app.py

import openai
from transformers import MBartForConditionalGeneration, MBart50Tokenizer
import gradio as gr
import requests
import io
from PIL import Image
import os

# Set up your OpenAI API key (make sure it's stored as an environment variable)
openai.api_key = os.getenv("OPENAI_API_KEY")

# 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 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 OpenAI GPT-3 text generation function
def generate_with_gpt3(prompt, max_tokens=150, temperature=0.7):
    response = openai.Completion.create(
        engine="text-davinci-003",  # You can also use "text-davinci-002" or "curie"
        prompt=prompt,
        max_tokens=max_tokens,
        temperature=temperature,
        top_p=0.9,
        frequency_penalty=0.0,
        presence_penalty=0.0
    )
    return response.choices[0].text.strip()

# Define the translation, GPT-3 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 high-quality descriptive text using OpenAI's GPT-3
    prompt = f"Create a detailed and creative description based on the following text: {translated_text}"
    generated_text = generate_with_gpt3(prompt, max_tokens=150, temperature=0.7)

    # 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, GPT-3 Text Generation, and Image Creation",
    description="Translate Tamil text to English using Facebook's mbart-large-50 model, generate high-quality text using GPT-3, and create an image using the generated text.",
)

# Launch Gradio app without `share=True`
iface.launch()