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import requests
import io
from PIL import Image
import gradio as gr
from transformers import MarianMTModel, MarianTokenizer, AutoModelForCausalLM, AutoTokenizer
import os
# Load models and tokenizers globally to avoid reloading them for every request
model_name = "Helsinki-NLP/opus-mt-mul-en"
translation_model = MarianMTModel.from_pretrained(model_name)
translation_tokenizer = MarianTokenizer.from_pretrained(model_name)
gpt_model_name = "EleutherAI/gpt-neo-1.3B"
gpt_tokenizer = AutoTokenizer.from_pretrained(gpt_model_name)
gpt_model = AutoModelForCausalLM.from_pretrained(gpt_model_name)
def translate_text(tamil_text):
inputs = translation_tokenizer(tamil_text, return_tensors="pt")
translated_tokens = translation_model.generate(**inputs)
translation = translation_tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
return translation
def query_gpt_neo(translated_text):
prompt = f"Continue the story based on the following text: {translated_text}"
inputs = gpt_tokenizer(prompt, return_tensors="pt")
outputs = gpt_model.generate(inputs['input_ids'], max_length=50, num_return_sequences=1) # Reduced max_length
creative_text = gpt_tokenizer.decode(outputs[0], skip_special_tokens=True)
return creative_text
def query_image(payload):
huggingface_api_key = os.getenv('HUGGINGFACE_API_KEY')
if not huggingface_api_key:
return "Error: Hugging Face API key not set."
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)
if response.status_code == 200:
return response.content
else:
return f"Error: {response.status_code} - {response.text}"
def process_input(tamil_input):
try:
# Translate the input text
translated_output = translate_text(tamil_input)
# Generate creative text using GPT-Neo
creative_output = query_gpt_neo(translated_output)
# Generate an image using Hugging Face's FLUX model
image_bytes = query_image({"inputs": translated_output})
image = Image.open(io.BytesIO(image_bytes))
return translated_output, creative_output, image
except Exception as e:
return f"Error occurred: {str(e)}", "", None
# Create a Gradio interface
interface = gr.Interface(
fn=process_input,
inputs=[gr.Textbox(label="Input Tamil Text")],
outputs=[
gr.Textbox(label="Translated Text"),
gr.Textbox(label="Creative Text"),
gr.Image(label="Generated Image")
],
title="TRANSART",
description="Enter Tamil text to translate to English and generate an image based on the translated text."
)
interface.launch()
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