# Corrected app.py file without `!pip` commands from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer from transformers import MBartForConditionalGeneration, MBart50Tokenizer import gradio as gr import requests import io from PIL import Image import os # Import os to access environment variables # Load the models and tokenizers model_name = "facebook/mbart-large-50-many-to-one-mmt" tokenizer = MBart50Tokenizer.from_pretrained(model_name) model = MBartForConditionalGeneration.from_pretrained(model_name) # Use the Hugging Face API key from environment variables API_URL = "https://api-inference.huggingface.co/models/ZB-Tech/Text-to-Image" headers = {"Authorization": f"Bearer {os.getenv('HF_API_KEY')}"} # Define the function to translate Tamil text and generate an image def translate_and_generate_image(tamil_text): # Step 1: Translate Tamil text to English tokenizer.src_lang = "ta_IN" inputs = tokenizer(tamil_text, return_tensors="pt") translated_tokens = 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] # Step 2: Use the translated English text to generate an image def query(payload): response = requests.post(API_URL, headers=headers, json=payload) return response.content image_bytes = query({"inputs": translated_text}) image = Image.open(io.BytesIO(image_bytes)) return translated_text, image # Gradio interface setup iface = gr.Interface( fn=translate_and_generate_image, inputs=gr.Textbox(lines=2, placeholder="Enter Tamil text here..."), outputs=[gr.Textbox(label="Translated English Text"), gr.Image(label="Generated Image")], title="Tamil to English Translation and Image Generation", description="Translate Tamil text to English using Facebook's mbart-large-50 model and generate an image using the translated text as the prompt.", ) # Launch Gradio app with a shareable link iface.launch(share=True)