majedk01 commited on
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78b03d9
1 Parent(s): 0e014d9

Update app.py

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Files changed (1) hide show
  1. app.py +71 -133
app.py CHANGED
@@ -1,146 +1,84 @@
1
- import gradio as gr
2
- import numpy as np
3
- import random
4
- from diffusers import DiffusionPipeline
5
  import torch
 
 
 
 
 
 
6
 
7
- device = "cuda" if torch.cuda.is_available() else "cpu"
8
 
9
- if torch.cuda.is_available():
10
- torch.cuda.max_memory_allocated(device=device)
11
- pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
12
- pipe.enable_xformers_memory_efficient_attention()
13
- pipe = pipe.to(device)
14
- else:
15
- pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
16
- pipe = pipe.to(device)
17
 
18
- MAX_SEED = np.iinfo(np.int32).max
19
- MAX_IMAGE_SIZE = 1024
 
 
20
 
21
- def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
 
22
 
23
- if randomize_seed:
24
- seed = random.randint(0, MAX_SEED)
25
-
26
- generator = torch.Generator().manual_seed(seed)
27
-
28
- image = pipe(
29
- prompt = prompt,
30
- negative_prompt = negative_prompt,
31
- guidance_scale = guidance_scale,
32
- num_inference_steps = num_inference_steps,
33
- width = width,
34
- height = height,
35
- generator = generator
36
- ).images[0]
37
-
38
- return image
39
 
40
- examples = [
41
- "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
42
- "An astronaut riding a green horse",
43
- "A delicious ceviche cheesecake slice",
44
- ]
45
 
46
- css="""
47
- #col-container {
48
- margin: 0 auto;
49
- max-width: 520px;
50
- }
51
- """
52
 
53
- if torch.cuda.is_available():
54
- power_device = "GPU"
55
- else:
56
- power_device = "CPU"
57
 
58
- with gr.Blocks(css=css) as demo:
59
-
60
- with gr.Column(elem_id="col-container"):
61
- gr.Markdown(f"""
62
- # Text-to-Image Gradio Template
63
- Currently running on {power_device}.
64
- """)
65
-
66
- with gr.Row():
67
-
68
- prompt = gr.Text(
69
- label="Prompt",
70
- show_label=False,
71
- max_lines=1,
72
- placeholder="Enter your prompt",
73
- container=False,
74
- )
75
-
76
- run_button = gr.Button("Run", scale=0)
77
-
78
- result = gr.Image(label="Result", show_label=False)
79
 
80
- with gr.Accordion("Advanced Settings", open=False):
81
-
82
- negative_prompt = gr.Text(
83
- label="Negative prompt",
84
- max_lines=1,
85
- placeholder="Enter a negative prompt",
86
- visible=False,
87
- )
88
-
89
- seed = gr.Slider(
90
- label="Seed",
91
- minimum=0,
92
- maximum=MAX_SEED,
93
- step=1,
94
- value=0,
95
- )
96
-
97
- randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
98
-
99
- with gr.Row():
100
-
101
- width = gr.Slider(
102
- label="Width",
103
- minimum=256,
104
- maximum=MAX_IMAGE_SIZE,
105
- step=32,
106
- value=512,
107
- )
108
-
109
- height = gr.Slider(
110
- label="Height",
111
- minimum=256,
112
- maximum=MAX_IMAGE_SIZE,
113
- step=32,
114
- value=512,
115
- )
116
-
117
- with gr.Row():
118
-
119
- guidance_scale = gr.Slider(
120
- label="Guidance scale",
121
- minimum=0.0,
122
- maximum=10.0,
123
- step=0.1,
124
- value=0.0,
125
- )
126
-
127
- num_inference_steps = gr.Slider(
128
- label="Number of inference steps",
129
- minimum=1,
130
- maximum=12,
131
- step=1,
132
- value=2,
133
- )
134
-
135
- gr.Examples(
136
- examples = examples,
137
- inputs = [prompt]
138
- )
139
 
