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| # Import necessary libraries | |
| import requests | |
| import io | |
| from PIL import Image | |
| import matplotlib.pyplot as plt | |
| from transformers import MarianMTModel, MarianTokenizer, pipeline | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import gradio as gr | |
| import os # For accessing environment variables | |
| # Constants for model names and API URLs | |
| class Constants: | |
| TRANSLATION_MODEL_NAME = "Helsinki-NLP/opus-mt-mul-en" | |
| IMAGE_GENERATION_API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev" | |
| GPT_NEO_MODEL_NAME = "EleutherAI/gpt-neo-125M" | |
| # Get the Hugging Face API token from environment variables | |
| HEADERS = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_KEY')}"} | |
| # Translation Class | |
| class Translator: | |
| def __init__(self): | |
| self.tokenizer = MarianTokenizer.from_pretrained(Constants.TRANSLATION_MODEL_NAME) | |
| self.model = MarianMTModel.from_pretrained(Constants.TRANSLATION_MODEL_NAME) | |
| self.pipeline = pipeline("translation", model=self.model, tokenizer=self.tokenizer) | |
| def translate(self, tamil_text): | |
| """Translate Tamil text to English.""" | |
| try: | |
| translation = self.pipeline(tamil_text, max_length=40) | |
| return translation[0]['translation_text'] | |
| except Exception as e: | |
| return f"Translation error: {str(e)}" | |
| # Image Generation Class | |
| class ImageGenerator: | |
| def __init__(self): | |
| self.api_url = Constants.IMAGE_GENERATION_API_URL | |
| def generate(self, prompt): | |
| """Generate an image based on the given prompt.""" | |
| try: | |
| response = requests.post(self.api_url, headers=Constants.HEADERS, json={"inputs": prompt}) | |
| if response.status_code == 200: | |
| image_bytes = response.content | |
| return Image.open(io.BytesIO(image_bytes)) | |
| else: | |
| print(f"Image generation failed: Status code {response.status_code}") | |
| return None | |
| except Exception as e: | |
| print(f"Image generation error: {str(e)}") | |
| return None | |
| # Creative Text Generation Class | |
| class CreativeTextGenerator: | |
| def __init__(self): | |
| self.tokenizer = AutoTokenizer.from_pretrained(Constants.GPT_NEO_MODEL_NAME) | |
| self.model = AutoModelForCausalLM.from_pretrained(Constants.GPT_NEO_MODEL_NAME) | |
| def generate(self, translated_text): | |
| """Generate creative text based on translated text.""" | |
| input_ids = self.tokenizer(translated_text, return_tensors='pt').input_ids | |
| generated_text_ids = self.model.generate(input_ids, max_length=100) | |
| return self.tokenizer.decode(generated_text_ids[0], skip_special_tokens=True) | |
| # Main Application Class | |
| class TransArtApp: | |
| def __init__(self): | |
| self.translator = Translator() | |
| self.image_generator = ImageGenerator() | |
| self.creative_text_generator = CreativeTextGenerator() | |
| def process(self, tamil_text): | |
| """Handle the full workflow: translate, generate image, and creative text.""" | |
| translated_text = self.translator.translate(tamil_text) | |
| image = self.image_generator.generate(translated_text) | |
| creative_text = self.creative_text_generator.generate(translated_text) | |
| return translated_text, creative_text, image | |
| # Function to display images | |
| def show_image(image): | |
| """Display an image using matplotlib.""" | |
| if image: | |
| plt.imshow(image) | |
| plt.axis('off') # Hide axes | |
| plt.show() | |
| else: | |
| print("No image to display.") | |
| # Create an instance of the TransArt app | |
| app = TransArtApp() | |
| # Gradio interface function | |
| def gradio_interface(tamil_text): | |
| """Interface function for Gradio.""" | |
| translated_text, creative_text, image = app.process(tamil_text) | |
| return translated_text, creative_text, image | |
| # Create Gradio interface | |
| interface = gr.Interface( | |
| fn=gradio_interface, | |
| inputs="text", | |
| outputs=["text", "text", "image"], | |
| title="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." | |
| ) | |
| # Launch Gradio app | |
| if __name__ == "__main__": | |
| interface.launch() |