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from transformers import MBartForConditionalGeneration, MBart50Tokenizer, pipeline
import gradio as gr
import requests
import io
from PIL import Image
import os
import torch # For text generation models
# Load the translation model and tokenizer
model_name = "facebook/mbart-large-50-many-to-one-mmt"
tokenizer = MBart50Tokenizer.from_pretrained(model_name)
model = MBartForConditionalGeneration.from_pretrained(model_name)
# Use GPT-2 for text generation instead of restricted models
text_gen_model = "gpt2"
pipe = pipeline(
"text-generation",
model=text_gen_model,
torch_dtype=torch.bfloat16,
device_map="auto"
)
# Use the Hugging Face API key from environment variables for text-to-image model
API_URL = "https://api-inference.huggingface.co/models/ZB-Tech/Text-to-Image"
headers = {"Authorization": f"Bearer {os.getenv('full_token')}"}
# Define the translation, text generation, and image generation function
def translate_and_generate_image(tamil_text):
# Step 1: Translate Tamil text to English using mbart-large-50
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: Generate descriptive English text using GPT-2
generated_text = pipe(translated_text, max_length=50, num_return_sequences=1, truncation=True)[0]['generated_text']
# Step 3: Use the generated English text to create an image
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.content
# Generate image using the generated text
image_bytes = query({"inputs": generated_text})
image = Image.open(io.BytesIO(image_bytes))
return translated_text, generated_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.Textbox(label="Generated Descriptive Text"),
gr.Image(label="Generated Image")],
title="Tamil to English Translation, Text Generation, and Image Creation",
description="Translate Tamil text to English using Facebook's mbart-large-50 model, generate descriptive text using GPT-2, and create an image using the generated text.",
)
# Launch Gradio app without `share=True` (Hugging Face Spaces already handles sharing)
iface.launch()
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