import gradio as gr
import requests
from transformers import MarianMTModel, MarianTokenizer, AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import torch
import io
import os
from typing import Tuple

# Load Hugging Face API key securely
HF_API_KEY = os.getenv("HF_API_KEY")  # You must set this as an environment variable
if not HF_API_KEY:
    raise ValueError("HF_API_KEY is not set. Add it in Hugging Face 'Variables and Secrets' or local environment.")

# API Endpoint for Image Generation
IMAGE_GEN_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"
HEADERS = {"Authorization": f"Bearer {HF_API_KEY}"}

# Check if GPU is available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load Tamil-to-English Translation Model
translator_model = "Helsinki-NLP/opus-mt-mul-en"
translator = MarianMTModel.from_pretrained(translator_model).to(device)
translator_tokenizer = MarianTokenizer.from_pretrained(translator_model)

# Load Text Generation Model
generator_model = "EleutherAI/gpt-neo-1.3B"
generator = AutoModelForCausalLM.from_pretrained(generator_model).to(device)
generator_tokenizer = AutoTokenizer.from_pretrained(generator_model)
if generator_tokenizer.pad_token is None:
    generator_tokenizer.pad_token = generator_tokenizer.eos_token

def translate_tamil_to_english(text: str) -> str:
    """Translates Tamil text to English."""
    inputs = translator_tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    output = translator.generate(**inputs)
    return translator_tokenizer.decode(output[0], skip_special_tokens=True)

def generate_text(prompt: str) -> str:
    """Generates a creative text based on English input."""
    inputs = generator_tokenizer(prompt, return_tensors="pt", padding=True, truncation=True).to(device)
    output = generator.generate(**inputs, max_length=100)
    return generator_tokenizer.decode(output[0], skip_special_tokens=True)

def generate_image(prompt: str) -> Image.Image:
    """Sends request to API for image generation."""
    response = requests.post(IMAGE_GEN_URL, headers=HEADERS, json={"inputs": prompt})
    try:
        if response.status_code == 200 and response.headers["content-type"].startswith("image"):
            return Image.open(io.BytesIO(response.content))
    except Exception as e:
        print("Image generation error:", e)
    return Image.new("RGB", (300, 300), "red")  # Fallback placeholder image

def process_input(tamil_text: str) -> Tuple[str, str, Image.Image]:
    """Complete pipeline: Translation, Text Generation, and Image Generation."""
    english_text = translate_tamil_to_english(tamil_text)
    creative_text = generate_text(english_text)
    image = generate_image(english_text)
    return english_text, creative_text, image

# Gradio Interface
interface = gr.Interface(
    fn=process_input,
    inputs=gr.Textbox(label="Enter Tamil Text"),
    outputs=[
        gr.Textbox(label="Translated English Text"),
        gr.Textbox(label="Creative Text"),
        gr.Image(label="Generated Image")
    ],
    title="Tamil to English Translator & Image Generator",
    description="Enter Tamil text, and this app will translate it, generate a creative description, and create an image based on the text.",
    allow_flagging="never"  # Avoids schema-related error in Spaces
)

# Launch the app
interface.launch()