Pinned gradio version to 5.6.0

README.md

@@ -4,7 +4,7 @@ emoji: 🌀
 colorFrom: green
 colorTo: yellow
 sdk: gradio
-sdk_version: "5.12.0"
+sdk_version: "5.6.0"
 app_file: app.py
 pinned: false
 license: mit

app.py

@@ -6,6 +6,7 @@ Estimates depth from a single image using MiDaS models.
 Optimized for free-tier CPU inference.
 """
 
+import os
 import time
 
 import gradio as gr
@@ -29,33 +30,20 @@ estimator = DepthEstimator(model_size="small")
 print("Ready!")
 
 
-def predict(
-    image: Image.Image,
-    colormap: str,
-    output_mode: str,
-    overlay_alpha: float,
-) -> tuple:
-    """
-    Run depth estimation and return results.
-
-    Returns:
-        (result_image, depth_colored, stats_string)
-    """
+def predict(image, colormap, output_mode, overlay_alpha):
+    """Run depth estimation and return result image + stats string."""
     if image is None:
         raise gr.Error("Please upload an image first.")
 
     start = time.time()
 
-    # Run depth estimation
-    image_rgb = image.convert("RGB")
+    image_rgb = Image.fromarray(image) if isinstance(image, np.ndarray) else image.convert("RGB")
     depth = estimator.predict(image_rgb)
 
     inference_time = time.time() - start
 
-    # Create colormapped depth
     depth_colored = depth_to_colormap(depth, colormap.lower())
 
-    # Create output based on mode
     if output_mode == "Side-by-Side":
         result = create_side_by_side(image_rgb, depth_colored)
     elif output_mode == "Overlay":
@@ -63,11 +51,20 @@ def predict(
     else:
         result = depth_colored
 
-    # Stats
     w, h = image_rgb.size
     stats = f"{w}×{h} · {inference_time:.2f}s inference · MiDaS Small"
 
-    return result, depth_colored, stats
+    return result, stats
+
+
+# ──────────────────────────────────────────────
+#  Build example list (only include files that exist)
+# ──────────────────────────────────────────────
+example_list = []
+for name in ["street.jpg", "landscape.jpg", "indoor.jpg"]:
+    path = os.path.join("examples", name)
+    if os.path.exists(path):
+        example_list.append([path])
 
 
 # ──────────────────────────────────────────────
@@ -104,40 +101,25 @@ with gr.Blocks(
             overlay_alpha = gr.Slider(
                 minimum=0.2, maximum=0.8, value=0.5, step=0.1,
                 label="Overlay Opacity",
-                visible=False,
             )
             run_btn = gr.Button("Estimate Depth", variant="primary")
             stats = gr.Textbox(label="Info", interactive=False)
 
         with gr.Column(scale=1):
             result_image = gr.Image(type="pil", label="Result")
-            depth_image = gr.Image(type="pil", label="Depth Map", visible=False)
 
-    # Show/hide overlay slider
-    def toggle_overlay(mode):
-        return gr.update(visible=(mode == "Overlay"))
-
-    output_mode.change(toggle_overlay, output_mode, overlay_alpha)
-
-    # Run prediction
     run_btn.click(
         fn=predict,
         inputs=[input_image, colormap, output_mode, overlay_alpha],
-        outputs=[result_image, depth_image, stats],
+        outputs=[result_image, stats],
+        api_name="predict",
     )
 
-    # Examples
-    gr.Examples(
-        examples=[
-            ["examples/street.jpg", "Inferno", "Side-by-Side", 0.5],
-            ["examples/landscape.jpg", "Magma", "Depth Map", 0.5],
-            ["examples/indoor.jpg", "Viridis", "Overlay", 0.5],
-        ],
-        inputs=[input_image, colormap, output_mode, overlay_alpha],
-        outputs=[result_image, depth_image, stats],
-        fn=predict,
-        cache_examples=False,
-    )
+    if example_list:
+        gr.Examples(
+            examples=example_list,
+            inputs=[input_image],
+        )
 
 
 if __name__ == "__main__":

requirements.txt

@@ -5,4 +5,4 @@ opencv-python-headless>=4.7.0
 Pillow>=9.4.0
 numpy>=1.23.0
 matplotlib>=3.6.0
-gradio>=5.0.0
+gradio==5.6.0