Spaces:
Sleeping
Sleeping
| from transformers import MarianMTModel, MarianTokenizer, AutoModelForCausalLM, AutoTokenizer | |
| import requests | |
| from PIL import Image | |
| import io | |
| import gradio as gr | |
| import os | |
| # Load the MarianMT model and tokenizer for translation (Tamil to English) | |
| model_name = "Helsinki-NLP/opus-mt-mul-en" | |
| translation_model = MarianMTModel.from_pretrained(model_name) | |
| translation_tokenizer = MarianTokenizer.from_pretrained(model_name) | |
| # Load GPT-Neo for creative text generation | |
| text_generation_model_name = "EleutherAI/gpt-neo-1.3B" | |
| text_generation_model = AutoModelForCausalLM.from_pretrained(text_generation_model_name) | |
| text_generation_tokenizer = AutoTokenizer.from_pretrained(text_generation_model_name) | |
| # Add padding token to GPT-Neo tokenizer if not present | |
| if text_generation_tokenizer.pad_token is None: | |
| text_generation_tokenizer.add_special_tokens({'pad_token': '[PAD]'}) | |
| # Hugging Face API for FLUX.1 image generation | |
| API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev" | |
| headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_KEY')}"} | |
| # Query Hugging Face API to generate image | |
| def query(payload): | |
| response = requests.post(API_URL, headers=headers, json=payload) | |
| if response.status_code == 200: | |
| return response.content | |
| else: | |
| return f"Error: {response.status_code} - {response.text}" | |
| # Translate Tamil text to English | |
| def translate_text(tamil_text): | |
| inputs = translation_tokenizer(tamil_text, return_tensors="pt", padding=True, truncation=True) | |
| translated_tokens = translation_model.generate(**inputs) | |
| translation = translation_tokenizer.decode(translated_tokens[0], skip_special_tokens=True) | |
| return translation | |
| # Generate an image based on the translated text | |
| def generate_image(prompt): | |
| image_bytes = query({"inputs": prompt}) | |
| if isinstance(image_bytes, str) and "Error" in image_bytes: | |
| return image_bytes # Return the error message if there's a problem with the API call | |
| image = Image.open(io.BytesIO(image_bytes)) | |
| return image | |
| # Generate creative text based on the translated English text | |
| def generate_creative_text(translated_text): | |
| inputs = text_generation_tokenizer(translated_text, return_tensors="pt", padding=True, truncation=True) | |
| generated_tokens = text_generation_model.generate(**inputs, max_length=100) | |
| creative_text = text_generation_tokenizer.decode(generated_tokens[0], skip_special_tokens=True) | |
| return creative_text | |
| # Function to handle the full workflow | |
| def translate_generate_image_and_text(tamil_text): | |
| # Step 1: Translate Tamil to English | |
| translated_text = translate_text(tamil_text) | |
| # Step 2: Generate an image from the translated text | |
| image = generate_image(translated_text) | |
| # Step 3: Generate creative text from the translated text | |
| creative_text = generate_creative_text(translated_text) | |
| return translated_text, creative_text, image | |
| # Create Gradio interface | |
| interface = gr.Interface( | |
| fn=translate_generate_image_and_text, | |
| inputs=gr.Textbox(label="Enter Tamil Text"), # Input for Tamil text | |
| outputs=[ | |
| gr.Textbox(label="Translated Text"), # Output for translated text | |
| gr.Textbox(label="Creative Generated Text"),# Output for creative text | |
| gr.Image(label="Generated Image") # Output for generated 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 | |
| interface.launch() | |