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| import os | |
| from huggingface_hub import login | |
| from transformers import MarianMTModel, MarianTokenizer, pipeline | |
| import requests | |
| import io | |
| from PIL import Image | |
| import gradio as gr | |
| # Set Hugging Face API key | |
| hf_token = os.getenv("HUGGINGFACE_API_KEY") | |
| if hf_token is None: | |
| raise ValueError("Hugging Face API key not found in environment variables.") | |
| # Login to Hugging Face | |
| login(token=hf_token) | |
| # Define models for specific languages | |
| language_models = { | |
| "fra": "Helsinki-NLP/opus-mt-fr-en", | |
| "spa": "Helsinki-NLP/opus-mt-es-en", | |
| "tam": "Helsinki-NLP/opus-mt-tam-en", | |
| "deu": "Helsinki-NLP/opus-mt-de-en", | |
| "jpn": "Helsinki-NLP/opus-mt-ja-en", | |
| "rus": "Helsinki-NLP/opus-mt-ru-en", | |
| "kor": "Helsinki-NLP/opus-mt-ko-en", | |
| "hin": "Helsinki-NLP/opus-mt-hi-en", | |
| "ita": "Helsinki-NLP/opus-mt-it-en", | |
| "por": "Helsinki-NLP/opus-mt-pt-en" | |
| # Add more language models as needed | |
| } | |
| # Function to get translator pipeline for specific language | |
| def get_translator(language_code): | |
| model_name = language_models.get(language_code) | |
| if model_name: | |
| tokenizer = MarianTokenizer.from_pretrained(model_name) | |
| model = MarianMTModel.from_pretrained(model_name) | |
| return pipeline("translation", model=model, tokenizer=tokenizer) | |
| else: | |
| raise ValueError(f"No translation model found for language code '{language_code}'.") | |
| # FLUX model API settings | |
| flux_API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev" | |
| flux_headers = {"Authorization": f"Bearer {hf_token}"} | |
| # Function for translation, creative text generation, and image creation | |
| def translate_generate_image_and_text(input_text, src_lang_code): | |
| try: | |
| # Step 1: Get translator and translate text | |
| translator = get_translator(src_lang_code) | |
| translation = translator(input_text, max_length=40) | |
| translated_text = translation[0]['translation_text'] | |
| # Step 2: Generate creative text with Mistral model | |
| mistral_API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-v0.1" | |
| mistral_headers = {"Authorization": f"Bearer {hf_token}"} | |
| mistral_response = requests.post(mistral_API_URL, headers=mistral_headers, json={"inputs": translated_text}) | |
| if mistral_response.status_code == 200: | |
| creative_text = mistral_response.json()[0]['generated_text'] | |
| else: | |
| creative_text = "Error generating creative text" | |
| # Step 3: Generate an image with FLUX model | |
| flux_response = requests.post(flux_API_URL, headers=flux_headers, json={"inputs": creative_text}) | |
| if flux_response.status_code == 200: | |
| image_bytes = flux_response.content | |
| image = Image.open(io.BytesIO(image_bytes)) | |
| else: | |
| image = None | |
| return translated_text, creative_text, image | |
| except Exception as e: | |
| return f"An error occurred: {str(e)}", None, None | |
| # Gradio interface setup | |
| interface = gr.Interface( | |
| fn=translate_generate_image_and_text, | |
| inputs=[ | |
| gr.Textbox(label="Enter text for translation"), | |
| gr.Textbox(label="Source Language Code", placeholder="e.g., 'fra' for French, 'spa' for Spanish") | |
| ], | |
| outputs=[ | |
| gr.Textbox(label="Translated Text"), | |
| gr.Textbox(label="Creative Text"), | |
| gr.Image(label="Generated Image") | |
| ], | |
| title="Multilingual Translation, Creative Content, and Image Generator", | |
| description="Select a language and translate text to English, generate creative content, and produce an image." | |
| ) | |
| # Launch the Gradio app | |
| interface.launch() | |