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# 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)
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