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| # -*- coding: utf-8 -*- | |
| """gen ai project f.ipynb | |
| Automatically generated by Colab. | |
| Original file is located at | |
| https://colab.research.google.com/drive/1iF7hdOjWNeFUtGvUYdaFsBErJGnY1h5J | |
| """ | |
| import os | |
| import asyncio | |
| from huggingface_hub import login | |
| from transformers import MarianMTModel, MarianTokenizer, pipeline, AutoTokenizer, AutoModelForCausalLM | |
| import aiohttp | |
| import io | |
| from PIL import Image | |
| import matplotlib.pyplot as plt | |
| import gradio as gr | |
| # Retrieve the actual token from the environment variable | |
| hf_token = os.getenv("HF_TOKEN") | |
| # Check if the token is retrieved properly | |
| if hf_token: | |
| # Use the retrieved token | |
| login(token=hf_token, add_to_git_credential=True) | |
| else: | |
| raise ValueError("Hugging Face token not found in environment variables.") | |
| # Load the translation model and tokenizer (cached for faster loading) | |
| model_name = "Helsinki-NLP/opus-mt-mul-en" | |
| tokenizer = MarianTokenizer.from_pretrained(model_name, cache_dir="./cache") | |
| model = MarianMTModel.from_pretrained(model_name, cache_dir="./cache") | |
| # Create a translation pipeline | |
| translator = pipeline("translation", model=model, tokenizer=tokenizer) | |
| # Load GPT-Neo model for creative text generation (cached) | |
| gpt_neo_tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M", cache_dir="./cache") | |
| gpt_neo_model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M", cache_dir="./cache") | |
| # API credentials and endpoint for image generation | |
| API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev" | |
| headers = {"Authorization": f"Bearer {hf_token}"} | |
| # Function for translation (batch translation for multiple inputs) | |
| def translate_text(tamil_text): | |
| try: | |
| translation = translator(tamil_text, max_length=40) | |
| translated_text = translation[0]['translation_text'] | |
| return translated_text | |
| except Exception as e: | |
| return f"An error occurred: {str(e)}" | |
| # Asynchronous function to send payload and generate image | |
| async def generate_image_async(prompt): | |
| try: | |
| async with aiohttp.ClientSession() as session: | |
| async with session.post(API_URL, headers=headers, json={"inputs": prompt}) as response: | |
| if response.status == 200: | |
| print("API call successful, generating image...") | |
| image_bytes = await response.read() | |
| # Try opening the image | |
| try: | |
| image = Image.open(io.BytesIO(image_bytes)) | |
| return image | |
| except Exception as e: | |
| print(f"Error opening image: {e}") | |
| return None | |
| else: | |
| print(f"Failed to get image: Status code {response.status}") | |
| return None | |
| except Exception as e: | |
| print(f"An error occurred: {e}") | |
| return None | |
| # Generate creative text based on the translated text (with optimization for generation) | |
| def generate_creative_text(translated_text, max_length=50): | |
| input_ids = gpt_neo_tokenizer(translated_text, return_tensors='pt').input_ids | |
| generated_text_ids = gpt_neo_model.generate(input_ids, max_length=max_length, num_return_sequences=1, do_sample=True, top_k=50) | |
| creative_text = gpt_neo_tokenizer.decode(generated_text_ids[0], skip_special_tokens=True) | |
| return creative_text | |
| # Handle the full workflow: translate, generate image, generate creative text | |
| async def translate_generate_image_and_text(tamil_text): | |
| # Step 1: Translate Tamil text to English | |
| translated_text = translate_text(tamil_text) | |
| # Step 2: Generate an image based on the translated text asynchronously | |
| image = await generate_image_async(translated_text) | |
| # Step 3: Generate creative text based on the translated text | |
| creative_text = generate_creative_text(translated_text) | |
| return translated_text, creative_text, image | |
| # Display image | |
| def show_image(image): | |
| if image: | |
| plt.imshow(image) | |
| plt.axis('off') # Hide axes | |
| plt.show() | |
| else: | |
| print("No image to display") | |
| # Create Gradio interface with live updates for faster feedback | |
| interface = gr.Interface( | |
| fn=lambda tamil_text: asyncio.run(translate_generate_image_and_text(tamil_text)), | |
| inputs="text", | |
| outputs=["text", "text", "image"], | |
| title="Optimized Tamil to English Translation, Image Generation & Creative Text", | |
| description="Enter Tamil text to translate to English, generate an image, and create creative text based on the translation.", | |
| live=True # Enables real-time outputs for faster feedback | |
| ) | |
| # Launch Gradio app | |
| interface.launch() | |