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from transformers import MarianMTModel, AutoModelForSeq2SeqLM, AutoTokenizer, GPTNeoForCausalLM, GPT2Tokenizer
import gradio as gr
import requests
import io
from PIL import Image
import os # Import os to access environment variables
# Load MarianMT model and tokenizer for Tamil to English translation
model_name = "Helsinki-NLP/opus-mt-mul-en"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# Load GPT-Neo model and tokenizer
gpt_neo_model_name = "EleutherAI/gpt-neo-125M"
gpt_neo_model = GPTNeoForCausalLM.from_pretrained(gpt_neo_model_name)
gpt_neo_tokenizer = GPT2Tokenizer.from_pretrained(gpt_neo_model_name)
# Retrieve the API URL and headers for Flux.1 from environment variables
API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev"
headers = {"Authorization": f"Bearer {os.environ.get('HUGGINGFACE_API_KEY')}"} # Use the environment variable
def generate_image_from_text(english_text):
payload = {"inputs": english_text}
response = requests.post(API_URL, headers=headers, json=payload)
if response.status_code == 200:
image_bytes = response.content
image = Image.open(io.BytesIO(image_bytes))
return image
else:
return None # Handle error appropriately
def translate_tamil_to_english(tamil_text):
# Tokenize input and generate translation
inputs = tokenizer(tamil_text, return_tensors="pt", padding=True)
translated_tokens = model.generate(**inputs)
translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
return translated_text
def generate_creative_text(english_text):
input_ids = gpt_neo_tokenizer.encode(english_text, return_tensors='pt')
output = gpt_neo_model.generate(input_ids, max_length=150, num_return_sequences=1)
return gpt_neo_tokenizer.decode(output[0], skip_special_tokens=True)
def process_input(tamil_text):
# Step 1: Translate Tamil to English
translated_text = translate_tamil_to_english(tamil_text)
# Step 2: Generate Image from Translated English Text
image = generate_image_from_text(translated_text)
# Step 3: Generate Creative Text
creative_text = generate_creative_text(translated_text)
# Return results (translated text, image, and creative text)
return translated_text, image, creative_text
# Create a Gradio interface with input and output components
interface = gr.Interface(
fn=process_input,
inputs=gr.Textbox(lines=2, placeholder="Enter Tamil text..."),
outputs=[gr.Textbox(label="Translated Text (English)"),
gr.Image(label="Generated Image"),
gr.Textbox(label="Creative Text")],
title="Tamil to Creative Text & Image Generator",
description="Enter Tamil text to translate, generate an image, and produce creative content."
)
# Launch the Gradio app
interface.launch(debug=True)
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