Update app.py

app.py

@@ -1,4 +1,4 @@
-# app.py
+# app.py (Final Corrected Version)
 
 import gradio as gr
 import torch
@@ -6,89 +6,64 @@ from diffusers import AutoPipelineForInpainting
 from PIL import Image
 import time
 
-# --- Model Loading (CPU Version) ---
-print("Loading model on CPU... This may take several minutes.")
-# This is the corrected line that fixes the error.
-pipe = AutoPipelineForInpainting.from_pretrained(
-    "stabilityai/stable-diffusion-2-inpainting",
-    torch_dtype=torch.float32, # Use float32 for broad CPU compatibility
-    safety_checker=None        # Explicitly disable the safety checker to prevent loading errors
-)
-print("Model loaded successfully.")
+# --- Model Loading ---
+print("Loading model for low-RAM CPU environment...")
+model_id = "runwayml/stable-diffusion-inpainting"
+try:
+    pipe = AutoPipelineForInpainting.from_pretrained(model_id, torch_dtype=torch.float32)
+    pipe.enable_model_cpu_offload()
+    print("Model loaded successfully with CPU offloading enabled.")
+except Exception as e:
+    print(f"An error occurred during model loading: {e}")
+    raise e
 
+# --- Prompts ---
+DEFAULT_PROMPT = "photorealistic, 4k, ultra high quality, sharp focus, masterpiece, high detail"
+DEFAULT_NEGATIVE_PROMPT = "blurry, pixelated, distorted, deformed, ugly, disfigured, cartoon, watermark"
 
-# --- Default "Magic" Prompts ---
-DEFAULT_PROMPT = "photorealistic, 4k, ultra high quality, sharp focus, masterpiece, high detail, professional photo"
-DEFAULT_NEGATIVE_PROMPT = "blurry, pixelated, distorted, deformed, ugly, disfigured, cartoon, anime, low quality, watermark, text"
-
-# --- The Inpainting Function ---
-def inpaint_image(input_dict, user_prompt, guidance_scale, num_steps, progress=gr.Progress()):
-    image = input_dict["image"].convert("RGB")
-    mask_image = input_dict["mask"].convert("RGB")
-
+# --- Inpainting Function (Correct Signature) ---
+def inpaint_image(image_and_mask, user_prompt, guidance_scale, num_steps, progress=gr.Progress(track_tqdm=True)):
+    # The input is now a dictionary with 'image' and 'mask' keys
+    image = image_and_mask["image"].convert("RGB")
+    mask = image_and_mask["mask"].convert("RGB")
+    
+    if image is None or mask is None:
+        raise gr.Error("Please upload an image and draw a mask on it first!")
+    
     if user_prompt and user_prompt.strip():
         prompt = user_prompt
         negative_prompt = DEFAULT_NEGATIVE_PROMPT 
-        print(f"Using custom prompt: '{prompt}'")
     else:
         prompt = DEFAULT_PROMPT
         negative_prompt = DEFAULT_NEGATIVE_PROMPT
-        print(f"User prompt is empty. Using default 'General Fix' prompt.")
     
     print(f"Starting inpainting on CPU...")
-    start_time = time.time()
-    
-    def progress_callback(step, timestep, latents):
-        progress(step / int(num_steps), desc=f"Running step {step}/{int(num_steps)}")
-
     result_image = pipe(
-        prompt=prompt,
-        image=image,
-        mask_image=mask_image,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=int(num_steps),
-        callback_steps=1,
-        callback=progress_callback,
+        prompt=prompt, image=image, mask_image=mask, negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale, num_inference_steps=int(num_steps)
     ).images[0]
     
-    end_time = time.time()
-    print(f"Inpainting finished in {end_time - start_time:.2f} seconds.")
     return result_image
 
-
-# --- Gradio User Interface ---
+# --- UI ---
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown(
-        """
-        # 🎨 AI Image Fixer
-        **How to use:**
-        1. Upload an image.
-        2. Use the brush to **paint over the area you want to fix**.
-        3. **(Optional)** For precise control, write a custom prompt describing the fix.
-        4. **(Easy Mode)** Or, just leave the prompt box empty for a general quality improvement.
-        5. Click "Fix It!"
-        """
-    )
-    
-    gr.Warning(
-        "⚠️ This Space is running on a free CPU. "
-        "Image generation will be VERY SLOW (expect 5-20 minutes). "
-        "A progress bar will show the status below the button. Please be patient!"
-    )
+    gr.Markdown("# 🎨 AI Image Fixer (Stable Version)")
+    gr.Warning("‼️ PATIENCE REQUIRED! Generation can take 15-30 minutes on free hardware.")
     
     with gr.Row():
         with gr.Column(scale=2):
+            # This component returns a dictionary when tool='brush'
             input_image = gr.Image(label="1. Upload & Mask Image", source="upload", tool="brush", type="pil")
-            prompt_textbox = gr.Textbox(label="2. Describe Your Fix (Optional)", placeholder="Leave empty for a general fix, or type e.g., 'a perfect human hand'")
+            prompt_textbox = gr.Textbox(label="2. Describe Your Fix (Optional)", placeholder="Leave empty for a general fix")
             with gr.Accordion("Advanced Settings", open=False):
                 guidance_scale = gr.Slider(minimum=0, maximum=20, value=8.0, label="Guidance Scale")
-                num_steps = gr.Slider(minimum=10, maximum=50, step=1, value=25, label="Inference Steps")
+                num_steps = gr.Slider(minimum=10, maximum=50, step=1, value=20, label="Inference Steps")
         with gr.Column(scale=1):
             output_image = gr.Image(label="Result", type="pil")
 
     submit_button = gr.Button("Fix It!", variant="primary")
     
+    # The `inputs` list is simple. The function signature must match what Gradio provides.
     submit_button.click(
         fn=inpaint_image,
         inputs=[input_image, prompt_textbox, guidance_scale, num_steps],