Update app.py

app.py

@@ -1,64 +1,208 @@
 import gradio as gr
-import threading
+import requests
+import io
+import random
 import os
-import torch
+import time
+from PIL import Image
+from deep_translator import GoogleTranslator
+import json
 
-os.environ["OMP_NUM_THREADS"] = str(os.cpu_count())
-torch.set_num_threads(os.cpu_count())
+from datetime import datetime
+from fastapi import FastAPI
 
-model1 = gr.load("models/prithivMLmods/SD3.5-Turbo-Realism-2.0-LoRA")
-model2 = gr.load("models/Purz/face-projection")
+app = FastAPI()
 
-stop_event = threading.Event()
+#----------Start of theme----------
+theme = gr.themes.Ocean(
+    primary_hue="zinc",
+    secondary_hue="slate",
+    neutral_hue="neutral",
+    font=[gr.themes.GoogleFont('Kavivanar'), gr.themes.GoogleFont('Kavivanar'), 'system-ui', 'sans-serif'],
+    font_mono=[gr.themes.GoogleFont('Source Code Pro'), gr.themes.GoogleFont('Inconsolata'), gr.themes.GoogleFont('Inconsolata'), 'monospace'],
+).set(
+    #Body Settings
+    body_background_fill='linear-gradient(10deg, *primary_200, *secondary_50)',
+    body_text_color='secondary_600',
+    body_text_color_subdued='*primary_500',
+    body_text_weight='500',
 
-def generate_images(text, selected_model):
-    stop_event.clear()
+    #Background Settings
+    background_fill_primary='*primary_100',
+    background_fill_secondary='*secondary_200',
+    
+    color_accent='*primary_300',
+
+    #Border Settings
+    border_color_accent_subdued='*primary_400',
+    border_color_primary='*primary_400',
+    
+    #Block Settings
+    block_radius='*radius_md',
+    block_background_fill='*primary_200',
+    block_border_color='*primary_500',
+    block_border_width='*panel_border_width',
+    block_info_text_color='*primary_700',
+    block_info_text_size='*text_md',
+    
+    container_radius='*radius_xl',
+    panel_background_fill='*primary_200',
+    accordion_text_color='*primary_600',
+    checkbox_border_radius='*radius_xl',
+    slider_color='*primary_500',
+    table_text_color='*primary_600',
+    input_background_fill='*primary_50',
+    input_background_fill_focus='*primary_100',
 
-    if selected_model == "Model 1 (Turbo Realism)":
-        model = model1
-    elif selected_model == "Model 2 (Face Projection)":
-        model = model2
-    else:
-        return ["Invalid model selection."] * 3
+    #Button Settings
+    button_border_width='1px',
+    button_transform_hover='scale(1.01)',
+    button_transition='all 0.1s ease-in-out',
+    button_transform_active='Scale(0.9)',
+    button_large_radius='*radius_xl',
+    button_medium_radius='*radius_xl',
+    button_small_radius='*radius_xl',
+    button_primary_border_color='*primary_500',
+    button_secondary_border_color='*primary_400',
+    button_primary_background_fill_hover='linear-gradient(90deg, *primary_400, *secondary_200, *primary_400)',
+    button_primary_background_fill='linear-gradient(90deg,*secondary_300 , *primary_500, *secondary_300)',
+    button_primary_text_color='*primary_100',
+    button_primary_text_color_hover='*primary_700',
+    button_cancel_background_fill='*primary_500',
+    button_cancel_background_fill_hover='*primary_400'
+)
+#----------End of theme----------
 
-    results = []
-    for i in range(3):
-        if stop_event.is_set():
-            return ["Image generation stopped by user."] * 3
+# Project by Nymbo
 
-        modified_text = f"{text} variation {i+1}"
-        result = model(modified_text)
-        results.append(result)
+API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-3.5-large"
+API_TOKEN = os.getenv("HF_READ_TOKEN")
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
+timeout = 100
 
-    return results
+# Function to clear input and output
+def clear():
+    return None 
 
-def stop_generation():
-    """Stops the ongoing image generation by setting the stop_event flag."""
-    stop_event.set()
-    return ["Generation stopped."] * 3
+# Function to query the API and return the generated image
+def query(prompt, is_negative=False, steps=35, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=896, height=1152):
+    if prompt == "" or prompt is None:
+        return None
 
-with gr.Blocks() as interface:#...
-    gr.Markdown(
-        "### ⚠ Sorry for the inconvenience. The Space is currently running on the CPU, which might affect performance. We appreciate your understanding."
-    )
+    key = random.randint(0, 999)
     
-    text_input = gr.Textbox(label="Type here your imagination:", placeholder="Type your prompt...")
-    model_selector = gr.Radio(
-        ["Model 1 (Turbo Realism)", "Model 2 (Face Projection)"],
-        label="Select Model",
-        value="Model 1 (Turbo Realism)"
-    )
+    API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")])
+    headers = {"Authorization": f"Bearer {API_TOKEN}"}
     
-    with gr.Row():
-        generate_button = gr.Button("Generate 3 Images 🎨")
-        stop_button = gr.Button("Stop Image Generation")
+    # Translate the prompt from Russian to English if necessary
+    prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
+    print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')
+
+    # Add some extra flair to the prompt
+    prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
+    print(f'\033[1mGeneration {key}:\033[0m {prompt}')
     
