Update app.py

app.py

@@ -4,88 +4,185 @@ import numpy as np
 import random
 from huggingface_hub import AsyncInferenceClient
 from translatepy import Translator
-import requests
-import re
-import asyncio
-from PIL import Image
 from gradio_client import Client, handle_file
+from PIL import Image
 from huggingface_hub import login
-from gradio_imageslider import ImageSlider
+from themes import IndonesiaTheme  # Import custom IndonesiaTheme
 
 MAX_SEED = np.iinfo(np.int32).max
-HF_TOKEN = os.environ.get("HF_TOKEN")
-HF_TOKEN_UPSCALER = os.environ.get("HF_TOKEN_UPSCALER")
+HF_TOKEN = "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"
+HF_TOKEN_UPSCALER = "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"
 
+# Function to enable LoRA if selected
 def enable_lora(lora_add, basemodel):
+    print(f"[-] Menentukan model: LoRA {'diaktifkan' if lora_add else 'tidak diaktifkan'}, model dasar: {basemodel}")
     return basemodel if not lora_add else lora_add
 
+# Function to generate image
 async def generate_image(prompt, model, lora_word, width, height, scales, steps, seed):
     try:
         if seed == -1:
             seed = random.randint(0, MAX_SEED)
         seed = int(seed)
+
+        print(f"[-] Menerjemahkan prompt: {prompt}")
         text = str(Translator().translate(prompt, 'English')) + "," + lora_word
+        
+        print(f"[-] Generating image with prompt: {text}, model: {model}")
         client = AsyncInferenceClient()
         image = await client.text_to_image(prompt=text, height=height, width=width, guidance_scale=scales, num_inference_steps=steps, model=model)
         return image, seed
     except Exception as e:
-        print(f"Error generando imagen: {e}")
+        print(f"[-] Error generating image: {e}")
         return None, None
 
+# Function to upscale image
 def get_upscale_finegrain(prompt, img_path, upscale_factor):
     try:
+        print(f"[-] Memulai proses upscaling dengan faktor {upscale_factor} untuk gambar {img_path}")
         client = Client("finegrain/finegrain-image-enhancer", hf_token=HF_TOKEN_UPSCALER)
-        result = client.predict(input_image=handle_file(img_path), prompt=prompt, negative_prompt="", seed=42, upscale_factor=upscale_factor, controlnet_scale=0.6, controlnet_decay=1, condition_scale=6, tile_width=112, tile_height=144, denoise_strength=0.35, num_inference_steps=18, solver="DDIM", api_name="/process")
-        return result[1]
+        result = client.predict(
+            input_image=handle_file(img_path), 
+            prompt=prompt, 
+            negative_prompt="worst quality, low quality, normal quality",
+            upscale_factor=upscale_factor,
+            controlnet_scale=0.6,
+            controlnet_decay=1,
+            condition_scale=6,
+            denoise_strength=0.35, 
+            num_inference_steps=18,
+            solver="DDIM", 
+            api_name="/process"
+        )
+        print(f"[-] Proses upscaling berhasil.")
+        return result[1]  # Return upscale image path
     except Exception as e:
-        print(f"Error escalando imagen: {e}")
+        print(f"[-] Error scaling image: {e}")
         return None
-
+        
+# Main function to generate images and optionally upscale
 async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora):
+    print(f"[-] Memulai generasi gambar dengan prompt: {prompt}")
+    
     model = enable_lora(lora_model, basemodel) if process_lora else basemodel
+    print(f"[-] Menggunakan model: {model}")
+
     image, seed = await generate_image(prompt, model, "", width, height, scales, steps, seed)
-    if image is None:
-        return [None, None]
     
+    if image is None:
+        print("[-] Image generation failed.")
+        return []
+
     image_path = "temp_image.jpg"
+    print(f"[-] Menyimpan gambar sementara di: {image_path}")
     image.save(image_path, format="JPEG")
-    
+
+    upscale_image_path = None
     if process_upscale:
+        print(f"[-] Memproses upscaling dengan faktor: {upscale_factor}")
         upscale_image_path = get_upscale_finegrain(prompt, image_path, upscale_factor)
-        if upscale_image_path is not None:
-            upscale_image = Image.open(upscale_image_path)
-            upscale_image.save("upscale_image.jpg", format="JPEG")
-            return [image_path, "upscale_image.jpg"]
+        if upscale_image_path is not None and os.path.exists(upscale_image_path):
+            print(f"[-] Proses upscaling selesai. Gambar tersimpan di: {upscale_image_path}")
+            return [image_path, upscale_image_path]  # Return both images
         else:
-            print("Error: La ruta de la imagen escalada es None")
-            return [image_path, image_path]
-    else:
-        return [image_path, image_path]
+            print("[-] Upscaling gagal, jalur gambar upscale tidak ditemukan.")
+
+    return [image_path]
 
+# CSS for styling the interface
 css = """
-#col-container{ margin: 0 auto; max-width: 1024px;}
+#col-left, #col-mid, #col-right {
+    margin: 0 auto;
+    max-width: 400px;
+    padding: 10px;
+    border-radius: 15px;
+    background-color: #f9f9f9;
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+}
+#banner {
+    width: 100%;
+    text-align: center;
+    margin-bottom: 20px;
+}
+#run-button {
+    background-color: #ff4b5c;
+    color: white;
+    font-weight: bold;
+    padding: 10px;
+    border-radius: 10px;
+    cursor: pointer;
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
+}
+#footer {
+    text-align: center;
+    margin-top: 20px;
+    color: silver;
+}
 """
 
