Update app.py

app.py

@@ -15,7 +15,10 @@ MAX_IMAGE_SIZE = 1024
 
 def get_random_api_key():
     keys = os.getenv("KEYS", "").split(",")
-    return random.choice(keys).strip()
+    if keys and keys[0]:  # Check if KEYS is set and not empty
+        return random.choice(keys).strip()
+    else:
+        raise ValueError("API keys not found. Please set the KEYS environment variable.")
 
 def resize_img(image, max_size=1024):
     width, height = image.size
@@ -33,7 +36,11 @@ def process_image(
     width,
     height,
     model_choice,
+    negative_prompt="", # Add negative_prompt parameter
+    guidance_scale=5,    # Add guidance_scale parameter
+    num_inference_steps=25, # Add num_inference_steps parameter
 ):
+
     api_key = get_random_api_key()
 
     if randomize_seed:
@@ -42,16 +49,15 @@ def process_image(
     if image is None:
         return None, seed
 
-    # Download image from URL if provided
     if isinstance(image, str) and image.startswith("http"):
         try:
             response = requests.get(image, stream=True)
-            response.raise_for_status()  # Raise an exception for bad status codes (e.g., 404)
+            response.raise_for_status()
             image = Image.open(BytesIO(response.content))
         except requests.exceptions.RequestException as e:
             print(f"Error downloading image from URL: {e}")
             return "Ошибка загрузки изображения", seed
-    elif not isinstance(image, Image.Image):  # Convert to PIL image if needed
+    elif not isinstance(image, Image.Image):
         image = Image.fromarray(image)
 
     resized_image = resize_img(image)
@@ -61,71 +67,105 @@ def process_image(
         image_path = temp_file.name
 
     language = detect(prompt)
-    
     if language != 'en':
         prompt = GoogleTranslator(source=language, target='en').translate(prompt)
 
     try:
-
         if model_choice == "Stable Diffusion":
             client = Client("InstantX/SD35-IP-Adapter", hf_token=api_key)
+            result = client.predict(
+                image=handle_file(image_path),
+                prompt=prompt,
+                scale=scale,
+                seed=seed,
+                width=width,
+                height=height,
+                api_name="/process_image"
+            )
 
         elif model_choice == "Flux":
             client = Client("InstantX/flux-IP-adapter", hf_token=api_key)
-        
-        result = client.predict(
-            image=handle_file(image_path), # Отправляем скачанный файл
-            prompt=prompt,
-            scale=scale,
-            seed=seed,
-            randomize_seed=randomize_seed,
-            width=width,
-            height=height,
-            api_name="/process_image"
-        )
-        generated_image_url = result[0]
-        print(f"Generated image URL: {generated_image_url}")
+            result = client.predict(
+                 image=handle_file(image_path),
+                prompt=prompt,
+                scale=scale,
+                seed=seed,
+                width=width,
+                height=height,
+                api_name="/process_image"
+            )
+
+        elif model_choice == "Kolors":
+            client = Client("multimodalart/Kolors-IPAdapter", hf_token=api_key)
+            result = client.predict(
+                prompt=prompt,
+                ip_adapter_image=handle_file(image_path),
+                ip_adapter_scale=scale,
+                negative_prompt=negative_prompt,
+                seed=seed,
+                width=width,
+                height=height,
+                guidance_scale=guidance_scale,
+                num_inference_steps=num_inference_steps,
+                api_name="/infer"
+            )
+
+        generated_image = result[0]
+
     finally:
         os.remove(image_path)
 
-    return gr.update(value=generated_image_url), result[1]
-    # Ссылка на файл CSS
-css_url = "https://neurixyufi-aihub.static.hf.space/style.css"
 
-# Получение CSS по ссылке
-response = requests.get(css_url)
-css = response.text + " .gradio-container{max-width: 700px !important} h1{text-align:center} #col-container { margin: 0 auto; max-width: 960px; }"
+    return gr.update(value=generated_image), result[1]
+
+
 
