import gradio as gr
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM
from diffusers import StableDiffusionPipeline
import torch

# 1. Tamil to English Translator
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ta-en")

# 2. English Text Generator (you can use GPT2 or any causal model)
generator = pipeline("text-generation", model="gpt2")

# 3. Image Generator using Stable Diffusion
device = "cuda" if torch.cuda.is_available() else "cpu"
image_pipe = StableDiffusionPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4",
    torch_dtype=torch.float16 if device == "cuda" else torch.float32
)
image_pipe = image_pipe.to(device)

# 👇 Combined function
def generate_image_from_tamil(tamil_input):
    # Step 1: Translate Tamil → English
    translated = translator(tamil_input, max_length=100)[0]['translation_text']

    # Step 2: Generate English sentence based on translated input
    generated = generator(translated, max_length=50, num_return_sequences=1)[0]['generated_text']

    # Step 3: Generate Image based on English text
    image = image_pipe(generated).images[0]

    return translated, generated, image

# 🎨 Gradio UI
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 story → creates image using Stable Diffusion."
)

iface.launch()