import gradio as gr
from transformers import MBartForConditionalGeneration, MBart50Tokenizer, AutoModelForCausalLM, AutoTokenizer, pipeline
from diffusers import DiffusionPipeline
import torch
from PIL import Image

# Load the Translation Model (MBART for Tamil to English Translation)
model_name = "facebook/mbart-large-50-many-to-one-mmt"
tokenizer = MBart50Tokenizer.from_pretrained(model_name)
model = MBartForConditionalGeneration.from_pretrained(model_name)

# Load the Text Generation Model (for generating a short paragraph)
text_generation_model_name = "EleutherAI/gpt-neo-1.3B"
text_tokenizer = AutoTokenizer.from_pretrained(text_generation_model_name)
text_model = AutoModelForCausalLM.from_pretrained(text_generation_model_name)
text_generator = pipeline("text-generation", model=text_model, tokenizer=text_tokenizer)

# Load the Stable Diffusion XL Model for Image Generation in full precision (fp32)
pipe = DiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",  # Remove torch_dtype and variant for CPU-friendly precision
)

# Check if GPU is available and use it for inference
if torch.cuda.is_available():
    pipe.to("cuda")  # Use GPU for faster inference
else:
    pipe.to("cpu")  # Use CPU if GPU is not available

# Function to generate image from text prompt using Stable Diffusion XL
def generate_image_from_text(translated_text):
    try:
        print(f"Generating image from translated text: {translated_text}")
        # Generate the image using the pipeline
        image = pipe(prompt=translated_text).images[0]
        print("Image generation completed.")
        return image
    except Exception as e:
        print(f"Error during image generation: {e}")
        return None

# Function to generate a short paragraph from the translated text
def generate_short_paragraph_from_text(translated_text):
    try:
        print(f"Generating a short paragraph from translated text: {translated_text}")
        paragraph = text_generator(
            translated_text, 
            max_length=80,  # Reduced to 80 tokens
            num_return_sequences=1, 
            temperature=0.6, 
            top_p=0.8,
            truncation=True  # Added truncation to avoid long sequences
        )[0]['generated_text']
        print(f"Paragraph generation completed: {paragraph}")
        return paragraph
    except Exception as e:
        print(f"Error during paragraph generation: {e}")
        return f"Error during paragraph generation: {e}"

# Function to translate Tamil text, generate a short paragraph, and create an image
def translate_generate_paragraph_and_image(tamil_text):
    # Step 1: Translate Tamil text to English using mbart-large-50
    try:
        print("Translating Tamil text to English...")
        tokenizer.src_lang = "ta_IN"
        inputs = tokenizer(tamil_text, return_tensors="pt")
        translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
        translated_text = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
        print(f"Translation completed: {translated_text}")
    except Exception as e:
        return f"Error during translation: {e}", "", None

    # Step 2: Generate a shorter paragraph based on the translated English text
    paragraph = generate_short_paragraph_from_text(translated_text)
    if "Error" in paragraph:
        return translated_text, paragraph, None

    # Step 3: Generate an image using the translated English text with the new model
    image = generate_image_from_text(translated_text)

    return translated_text, paragraph, image

# Define Gradio Interface
def interface(tamil_text):
    translated_text, paragraph, image = translate_generate_paragraph_and_image(tamil_text)
    return translated_text, paragraph, image

# Create Gradio Interface (with the image output)
iface = gr.Interface(
    fn=interface, 
    inputs=gr.Textbox(lines=2, placeholder="Enter Tamil text here..."), 
    outputs=[
        gr.Textbox(label="Translated Text"),
        gr.Textbox(label="Generated Paragraph"),
        gr.Image(type="pil", label="Generated Image")
    ],
    title="Tamil Text Translation, Paragraph Generation, and Image Generation",
    description="Input Tamil text, and this tool will translate it, generate a short paragraph, and create an image based on the translated text."
)

# Launch the Gradio app with share=True to create a shareable link
iface.launch(share=True)