Update app.py

app.py

@@ -1,43 +1,140 @@
-import gradio as gr
-from PIL import Image
 import torch
+import gradio as gr
 from diffusers import StableDiffusionImg2ImgPipeline
+from PIL import Image
 
+# =========================
+# Load model (CPU friendly)
+# =========================
 model_id = "runwayml/stable-diffusion-v1-5"
 
 pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
     model_id,
+    torch_dtype=torch.float32,
     safety_checker=None
 )
 
 pipe = pipe.to("cpu")
 
-def generate(image, prompt):
-    image = image.convert("RGB").resize((512, 512))
+# =========================
+# Default Prompts (PRO)
+# =========================
+DEFAULT_PROMPT = (
+    "Ultra high-end photorealistic architectural visualization, "
+    "keep the exact same building structure, proportions, and facade, "
+    "modern luxury interior furniture visible through windows, "
+    "warm interior lighting turned on in all rooms, "
+    "realistic interior details behind glass, "
+    "dusk to night realistic lighting, "
+    "high-end real estate photography, "
+    "physically accurate lighting, "
+    "realistic glass reflections, "
+    "sharp edges, clean geometry, "
+    "cinematic but realistic, "
+    "no change to architecture"
+)
+
+NEGATIVE_PROMPT = (
+    "distorted architecture, warped geometry, melted building, "
+    "curved walls, broken symmetry, bad proportions, "
+    "cartoon, illustration, sketch, anime, "
+    "blurry, low quality, noisy, "
+    "overexposed windows, glowing mess, "
+    "fantasy lighting, unreal interior"
+)
+
+# =========================
+# Image Generation Function
+# =========================
+def enhance_image(
+    input_image,
+    prompt,
+    strength,
+    guidance_scale,
+    steps
+):
+    if input_image is None:
+        return None
+
+    image = input_image.convert("RGB")
 
     result = pipe(
         prompt=prompt,
+        negative_prompt=NEGATIVE_PROMPT,
         image=image,
-        strength=0.55,
-        guidance_scale=7.5,
-        num_inference_steps=25
+        strength=strength,
+        guidance_scale=guidance_scale,
+        num_inference_steps=steps
     ).images[0]
 
     return result
 
 
-demo = gr.Interface(
-    fn=generate,
-    inputs=[
-        gr.Image(type="pil", label="Upload Image"),
-        gr.Textbox(
-            label="Prompt",
-            placeholder="مثال: realistic architectural rendering, ultra high quality, cinematic lighting"
-        )
-    ],
-    outputs=gr.Image(label="Result"),
-    title="Mahmoud AI",
-    description="High quality image-to-image enhancement (free CPU version)"
-)
+# =========================
+# Gradio UI
+# =========================
+with gr.Blocks(title="Mahmoud AI – Ultra Real Estate Enhancer") as demo:
+    gr.Markdown("""
+    # 🏗️ Mahmoud AI – Architectural Image Enhancer  
+    **Ultra-Realistic | Interior Lighting | Luxury Finish**  
+    Upload ➜ Generate ➜ Download  
+    """)
+
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(
+                label="Upload Image",
+                type="pil"
+            )
+
+            prompt = gr.Textbox(
+                label="Prompt",
+                value=DEFAULT_PROMPT,
+                lines=6
+            )
+
+            strength = gr.Slider(
+                label="Strength (Preserve Architecture)",
+                minimum=0.20,
+                maximum=0.40,
+                value=0.32,
+                step=0.01
+            )
+
+            guidance_scale = gr.Slider(
+                label="Guidance Scale (Quality)",
+                minimum=6,
+                maximum=10,
+                value=8.5,
+                step=0.1
+            )
+
+            steps = gr.Slider(
+                label="Steps (Detail Level)",
+                minimum=20,
+                maximum=50,
+                value=40,
+                step=1
+            )
+
+            generate_btn = gr.Button("🚀 Generate Ultra Quality")
+
+        with gr.Column():
+            output_image = gr.Image(
+                label="Result",
+                type="pil"
+            )
+
+    generate_btn.click(
+        fn=enhance_image,
+        inputs=[
+            input_image,
+            prompt,
+            strength,
+            guidance_scale,
+            steps
+        ],
+        outputs=output_image
+    )
 
 demo.launch()