import requests import io from PIL import Image, UnidentifiedImageError import gradio as gr from transformers import MarianMTModel, MarianTokenizer import os model_name = "Helsinki-NLP/opus-mt-mul-en" model = MarianMTModel.from_pretrained(model_name) tokenizer = MarianTokenizer.from_pretrained(model_name) def translate_text(input_text, language): language_map = { "Tamil": "ta", "French": "fr", "Hindi": "hi", "German": "de" } lang_prefix = f">>{language_map[language]}<< " 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 query_gemini_api(translated_text, gemini_api_key): url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash-latest:generateContent" headers = {"Content-Type": "application/json"} prompt = f"Based on the following sentence, continue the story: {translated_text}" payload = { "contents": [{"parts": [{"text": prompt}]}] } response = requests.post(f"{url}?key={gemini_api_key}", headers=headers, json=payload) if response.status_code == 200: result = response.json() creative_text = result['candidates'][0]['content']['parts'][0]['text'] return creative_text else: return f"Error: {response.status_code} - {response.text}" def query_image(payload): huggingface_api_key = os.getenv('HUGGINGFACE_API_KEY') API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev" headers = {"Authorization": f"Bearer {huggingface_api_key}"} response = requests.post(API_URL, headers=headers, json=payload) return response.content def process_input(tamil_input, language): gemini_api_key = os.getenv('GEMINI_API_KEY') translated_output = translate_text(tamil_input, language) creative_output = query_gemini_api(translated_output, gemini_api_key) image_bytes = query_image({"inputs": translated_output}) try: image = Image.open(io.BytesIO(image_bytes)) except UnidentifiedImageError: image = None return translated_output, creative_output, image # Gradio interface setup iface = gr.Interface( fn=process_input, inputs=[ gr.Textbox(label="Input Text"), gr.Dropdown(label="Select Language", choices=["Tamil", "French", "Hindi", "German"]) ], outputs=[ gr.Textbox(label="Translated Text"), gr.Textbox(label="Creative Text"), gr.Image(label="Generated Image") ], title="TRANSART🎨 BY Sakthi", description="Enter text to translate into English and generate an image based on the translated text." ) iface.launch()