app.py

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)