-    with gr.Row():
-        output1 = gr.Image(label="Generated Image 1")
-        output2 = gr.Image(label="Generated Image 2")
-        output3 = gr.Image(label="Generated Image 3")
+    # Prepare the payload for the API call, including width and height
+    payload = {
+        "inputs": prompt,
+        "is_negative": is_negative,
+        "steps": steps,
+        "cfg_scale": cfg_scale,
+        "seed": seed if seed != -1 else random.randint(1, 1000000000),
+        "strength": strength,
+        "parameters": {
+            "width": width,  # Pass the width to the API
+            "height": height  # Pass the height to the API
+        }
+    }
+
+    # Send the request to the API and handle the response
+    response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
+    if response.status_code != 200:
+        print(f"Error: Failed to get image. Response status: {response.status_code}")
+        print(f"Response content: {response.text}")
+        if response.status_code == 503:
+            raise gr.Error(f"{response.status_code} : The model is being loaded")
+        raise gr.Error(f"{response.status_code}")
     
-    generate_button.click(generate_images, inputs=[text_input, model_selector], outputs=[output1, output2, output3])
-    stop_button.click(stop_generation, inputs=[], outputs=[output1, output2, output3])
+    try:
+        # Convert the response content into an image
+        image_bytes = response.content
+        image = Image.open(io.BytesIO(image_bytes))
+        print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
+        return image
+    except Exception as e:
+        print(f"Error when trying to open the image: {e}")
+        return None
+
+examples = [
+    "a beautiful woman with blonde hair and blue eyes",
+    "a beautiful woman with brown hair and grey eyes",
+    "a beautiful woman with black hair and brown eyes",
+]        
+
+# CSS to style the app
+css = """
+#app-container {
+    max-width: 930px;
+    margin-left: auto;
+    margin-right: auto;
+    background-image: url("https://drive.google.com/file/d/1Kz2pi93EfsEHw90fil6XJBoSq9f-BlkJ"); repeat 0 0;}')
+}
+".gradio-container {background: url('file/abstract.png')"
+   
+"""
+
+# Build the Gradio UI with Blocks
+with gr.Blocks(theme=theme, css=css) as app:
+    # Add a title to the app
+    gr.HTML("<center><h1>🎨 Stable Diffusion 3.5 🇬🇧</h1></center>")
+    
+    # Container for all the UI elements
+    with gr.Column(elem_id="app-container"):
+        # Add a text input for the main prompt
+        with gr.Row():
+            with gr.Column(elem_id="prompt-container"):
+                with gr.Row():
+                    text_prompt = gr.Textbox(label="Image Prompt", placeholder="Enter a prompt here", lines=2, show_copy_button = True, elem_id="prompt-text-input")
+                
+                # Accordion for advanced settings
+                with gr.Row():
+                    with gr.Accordion("Advanced Settings", open=False):
+                        negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What should not be in the image", value="((visible hand:1.3), (ugly:1.3), (duplicate:1.2), (morbid:1.1), (mutilated:1.1), out of frame, bad face, extra fingers, mutated hands, (poorly drawn hands:1.1), (poorly drawn face:1.3), (mutation:1.3), (deformed:1.3), blurry, (bad anatomy:1.1), (bad proportions:1.2), (extra limbs:1.1), cloned face, (disfigured:1.2), gross proportions, malformed limbs, (missing arms:1.1), (missing legs:1.1), (extra arms:1.2), (extra legs:1.2), fused fingers, too many fingers, (long neck:1.2), sketched by bad-artist, (bad-image-v2-39000:1.3)", lines=5, elem_id="negative-prompt-text-input")
+                        with gr.Row():
+                            width = gr.Slider(label="ImageWidth", value=896, minimum=64, maximum=1216, step=32)
+                            height = gr.Slider(label="Image Height", value=1152, minimum=64, maximum=1216, step=32)
+                        steps = gr.Slider(label="Sampling steps", value=50, minimum=1, maximum=100, step=1)
+                        cfg = gr.Slider(label="CFG Scale", value=3.5, minimum=1, maximum=20, step=1)
+                        strength = gr.Slider(label="PromptStrength", value=100, minimum=0, maximum=100, step=1)
+                        seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1) # Setting the seed to -1 will make it random
+                        method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "DEIS", "LMS", "DPM Adaptive", "DPM++ 2M", "DPM2 Ancestral", "DPM++ S", "DPM++ SDE", "DDPM", "DPM Fast", "dpmpp_2s_ancestral", "Euler", "Euler CFG PP", "Euler a", "Euler Ancestral", "Euler+beta", "Heun", "Heun PP2", "DDIM", "PLMS", "UniPC", "UniPC BH2"])
+
+        # Add a button to trigger the image generation
+        with gr.Row():
+            text_button = gr.Button("Generate Image", variant='primary', elem_id="gen-button")
+            clr_button =gr.Button("Clear Prompt",variant="primary", elem_id="clear_button")
+            clr_button.click(lambda: gr.Textbox(value=""), None, text_prompt)
+            
+        # Image output area to display the generated image
+        with gr.Row():
+            image_output1 = gr.Image(type="pil", label="Image Output 1", format="png", elem_id="gallery")
+            image_output2 = gr.Image(type="pil", label="Image Output 2", format="png", elem_id="gallery")			
+            
+        with gr.Row():
+            clear_btn = gr.Button(value="Clear Image", variant="primary", elem_id="clear_button")
+            clear_btn.click(clear, inputs=[], outputs=[image_output])
+            
+        gr.Examples(
+            examples = examples,    
+            inputs = [text_prompt],    
+        )    
+            
+        # Bind the button to the query function with the added width and height inputs
+        text_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height], outputs=[image_output1, image_output2])
 
-interface.launch()
+        if __name__ == "__main__":
+            
+            # Launch the Gradio app
+            app.launch(show_api=False, share=False)