Upload app (3).py

app (3).py

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+import gradio as gr
+import os
+import sys
+import random
+import string
+import time
+from queue import Queue
+from threading import Thread
+
+
+proc1 = gr.Interface.load("models/segmind/SSD-1B")
+
+def restart_script_periodically():
+    while True:
+        random_time = random.randint(540, 600)
+        time.sleep(random_time)
+        os.execl(sys.executable, sys.executable, *sys.argv)
+
+restart_thread = Thread(target=restart_script_periodically, daemon=True)
+restart_thread.start()
+
+queue = Queue()
+queue_threshold = 100
+
+def add_random_noise(prompt, noise_level=0.00):
+    if noise_level == 0:
+        noise_level = 0.00
+    percentage_noise = noise_level * 5
+    num_noise_chars = int(len(prompt) * (percentage_noise / 100))
+    noise_indices = random.sample(range(len(prompt)), num_noise_chars)
+    prompt_list = list(prompt)
+    noise_chars = list(string.ascii_letters + string.punctuation + ' ' + string.digits)
+    noise_chars.extend(['😍', '💩', '😂', '🤔', '😊', '🤗', '😭', '🙄', '😷', '🤯', '🤫', '🥴', '😴', '🤩', '🥳', '😔', '😩', '🤪', '😇', '🤢', '😈', '👹', '👻', '🤖', '👽', '💀', '🎃', '🎅', '🎄', '🎁', '🎂', '🎉', '🎈', '🎊', '🎮', '❤️', '💔', '💕', '💖', '💗', '🐶', '🐱', '🐭', '🐹', '🦊', '🐻', '🐨', '🐯', '🦁', '🐘', '🔥', '🌧️', '🌞', '🌈', '💥', '🌴', '🌊', '🌺', '🌻', '🌸', '🎨', '🌅', '🌌', '☁️', '⛈️', '❄️', '☀️', '🌤️', '⛅️', '🌥️', '🌦️', '🌧️', '🌩️', '🌨️', '🌫️', '☔️', '🌬️', '💨', '🌪️', '🌈'])
+    for index in noise_indices:
+        prompt_list[index] = random.choice(noise_chars)
+    return "".join(prompt_list)
+
+# Existing code...
+
+import uuid  # Import the UUID library
+
+# Existing code...
+
+# Existing code...
+
+request_counter = 0  # Global counter to track requests
+# Defining the image styles and default style
+IMAGE_STYLES = [
+    "No style",
+    "Cinematic",
+    "Photographic",
+    "Anime",
+    "Manga",
+    "Digital Art",
+    "Pixel art",
+    "Fantasy art",
+    "Neonpunk",
+    "3D Model"
+]
+
+DEFAULT_IMAGE_STYLE = "Cinematic"
+
+
+def send_it1(inputs, noise_level, style, proc=proc1):
+    global request_counter
+    request_counter += 1
+    timestamp = f"{time.time()}_{request_counter}"
+    advanced_options = f"\nStyle: {style}\n"
+    prompt_with_noise = add_random_noise(inputs, noise_level) + f" - {timestamp}"+ advanced_options
+    while queue.qsize() >= queue_threshold:
+        time.sleep(2)
+    queue.put(prompt_with_noise)
+    output = proc(prompt_with_noise)
+    return output
+
+
+# ... (existing code)
+
+# ... (existing code)
+
+with gr.Blocks(css=".gradio-container {background-color: #fdf7e6;} footer{display:none !important;}",) as demo:
+
+    
+
+    with gr.Column(elem_id="col-container"):
+        
+        with gr.Row(variant="compact"):
+            prompt = gr.Textbox(
+                lines=8,
+                label="Enter your prompt",
+                show_label=False,
+                max_lines=10,
+                placeholder="Full Prompt",
+            ).style(
+                container=False,
+                textarea={'height': '400px'}
+            )
+
+            run = gr.Button("Generate Images").style(full_width=False)
+        with gr.Accordion("Advanced options", open=False):
+         with gr.Row():
+            style_selection = gr.Radio(
+                show_label=True, container=True, interactive=True,
+                choices=IMAGE_STYLES,
+                value=DEFAULT_IMAGE_STYLE,
+                label='Image Style'
+            )   
+
+        with gr.Row():
+            with gr.Row():
+                noise_level = gr.Slider(minimum=0.0, maximum=3, step=0.1, label="Noise Level")
+
+        with gr.Row():
+            with gr.Row():
+                output1 = gr.Image(label="", show_label=False, show_share_button=False)
+                output2 = gr.Image(label="", show_label=False, show_share_button=False)
+
+        # Adjust the click event to include the style_selection Radio component
+        run.click(send_it1, inputs=[prompt, noise_level, style_selection], outputs=[output1])
+        run.click(send_it1, inputs=[prompt, noise_level, style_selection], outputs=[output2])
+        
+    demo.launch(enable_queue=True, inline=True)