app.py

import torch
from transformers import MBartForConditionalGeneration, AutoTokenizer, AutoModelForCausalLM, pipeline
import gradio as gr
import requests
import io
from PIL import Image
import os

# Set up the Hugging Face API key from environment variables
hf_api_key = os.getenv("new_hf_token")
if not hf_api_key:
    raise ValueError("Hugging Face API key not found! Please set the 'HF_API_KEY' environment variable.")
headers = {"Authorization": f"Bearer {hf_api_key}"}

# Define the text-to-image model URLs
model_urls = {
    "stable_diffusion_v1_4": "https://api-inference.huggingface.co/models/CompVis/stable-diffusion-v1-4",
    "stable_diffusion_v1_5": "https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5",
}
API_URL = model_urls["stable_diffusion_v1_4"]

# Define the translation model for multilingual text inputs
translation_model_name = "facebook/mbart-large-50-many-to-one-mmt"
tokenizer = AutoTokenizer.from_pretrained(translation_model_name)
translation_model = MBartForConditionalGeneration.from_pretrained(translation_model_name)

# Load a text generation model from Hugging Face
text_generation_model_name = "EleutherAI/gpt-neo-2.7B"
text_tokenizer = AutoTokenizer.from_pretrained(text_generation_model_name)
text_model = AutoModelForCausalLM.from_pretrained(text_generation_model_name, device_map="auto", torch_dtype=torch.float32)

# Create a pipeline for text generation
text_generator = pipeline("text-generation", model=text_model, tokenizer=text_tokenizer)

# Function to generate an image using Hugging Face's text-to-image model
def generate_image_from_text(translated_text):
    payload = {"inputs": translated_text, "options": {"wait_for_model": True}}
    response = requests.post(API_URL, headers=headers, json=payload)
    if response.status_code == 200:
        image_data = response.content
        image = Image.open(io.BytesIO(image_data))
        return image
    else:
        # If the model is loading, check the estimated wait time
        if response.status_code == 503:
            error_message = response.json()
            estimated_time = error_message.get("estimated_time", "Unknown")
            return f"Model is currently loading. Estimated wait time: {estimated_time} seconds. Try again later."
        else:
            return f"Failed to generate image. Error: {response.status_code}, Message: {response.text}"

# Function to translate text using the MBart model
def translate_text(input_text, src_lang="en"):
    # Tokenize and translate
    tokenizer.src_lang = src_lang
    encoded_input = tokenizer(input_text, return_tensors="pt")
    translated_tokens = translation_model.generate(**encoded_input)
    translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
    return translated_text

# Function to generate text using the GPT-Neo model
def generate_text(prompt, max_length=50):
    generated_texts = text_generator(prompt, max_length=max_length, num_return_sequences=1)
    return generated_texts[0]["generated_text"]

# Define the Gradio Interface
def app_interface(input_text, src_language="en"):
    translated_text = translate_text(input_text, src_lang=src_language)
    generated_image = generate_image_from_text(translated_text)
    generated_text = generate_text(translated_text)
    return generated_text, generated_image

# Launch the Gradio App using the new Gradio components
with gr.Blocks() as demo:
    gr.Markdown("# Multilingual Text-to-Image & Text Generation")

    # Define Gradio components
    input_text = gr.Textbox(lines=2, placeholder="Enter text here...")
    src_language = gr.Dropdown(["en", "fr", "de", "es"], value="en", label="Source Language")
    
    # Display outputs for text and image generation
    generated_text_output = gr.Textbox(label="Generated Text")
    generated_image_output = gr.Image(label="Generated Image")

    # Button to trigger the processing
    generate_button = gr.Button("Generate")

    # Link the button to the function call
    generate_button.click(fn=app_interface, inputs=[input_text, src_language], outputs=[generated_text_output, generated_image_output])

# Run the app
demo.launch()