-with gr.Blocks(css=css, theme="Nymbo/Nymbo_Theme") as demo:
+# Creating Gradio interface
+with gr.Blocks(css=css, theme=IndonesiaTheme()) as WallpaperFluxMaker:
+    # Displaying the application title
+    gr.HTML('<div id="banner">✨ Flux MultiMode Generator + Upscaler ✨</div>')
+
     with gr.Column(elem_id="col-container"):
+        # Output section (replacing ImageSlider with gr.Gallery)
         with gr.Row():
-            with gr.Column(scale=3):
-                output_res = ImageSlider(label="Flux / Upscaled")
-            with gr.Column(scale=2):
-                prompt = gr.Textbox(label="Descripción de imágen")
-                basemodel_choice = gr.Dropdown(label="Modelo", choices=["black-forest-labs/FLUX.1-schnell", "black-forest-labs/FLUX.1-DEV", "enhanceaiteam/Flux-uncensored", "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", "Shakker-Labs/FLUX.1-dev-LoRA-add-details", "city96/FLUX.1-dev-gguf"], value="black-forest-labs/FLUX.1-schnell")
-                lora_model_choice = gr.Dropdown(label="LORA", choices=["Shakker-Labs/FLUX.1-dev-LoRA-add-details", "XLabs-AI/flux-RealismLora", "enhanceaiteam/Flux-uncensored"], value="XLabs-AI/flux-RealismLora")
-                process_lora = gr.Checkbox(label="Procesar LORA")
-                process_upscale = gr.Checkbox(label="Procesar Escalador")
-                upscale_factor = gr.Radio(label="Factor de Escala", choices=[2, 4, 8], value=2)
-                
-                with gr.Accordion(label="Opciones Avanzadas", open=False):
-                    width = gr.Slider(label="Ancho", minimum=512, maximum=1280, step=8, value=1280)
-                    height = gr.Slider(label="Alto", minimum=512, maximum=1280, step=8, value=768)
-                    scales = gr.Slider(label="Escalado", minimum=1, maximum=20, step=1, value=8)
-                    steps = gr.Slider(label="Pasos", minimum=1, maximum=100, step=1, value=8)
-                    seed = gr.Number(label="Semilla", value=-1)
-    
-                btn = gr.Button("Generar")
-                btn.click(fn=gen, inputs=[prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora], outputs=output_res,)
-    demo.launch()
+            output_res = gr.Gallery(label="⚡ Flux / Upscaled Image ⚡", elem_id="output-res", columns=2, height="auto")
+
+        # User input section split into two columns
+        with gr.Row():
+            # Column 1: Input prompt, LoRA, and base model
+            with gr.Column(scale=1, elem_id="col-left"):
+                prompt = gr.Textbox(
+                    label="📜 Deskripsi Gambar", 
+                    placeholder="Tuliskan prompt Anda dalam bahasa apapun, yang akan langsung diterjemahkan ke bahasa Inggris.",
+                    elem_id="textbox-prompt"
+                )
+
+                basemodel_choice = gr.Dropdown(
+                    label="🖼️ Pilih Model", 
+                    choices=[
+                        "black-forest-labs/FLUX.1-schnell", 
+                        "black-forest-labs/FLUX.1-DEV", 
+                        "enhanceaiteam/Flux-uncensored", 
+                        "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", 
+                        "Shakker-Labs/FLUX.1-dev-LoRA-add-details", 
+                        "city96/FLUX.1-dev-gguf"
+                    ], 
+                    value="black-forest-labs/FLUX.1-schnell"
+                )
+
+                lora_model_choice = gr.Dropdown(
+                    label="🎨 Pilih LoRA", 
+                    choices=[
+                        "Shakker-Labs/FLUX.1-dev-LoRA-add-details", 
+                        "XLabs-AI/flux-RealismLora", 
+                        "enhanceaiteam/Flux-uncensored"
+                    ], 
+                    value="XLabs-AI/flux-RealismLora"
+                )
+
+                process_lora = gr.Checkbox(label="🎨 Aktifkan LoRA")
+                process_upscale = gr.Checkbox(label="🔍 Aktifkan Peningkatan Resolusi")
+                upscale_factor = gr.Radio(label="🔍 Faktor Peningkatan Resolusi", choices=[2, 4, 8], value=2)
+
+            # Column 2: Advanced options (always open)
+            with gr.Column(scale=1, elem_id="col-right"):
+                with gr.Accordion(label="⚙️ Opsi Lanjutan", open=True):
+                    width = gr.Slider(label="Lebar", minimum=512, maximum=1280, step=8, value=1280)
+                    height = gr.Slider(label="Tinggi", minimum=512, maximum=1280, step=8, value=768)
+                    scales = gr.Slider(label="Skala", minimum=1, maximum=20, step=1, value=8)
+                    steps = gr.Slider(label="Langkah", minimum=1, maximum=100, step=1, value=8)
+                    seed = gr.Number(label="Seed", value=-1)
+
+        # Button to generate image
+        btn = gr.Button("🚀 Buat Gambar", elem_id="generate-btn")
+
+        # Running the `gen` function when "Generate" button is pressed
+        btn.click(fn=gen, inputs=[
+            prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora
+        ], outputs=output_res)
+
+# Launching the Gradio app
+WallpaperFluxMaker.queue(api_open=False).launch(show_api=False)