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from transformers import MBartForConditionalGeneration, MBart50Tokenizer, AutoModelForCausalLM, AutoTokenizer, pipeline
import gradio as gr
import torch
from diffusers import FluxPipeline
# 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 a smaller text generation model to reduce generation time
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)
# Create a pipeline for text generation using the selected model
text_generator = pipeline("text-generation", model=text_model, tokenizer=text_tokenizer)
# Set up the new FluxPipeline for the text-to-image model
pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload() # Enable CPU offloading to save GPU memory if needed
# Function to generate an image using the new FluxPipeline model
def generate_image_from_text(translated_text):
try:
print(f"Generating image from translated text: {translated_text}")
# Use the FluxPipeline to generate an image from the text
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 shorter 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.",
)
# Launch the Gradio app
iface.launch()
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