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| from transformers import MBartForConditionalGeneration, MBart50Tokenizer, AutoModelForCausalLM, AutoTokenizer, pipeline | |
| import gradio as gr | |
| import requests | |
| import io | |
| from PIL import Image | |
| import os | |
| # Set up the Hugging Face API key from environment variables | |
| hf_api_key = os.getenv("new_hf_token") | |
| if not hf_api_key: | |
| raise ValueError("Hugging Face API key not found! Please set the 'HF_API_KEY' environment variable.") | |
| headers = {"Authorization": f"Bearer {hf_api_key}"} | |
| # Define the text-to-image model URL | |
| API_URL = "https://api-inference.huggingface.co/models/CompVis/stable-diffusion-v1-4" | |
| # Load the translation model and tokenizer | |
| translation_model_name = "facebook/mbart-large-50-many-to-one-mmt" | |
| tokenizer = MBart50Tokenizer.from_pretrained(translation_model_name) | |
| translation_model = MBartForConditionalGeneration.from_pretrained(translation_model_name) | |
| # Load a text generation model from Hugging Face | |
| text_generation_model_name = "EleutherAI/gpt-neo-2.7B" # Use "EleutherAI/gpt-j-6B" for better quality | |
| text_tokenizer = AutoTokenizer.from_pretrained(text_generation_model_name) | |
| text_model = AutoModelForCausalLM.from_pretrained(text_generation_model_name, device_map="auto", torch_dtype=torch.float32) | |
| # Create a pipeline for text generation | |
| text_generator = pipeline("text-generation", model=text_model, tokenizer=text_tokenizer) | |
| # Function to generate an image using Hugging Face's text-to-image model | |
| def generate_image_from_text(translated_text): | |
| try: | |
| # Send the translated text to the text-to-image model | |
| response = requests.post(API_URL, headers=headers, json={"inputs": translated_text}) | |
| # Check if the response is successful | |
| if response.status_code != 200: | |
| return None, f"Error generating image: {response.text}" | |
| # Read and return the generated image | |
| image_bytes = response.content | |
| image = Image.open(io.BytesIO(image_bytes)) | |
| return image, None | |
| except Exception as e: | |
| return None, f"Error during image generation: {e}" | |
| # Define the function to translate Tamil text, generate an image, and create a descriptive text | |
| def translate_generate_image_and_text(tamil_text): | |
| try: | |
| # Step 1: Translate Tamil text to English | |
| tokenizer.src_lang = "ta_IN" | |
| inputs = tokenizer(tamil_text, return_tensors="pt") | |
| translated_tokens = translation_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] | |
| except Exception as e: | |
| return f"Error during translation: {e}", None, None | |
| try: | |
| # Step 2: Use the translated English text to generate an image directly | |
| image, error_message = generate_image_from_text(translated_text) | |
| if error_message: | |
| return translated_text, None, error_message | |
| except Exception as e: | |
| return translated_text, None, f"Error during image generation: {e}" | |
| try: | |
| # Step 3: Generate a descriptive English text using GPT-Neo based on the translated text | |
| descriptive_text = text_generator(translated_text, max_length=100, num_return_sequences=1, temperature=0.7, top_p=0.9)[0]['generated_text'] | |
| except Exception as e: | |
| return translated_text, image, f"Error during text generation: {e}" | |
| return translated_text, image, descriptive_text | |
| # Gradio interface setup | |
| iface = gr.Interface( | |
| fn=translate_generate_image_and_text, | |
| inputs=gr.Textbox(lines=2, placeholder="Enter Tamil text here..."), | |
| outputs=[gr.Textbox(label="Translated English Text"), | |
| gr.Image(label="Generated Image"), | |
| gr.Textbox(label="Generated Descriptive Text")], | |
| title="Tamil to English Translation, Image Creation, and Descriptive Text Generation", | |
| description="Translate Tamil text to English using Facebook's mbart-large-50 model, create an image using the translated text, and generate a descriptive text based on the translated content.", | |
| ) | |
| # Launch the Gradio app | |
| iface.launch() | |