-# Create the Gradio interface
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
         gr.Markdown("# Ии Редактор")
-        
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(
-                    label="Входное изображение",
-                    type="pil"
-                )
-                scale = gr.Slider(
-                    label="Схожесть с оригиналом",
-                    minimum=0.0,
-                    maximum=1.0,
-                    step=0.1,
-                    value=0.7,
-                )
-                prompt = gr.Text(
-                    label="Описание изображения",
-                    max_lines=1,
-                    placeholder="Введите ваш запрос (Например: Сделай в аниме стиле)",
-                )
-                model_choice = gr.Radio(choices=["Stable Diffusion", "Flux"], value="Stable Diffusion", label="Модель")
-                
-                run_button = gr.Button("Изменить", variant="primary")
-            
-            with gr.Column():
-                result = gr.Image(label="Результат", show_share_button=False)
-        
+
+        input_image = gr.Image(label="Входное изображение", type="pil")
+        result = gr.Image(label="Результат", show_share_button=False)
+
+
+        with gr.Tabs():
+            with gr.TabItem("Stable Diffusion & Flux"):
+                with gr.Row():
+                    with gr.Column():
+                        prompt = gr.Text(
+                            label="Описание изображения",
+                            max_lines=1,
+                            placeholder="Введите ваш запрос (Например: Сделай в аниме стиле)",
+                        )
+                        model_choice_sf = gr.Radio(choices=["Stable Diffusion", "Flux"], value="Stable Diffusion", label="Модель")
+                        scale = gr.Slider(
+                            label="Схожесть с оригиналом (scale)",
+                            minimum=0.0,
+                            maximum=1.0,
+                            step=0.1,
+                            value=0.7,
+                        )
+                    
+            with gr.TabItem("Kolors"):
+                 with gr.Row():
+                    with gr.Column():
+                        prompt_kolors = gr.Text(
+                           label="Описание изображения",
+                           max_lines=1,
+                           placeholder="Введите ваш запрос (Например: Сделай в аниме стиле)",
+                        )
+                        negative_prompt = gr.Text(label="Негативное описание", max_lines=1)
+                        scale_kolors = gr.Slider(
+                            label="Схожесть с оригиналом (ip_adapter_scale)",
+                            minimum=0.0,
+                            maximum=1.0,
+                            step=0.1,
+                            value=0.5,
+                         )
+                        guidance_scale = gr.Slider(label="Guidance Scale", minimum=0, maximum=20, value=7, step=0.5)
+                        num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, value=25, step=1)
+
+
         with gr.Accordion("Дополнительные настройки", open=False):
             seed = gr.Slider(
                 label="Сид",
@@ -134,9 +174,7 @@ with gr.Blocks(css=css) as demo:
                 step=1,
                 value=42,
             )
-            
             randomize_seed = gr.Checkbox(label="Случайный сид", value=True)
-            
             with gr.Row():
                 width = gr.Slider(
                     label="Ширина",
@@ -145,15 +183,16 @@ with gr.Blocks(css=css) as demo:
                     step=32,
                     value=1024,
                 )
-                
                 height = gr.Slider(
                     label="Высота",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
                     value=1024,
-                )
-    
+                 )
+        run_button = gr.Button("Изменить", variant="primary")
+
+
     run_button.click(
         fn=process_image,
         inputs=[
@@ -164,11 +203,15 @@ with gr.Blocks(css=css) as demo:
             randomize_seed,
             width,
             height,
-            model_choice,
+            model_choice_sf, # Use model_choice_sf here
+            negative_prompt,
+            guidance_scale,
+            num_inference_steps
         ],
         outputs=[result, seed],
-        concurrency_limit=250,
     )
 
+
 if __name__ == "__main__":
     demo.queue(max_size=250).launch(show_api=False, share=False)
+