Update app.py

app.py

@@ -1,154 +1,129 @@
-import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
+import tkinter as tk
+from tkinter import ttk, scrolledtext
+from PIL import Image, ImageTk
+import os
+from datetime import datetime
+import json
+import requests
+from io import BytesIO
+
+class DrawingTutorialApp:
+    def __init__(self, root):
+        self.root = root
+        self.root.title("Générateur de Tutoriel de Dessin")
+        
+        # Configuration des styles
+        self.setup_styles()
+        
+        # Variables
+        self.current_step = 0
+        self.generated_images = []
+        self.steps_description = []
+        
+        # Création de l'interface
+        self.create_interface()
+        
+    def setup_styles(self):
+        style = ttk.Style()
+        style.configure('TButton', padding=5)
+        style.configure('TFrame', padding=5)
+        
+    def create_interface(self):
+        # Frame principal
+        main_frame = ttk.Frame(self.root)
+        main_frame.pack(expand=True, fill='both', padx=10, pady=10)
+        
+        # Zone de description
+        desc_frame = ttk.LabelFrame(main_frame, text="Description du dessin")
+        desc_frame.pack(fill='x', pady=5)
+        
+        self.description_text = scrolledtext.ScrolledText(desc_frame, height=4)
+        self.description_text.pack(fill='x', padx=5, pady=5)
+        
+        # Boutons de contrôle
+        control_frame = ttk.Frame(main_frame)
+        control_frame.pack(fill='x', pady=5)
+        
+        ttk.Button(control_frame, text="Générer les étapes", 
+                   command=self.generate_steps).pack(side='left', padx=5)
+        
+        ttk.Button(control_frame, text="Étape précédente", 
+                   command=self.previous_step).pack(side='left', padx=5)
+        
+        ttk.Button(control_frame, text="Étape suivante", 
+                   command=self.next_step).pack(side='left', padx=5)
+        
+        # Zone d'affichage
+        self.display_frame = ttk.LabelFrame(main_frame, text="Aperçu de l'étape")
+        self.display_frame.pack(fill='both', expand=True, pady=5)
+        
+        self.image_label = ttk.Label(self.display_frame)
+        self.image_label.pack(padx=5, pady=5)
+        
+        # Zone de description des étapes
+        self.step_description = ttk.Label(self.display_frame, 
+                                        text="Aucune étape générée", 
+                                        wraplength=400)
+        self.step_description.pack(padx=5, pady=5)
+        
+    def generate_steps(self):
+        description = self.description_text.get("1.0", "end-1c")
+        if not description:
+            return
+            
+        # Définition des étapes standard de dessin
+        base_steps = [
+            "Esquisse de base et formes géométriques",
+            "Ajout des détails principaux",
+            "Affinement des lignes",
+            "Ajout des ombres de base",
+            "Colorisation de base",
+            "Ajout des détails de couleur",
+            "Finalisation et mise en valeur"
+        ]
+        
+        # Ici, vous intégreriez votre API de génération d'images
+        # Pour l'exemple, nous simulons la génération
+        self.generated_images = []
+        self.steps_description = []
+        
+        for step in base_steps:
+            # Simuler la génération d'image
+            # Dans une vraie implémentation, appelez votre API ici
+            blank_image = Image.new('RGB', (400, 400), 'white')
+            self.generated_images.append(blank_image)
+            self.steps_description.append(f"{step}\n{description}")
+        
+        self.current_step = 0
+        self.update_display()
+        
+    def update_display(self):
+        if not self.generated_images:
+            return
+            
+        image = self.generated_images[self.current_step]
+        photo = ImageTk.PhotoImage(image)
+        self.image_label.configure(image=photo)
+        self.image_label.image = photo
+        
+        step_text = f"Étape {self.current_step + 1}/{len(self.generated_images)}\n"
+        step_text += self.steps_description[self.current_step]
+        self.step_description.configure(text=step_text)
+        
+    def previous_step(self):
+        if self.current_step > 0:
+            self.current_step -= 1
+            self.update_display()
+            
+    def next_step(self):
+        if self.current_step < len(self.generated_images) - 1:
+            self.current_step += 1
+            self.update_display()
+
+def main():
+    root = tk.Tk()
+    app = DrawingTutorialApp(root)
+    root.mainloop()
 
 if __name__ == "__main__":
-    demo.launch()
+    main()