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Update app.py
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app.py
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import numpy as np
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import random
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from diffusers import DiffusionPipeline
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import torch
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if
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#
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max-width: 520px;
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}
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"""
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max_lines=1,
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placeholder="Enter your prompt",
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container=False,
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)
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run_button = gr.Button("Run", scale=0)
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result = gr.Image(label="Result", show_label=False)
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with gr.Accordion("Advanced Settings", open=False):
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negative_prompt = gr.Text(
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label="Negative prompt",
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max_lines=1,
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placeholder="Enter a negative prompt",
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visible=False,
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)
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seed = gr.Slider(
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label="Seed",
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minimum=0,
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maximum=MAX_SEED,
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step=1,
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value=0,
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)
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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with gr.Row():
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width = gr.Slider(
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label="Width",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=512,
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)
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height = gr.Slider(
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label="Height",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=512,
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)
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with gr.Row():
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guidance_scale = gr.Slider(
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label="Guidance scale",
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minimum=0.0,
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maximum=10.0,
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step=0.1,
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value=0.0,
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)
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num_inference_steps = gr.Slider(
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label="Number of inference steps",
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minimum=1,
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maximum=12,
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step=1,
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value=2,
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)
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gr.Examples(
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examples = examples,
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inputs = [prompt]
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)
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run_button.click(
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fn = infer,
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inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
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outputs = [result]
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)
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demo.queue().launch()
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## app.py:
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import torch
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import gradio as gr
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from diffusers import StableDiffusionPipeline
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import requests
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from io import BytesIO
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import os
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from PIL import Image
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def translate_text(text, target_language='en'):
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API_URL = "https://api-inference.huggingface.co/models/Helsinki-NLP/opus-mt-ar-en"
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headers = {"Authorization": f"Bearer {os.getenv('API_TOKEN')}"}
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response = requests.post(API_URL, headers=headers, json=text)
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if response.status_code == 200:
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return response.json()[0]['translation_text']
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else:
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print("Failed to translate text:", response.text)
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return text # Return the original text if translation fails
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# Function to post data to an API and return response
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def query(payload, API_URL, headers):
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response = requests.post(API_URL, headers=headers, json=payload)
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return response.content
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# Function to generate images based on prompts using the Hugging Face API
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def generate_image(prompt, model_choice, translate=False):
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if translate:
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prompt = translate_text(prompt, target_language='en') # Assuming you want to translate to English
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model_urls = {
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"Stable Diffusion v1.5": "https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5",
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"dalle-3-xl-v2": "https://api-inference.huggingface.co/models/ehristoforu/dalle-3-xl-v2",
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"midjourney-v6": "https://api-inference.huggingface.co/models/Kvikontent/midjourney-v6",
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"openjourney-v4": "https://api-inference.huggingface.co/models/prompthero/openjourney-v4",
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"LCM_Dreamshaper_v7": "https://api-inference.huggingface.co/models/SimianLuo/LCM_Dreamshaper_v7",
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}
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API_URL = model_urls[model_choice]
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headers = {"Authorization": f"Bearer {os.getenv('API_TOKEN')}"}
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payload = {"inputs": prompt}
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data = query(payload, API_URL, headers)
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try:
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# Load the image from byte data
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image = Image.open(BytesIO(data))
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# Resize the image
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image = image.resize((400, 400))
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# Convert the image object back to bytes for Gradio output
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buf = BytesIO()
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image.save(buf, format='PNG')
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buf.seek(0)
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return image
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except Exception as e:
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print("Error processing the image:", e)
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return None # Return None or an appropriate error message/image
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# Set up environment variable correctly
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API_TOKEN = os.getenv("API_TOKEN")
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# Styling with custom CSS
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css = """
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body {background-color: #f0f2f5;}
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.gradio-app {background-color: #ffffff; border-radius: 12px; box-shadow: 0 0 12px rgba(0,0,0,0.1);}
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button {color: white; background-color: #106BA3; border: none; border-radius: 5px;}
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"""
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# Define interface
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title = "نموذج توليد الصور"
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description = "اكتب وصف للصورة التي تود من النظام التوليدي انشاءها"
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iface = gr.Interface(
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fn=generate_image,
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inputs=[
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gr.components.Textbox(lines=2, placeholder="Enter the description of the image here..."),
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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'),
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],
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outputs=gr.components.Image(),
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title=title,
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description=description,
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theme="default",
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css=css
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)
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# Launch the interface
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iface.launch()
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