app.py

## app.py:
import torch
import gradio as gr
from diffusers import StableDiffusionPipeline
import requests
from io import BytesIO
import os
from PIL import Image



def translate_text(text, target_language='en'):
    API_URL = "https://api-inference.huggingface.co/models/Helsinki-NLP/opus-mt-ar-en"
    headers = {"Authorization": f"Bearer {os.getenv('API_TOKEN')}"}
    response = requests.post(API_URL, headers=headers, json=text)

    if response.status_code == 200:
        return response.json()[0]['translation_text']

    else:
        print("Failed to translate text:", response.text)
        return text  # Return the original text if translation fails

# Function to post data to an API and return response
def query(payload, API_URL, headers):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.content

# Function to generate images based on prompts using the Hugging Face API
def generate_image(prompt, model_choice, translate=False):
    if translate:
        prompt = translate_text(prompt, target_language='en')  # Assuming you want to translate to English
    model_urls = {
        "Stable Diffusion v1.5": "https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5",
        "dalle-3-xl-v2": "https://api-inference.huggingface.co/models/ehristoforu/dalle-3-xl-v2",
        "midjourney-v6": "https://api-inference.huggingface.co/models/Kvikontent/midjourney-v6",
        "openjourney-v4": "https://api-inference.huggingface.co/models/prompthero/openjourney-v4",
        "LCM_Dreamshaper_v7": "https://api-inference.huggingface.co/models/SimianLuo/LCM_Dreamshaper_v7",
        
        

    }
    API_URL = model_urls[model_choice]

    headers = {"Authorization": f"Bearer {os.getenv('API_TOKEN')}"}
    payload = {"inputs": prompt}
    data = query(payload, API_URL, headers)
    try:
        # Load the image from byte data
        image = Image.open(BytesIO(data))
        # Resize the image
        image = image.resize((400, 400))
        # Convert the image object back to bytes for Gradio output
        buf = BytesIO()
        image.save(buf, format='PNG')
        buf.seek(0)
        return image

    except Exception as e:
        print("Error processing the image:", e)
        return None  # Return None or an appropriate error message/image

# Set up environment variable correctly
API_TOKEN = os.getenv("API_TOKEN")

# Styling with custom CSS
css = """
body {background-color: #f0f2f5;}
.gradio-app {background-color: #ffffff; border-radius: 12px; box-shadow: 0 0 12px rgba(0,0,0,0.1);}
button {color: white; background-color: #106BA3; border: none; border-radius: 5px;}
"""

# Define interface
title = "نموذج توليد الصور"
description = "اكتب وصف للصورة التي تود من النظام التوليدي انشاءها"
iface = gr.Interface(
    fn=generate_image,
    inputs=[
        gr.components.Textbox(lines=2, placeholder="Enter the description of the image here..."),
        gr.components.Dropdown(choices=["Stable Diffusion v1.5","dalle-3-xl-v2","midjourney-v6","openjourney-v4","LCM_Dreamshaper_v7"], label="Choose Model", value='Stable Diffusion v1.5'),
        
    ],
    outputs=gr.components.Image(),
    title=title,
    description=description,
    theme="default",
    css=css
)
# Launch the interface
iface.launch()