app.py

import gradio as gr
from transformers import pipeline
from diffusers import StableDiffusionPipeline
import torch
import os

HF_TOKEN = os.getenv("HF_TOKEN")
if HF_TOKEN is None:
    raise ValueError("Set HF_TOKEN in env variables.")

device = "cuda" if torch.cuda.is_available() else "cpu"

# ✅ Use multilingual model that supports Tamil→English
translator = pipeline(
    "translation",
    model="Helsinki-NLP/opus-mt-mul-en",
    use_auth_token=HF_TOKEN
)

generator = pipeline("text-generation", model="gpt2")

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_input):
    translated = translator(tamil_input, max_length=100)[0]['translation_text']
    generated = generator(translated, max_length=50, num_return_sequences=1)[0]['generated_text'].strip()
    image = image_pipe(generated).images[0]
    return translated, generated, image

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→Image Generator",
    description="Translate Tamil → English, generate prompt → create image.",
    allow_flagging="never"
)

iface.launch()