TOKEN = os.getenv("HF_TOKEN")
# 1. Tamil to English translator (public model, no token required)
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-mul-en")
# 2. English text generator (GPT-2, public model, no token required)
generator = pipeline("text-generation", model="gpt2")
# 3. Stable Diffusion image generator (needs token)
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
)
image_pipe = image_pipe.to(device)
def generate_image_from_tamil(tamil_text):
# Translate Tamil → English
translated = translator(tamil_text, max_length=100)[0]['translation_text']
# Generate English text from translated sentence
generated = generator(translated, max_length=50, num_return_sequences=1)[0]['generated_text']
# Generate image from generated English text
image = image_pipe(generated).images[0]
return translated, generated, image
# Create Gradio interface
iface = gr.Interface(
fn=generate_image_from_tamil,
inputs=gr.Textbox(lines=2, label="Enter Tamil Text"),
outputs=[
gr.Textbox(label="Translated English Text"),
gr.Textbox(label="Generated English Prompt"),
gr.Image(label="Generated Image")
],
title="Tamil Text to English and Image Generator",
description="Translate Tamil to English, generate English text, and create image using Stable Diffusion."
)
# Launch Gradio app with public link
iface.launch(share=True)
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