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import gradio as gr
from transformers import pipeline
from diffusers import StableDiffusionPipeline
import torch
import os
# ✅ Load HF token safely from environment
HF_TOKEN = os.getenv("HF_TOKEN", None)
# ✅ Select device
device = "cuda" if torch.cuda.is_available() else "cpu"
# ✅ 1. Tamil → English Translator (multilingual model)
translator = pipeline(
"translation",
model="Helsinki-NLP/opus-mt-mul-en",
use_auth_token=HF_TOKEN
)
# ✅ 2. English Text Generator
generator = pipeline("text-generation", model="gpt2")
# ✅ 3. Image Generator using Stable Diffusion
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)
# ✅ Function to connect all 3 stages
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
# ✅ 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 to Image Generator",
description="🔤 Translates Tamil → English → Generates prompt → Creates an image using Stable Diffusion"
)
iface.launch()
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