app.py

import os
import torch
import gradio as gr
from huggingface_hub import login
from transformers import (
    AutoTokenizer,
    AutoModelForSeq2SeqLM,
    GPT2LMHeadModel,
    GPT2Tokenizer
)
from diffusers import StableDiffusionPipeline

# Authenticate via token
hf_token = os.getenv("HUGGINGFACE_TOKEN")
if hf_token:
    login(token=hf_token)

# 📚 Tamil ↔ English Translation (Multilingual M2M100 model)
trans_checkpoint = "Hemanth-thunder/english-tamil-mt"
trans_tokenizer = AutoTokenizer.from_pretrained(trans_checkpoint)
trans_model = AutoModelForSeq2SeqLM.from_pretrained(trans_checkpoint)

# 🧠 GPT-2 English Text Generation
gpt_tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
gpt_model = GPT2LMHeadModel.from_pretrained("gpt2")
gpt_model.eval()

# 🎨 Stable Diffusion
device = "cuda" if torch.cuda.is_available() else "cpu"
sd_pipe = StableDiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    use_auth_token=hf_token,
    torch_dtype=torch.float16 if device == "cuda" else torch.float32
).to(device)

# Pipeline: Tamil → English → GPT-2 → Image
def tam_to_image_pipeline(tamil_text):
    # Translate Tamil → English
    inputs = trans_tokenizer(tamil_text, return_tensors="pt", truncation=True)
    translated_ids = trans_model.generate(**inputs, max_length=128)
    english_text = trans_tokenizer.decode(translated_ids[0], skip_special_tokens=True)

    # Generate additional English Description via GPT-2
    input_ids = gpt_tokenizer.encode(english_text, return_tensors="pt")
    with torch.no_grad():
        gpt_output = gpt_model.generate(
            input_ids,
            max_length=60,
            num_return_sequences=1,
            no_repeat_ngram_size=2,
            pad_token_id=gpt_tokenizer.eos_token_id
        )
    generated_text = gpt_tokenizer.decode(gpt_output[0], skip_special_tokens=True)

    # Generate image from description
    image = sd_pipe(generated_text).images[0]

    return english_text, generated_text, image

# Gradio UI
interface = gr.Interface(
    fn=tam_to_image_pipeline,
    inputs=gr.Textbox(label="Enter Tamil Text"),
    outputs=[
        gr.Textbox(label="Translated English Text"),
        gr.Textbox(label="Generated Description"),
        gr.Image(label="Generated Image")
    ],
    title="Tamil → Image Generator",
    description="📘 Tamil to English (M2M100) → GPT‑2 → Image via Stable Diffusion"
)

interface.launch()