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| # -*- coding: utf-8 -*- | |
| """gen ai project f.ipynb | |
| Automatically generated by Colab. | |
| Original file is located at | |
| https://colab.research.google.com/drive/1iF7hdOjWNeFUtGvUYdaFsBErJGnY1h5J | |
| """ | |
| import os | |
| from transformers import MarianMTModel, MarianTokenizer, GPTNeoForCausalLM, AutoTokenizer | |
| import gradio as gr | |
| from PIL import Image, UnidentifiedImageError | |
| import requests | |
| import io | |
| # Load translation model | |
| model_name = "Helsinki-NLP/opus-mt-mul-en" | |
| tokenizer = MarianTokenizer.from_pretrained(model_name) | |
| model = MarianMTModel.from_pretrained(model_name) | |
| # Load GPT-Neo model for creative text generation | |
| gpt_neo_model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B") | |
| gpt_neo_tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B") | |
| # Define language map (including new languages) | |
| language_map = { | |
| "Tamil": "ta", | |
| "Russian": "rus", | |
| "Arabic": "ar", | |
| "Portuguese": "pt" | |
| } | |
| def translate_text(input_text, selected_languages): | |
| if not selected_languages: | |
| return "Please select at least one language." | |
| selected_language = selected_languages[0] # Pick the first selected language | |
| lang_code = language_map[selected_language] | |
| lang_prefix = f">>{lang_code}<< " | |
| text_with_lang = lang_prefix + input_text | |
| inputs = tokenizer(text_with_lang, return_tensors="pt", padding=True) | |
| translated_tokens = model.generate(**inputs) | |
| translation = tokenizer.decode(translated_tokens[0], skip_special_tokens=True) | |
| return translation | |
| def generate_image(prompt): | |
| API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev" | |
| hf_token = os.getenv("HF_TOKEN") | |
| headers = {"Authorization": f"Bearer {hf_token}"} | |
| response = requests.post(API_URL, headers=headers, json={"inputs": prompt}) | |
| if response.status_code == 200: | |
| image_bytes = response.content | |
| try: | |
| image = Image.open(io.BytesIO(image_bytes)) | |
| return image | |
| except UnidentifiedImageError: | |
| return None | |
| else: | |
| return None | |
| def generate_creative_text(translated_text): | |
| prompt = f"Create a creative text based on the following sentence: {translated_text}" | |
| inputs = gpt_neo_tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=100) | |
| output = gpt_neo_model.generate(inputs["input_ids"], max_length=100, do_sample=True, temperature=0.7) | |
| creative_text = gpt_neo_tokenizer.decode(output[0], skip_special_tokens=True) | |
| return creative_text | |
| def process_input(text_input, selected_languages): | |
| translated_output = translate_text(text_input, selected_languages) | |
| creative_text = generate_creative_text(translated_output) | |
| image = generate_image(translated_output) | |
| return translated_output, creative_text, image | |
| # Gradio interface | |
| interface = gr.Interface( | |
| fn=process_input, | |
| inputs=[gr.Textbox(label="Input Text"), gr.CheckboxGroup(choices=["Tamil", "Russian", "Arabic", "Portuguese"], label="Select Language")], | |
| outputs=[gr.Textbox(label="Translated Text"), gr.Textbox(label="Creative Text"), gr.Image(label="Generated Image")], | |
| title="Multilingual Translation, Creative Text, and Image Generation", | |
| description="Translate Tamil, Russian, Arabic, or Portuguese text to English, generate creative text, and generate an image." | |
| ) | |
| interface.launch() | |