app.py

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

# Load the Hugging Face API key from environment variables
hf_api_key = os.getenv("multimodel_token")
if hf_api_key is None:
    raise ValueError("Hugging Face API key not found! Please set the 'full_token' environment variable.")

# Load the translation model and tokenizer
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
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 alvdansen/flux-koda image generation model using Diffusers
pipe = StableDiffusionPipeline.from_pretrained("alvdansen/flux-koda", use_auth_token=hf_api_key)
pipe.to("cuda")  # Use GPU for faster generation, if available

# Function to generate an image using alvdansen/flux-koda model
def generate_image_from_text(translated_text):
    try:
        print(f"Generating image from translated text: {translated_text}")
        # Generate the image using the alvdansen/flux-koda model
        image = pipe(translated_text).images[0]
        print("Image generation completed.")
        return image, None
    except Exception as e:
        print(f"Error during image generation: {e}")
        return None, f"Error during image generation: {e}"

# Function to generate a short paragraph based on the translated text
def generate_short_paragraph_from_text(translated_text):
    try:
        print(f"Generating a short paragraph from translated text: {translated_text}")
        # Generate a shorter paragraph from the translated text using smaller settings
        paragraph = text_generator(translated_text, max_length=150, num_return_sequences=1, temperature=0.2, top_p=0.8)[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}"

# Define the 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, 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, None

    # Step 3: Generate an image using the translated English text
    image, error_message = generate_image_from_text(translated_text)
    if error_message:
        return translated_text, paragraph, None, error_message

    return translated_text, paragraph, image, None

# Gradio interface setup
iface = gr.Interface(
    fn=translate_generate_paragraph_and_image,
    inputs=gr.Textbox(lines=2, placeholder="Enter Tamil text here..."),
    outputs=[gr.Textbox(label="Translated English Text"), 
             gr.Textbox(label="Generated Short Paragraph"),
             gr.Image(label="Generated Image")],
    title="Tamil to English Translation, Short Paragraph Generation, and Image Creation",
    description="Translate Tamil text to English using Facebook's mbart-large-50 model, generate a short paragraph, and create an image using the translated text.",
)

iface.launch()