140
- run_button.click(
141
- fn = infer,
142
- inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
143
- outputs = [result]
144
- )
 
 
 
 
145
 
146
- demo.queue().launch()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ## app.py:
 
 
 
2
  import torch
3
+ import gradio as gr
4
+ from diffusers import StableDiffusionPipeline
5
+ import requests
6
+ from io import BytesIO
7
+ import os
8
+ from PIL import Image
9
 
 
10
 
 
 
 
 
 
 
 
 
11
 
12
+ def translate_text(text, target_language='en'):
13
+ API_URL = "https://api-inference.huggingface.co/models/Helsinki-NLP/opus-mt-ar-en"
14
+ headers = {"Authorization": f"Bearer {os.getenv('API_TOKEN')}"}
15
+ response = requests.post(API_URL, headers=headers, json=text)
16
 
17
+ if response.status_code == 200:
18
+ return response.json()[0]['translation_text']
19
 
20
+ else:
21
+ print("Failed to translate text:", response.text)
22
+ return text # Return the original text if translation fails
 
 
 
 
 
 
 
 
 
 
 
 
 
23
 
24
+ # Function to post data to an API and return response
25
+ def query(payload, API_URL, headers):
26
+ response = requests.post(API_URL, headers=headers, json=payload)
27
+ return response.content
 
28
 
29
+ # Function to generate images based on prompts using the Hugging Face API
30
+ def generate_image(prompt, model_choice, translate=False):
31
+ if translate:
32
+ prompt = translate_text(prompt, target_language='en') # Assuming you want to translate to English
33
+ model_urls = {
34
+ "Stable Diffusion v1.5": "https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5",
35
 
36
+ }
37
+ API_URL = model_urls[model_choice]
 
 
38
 
39
+ headers = {"Authorization": f"Bearer {os.getenv('API_TOKEN')}"}
40
+ payload = {"inputs": prompt}
41
+ data = query(payload, API_URL, headers)
42
+ try:
43
+ # Load the image from byte data
44
+ image = Image.open(BytesIO(data))
45
+ # Resize the image
46
+ image = image.resize((400, 400))
47
+ # Convert the image object back to bytes for Gradio output
48
+ buf = BytesIO()
49
+ image.save(buf, format='PNG')
50
+ buf.seek(0)
51
+ return image
 
 
 
 
 
 
 
 
52
 
53
+ except Exception as e:
54
+ print("Error processing the image:", e)
55
+ return None # Return None or an appropriate error message/image
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
56
 
57
+ # Set up environment variable correctly
58
+ API_TOKEN = os.getenv("API_TOKEN")
59
+
60
+ # Styling with custom CSS
61
+ css = """
62
+ body {background-color: #f0f2f5;}
63
+ .gradio-app {background-color: #ffffff; border-radius: 12px; box-shadow: 0 0 12px rgba(0,0,0,0.1);}
64
+ button {color: white; background-color: #106BA3; border: none; border-radius: 5px;}
65
+ """
66
 
67
+ # Define interface
68
+ title = "نموذج توليد الصور"
69
+ description = "اكتب وصف للصورة التي تود من النظام التوليدي انشاءها. على سبيل المثال: 'قطة ترتدي قبعة في مشهد شتوي'."
70
+ iface = gr.Interface(
71
+ fn=generate_image,
72
+ inputs=[
73
+ gr.components.Textbox(lines=2, placeholder="Enter the description of the image here..."),
74
+ gr.components.Dropdown(choices=["Stable Diffusion v1.5",], label="Choose Model", value='Stable Diffusion v1.5'),
75
+ gr.components.Checkbox(label="Translate The Text Before Generating Image", value=False)
76
+ ],
77
+ outputs=gr.components.Image(),
78
+ title=title,
79
+ description=description,
80
+ theme="default",
81
+ css=css
82
+ )
83
+ # Launch the interface
84
+ iface.launch()