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| from transformers import MarianMTModel, AutoModelForSeq2SeqLM, AutoTokenizer, GPTNeoForCausalLM, GPT2Tokenizer | |
| import gradio as gr | |
| import requests | |
| import io | |
| from PIL import Image | |
| import os # Import os to access environment variables | |
| # Load MarianMT model and tokenizer for Tamil to English translation | |
| model_name = "Helsinki-NLP/opus-mt-mul-en" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| # Load GPT-Neo model and tokenizer | |
| gpt_neo_model_name = "EleutherAI/gpt-neo-125M" | |
| gpt_neo_model = GPTNeoForCausalLM.from_pretrained(gpt_neo_model_name) | |
| gpt_neo_tokenizer = GPT2Tokenizer.from_pretrained(gpt_neo_model_name) | |
| # Retrieve the API URL and headers for Flux.1 from environment variables | |
| API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev" | |
| headers = {"Authorization": f"Bearer {os.environ.get('HUGGINGFACE_API_KEY')}"} # Use the environment variable | |
| def generate_image_from_text(english_text): | |
| payload = {"inputs": english_text} | |
| response = requests.post(API_URL, headers=headers, json=payload) | |
| if response.status_code == 200: | |
| image_bytes = response.content | |
| image = Image.open(io.BytesIO(image_bytes)) | |
| return image | |
| else: | |
| return None # Handle error appropriately | |
| def translate_tamil_to_english(tamil_text): | |
| # Tokenize input and generate translation | |
| inputs = tokenizer(tamil_text, return_tensors="pt", padding=True) | |
| translated_tokens = model.generate(**inputs) | |
| translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True) | |
| return translated_text | |
| def generate_creative_text(english_text): | |
| input_ids = gpt_neo_tokenizer.encode(english_text, return_tensors='pt') | |
| output = gpt_neo_model.generate(input_ids, max_length=150, num_return_sequences=1) | |
| return gpt_neo_tokenizer.decode(output[0], skip_special_tokens=True) | |
| def process_input(tamil_text): | |
| # Step 1: Translate Tamil to English | |
| translated_text = translate_tamil_to_english(tamil_text) | |
| # Step 2: Generate Image from Translated English Text | |
| image = generate_image_from_text(translated_text) | |
| # Step 3: Generate Creative Text | |
| creative_text = generate_creative_text(translated_text) | |
| # Return results (translated text, image, and creative text) | |
| return translated_text, image, creative_text | |
| # Create a Gradio interface with input and output components | |
| interface = gr.Interface( | |
| fn=process_input, | |
| inputs=gr.Textbox(lines=2, placeholder="Enter Tamil text..."), | |
| outputs=[gr.Textbox(label="Translated Text (English)"), | |
| gr.Image(label="Generated Image"), | |
| gr.Textbox(label="Creative Text")], | |
| title="Tamil to Creative Text & Image Generator", | |
| description="Enter Tamil text to translate, generate an image, and produce creative content." | |
| ) | |
| # Launch the Gradio app | |
| interface.launch(debug=True) | |