Update app.py

app.py

@@ -4,9 +4,10 @@ import tempfile, os
 
 def detect_and_palette(image):
     # 1) Save upload to a temp file
-    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
-        image.save(tmp, format="PNG")
-        tmp_path = tmp.name
+    tmp = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
+    image.save(tmp.name, format="PNG")
+    tmp_path = tmp.name
+    tmp.close()
 
     try:
         # 2) Run stone with return_report_image=True to get annotated face image
@@ -15,90 +16,46 @@ def detect_and_palette(image):
             image_type="auto",
             return_report_image=True
         )
+    except Exception as e:
+        # Clean up and return error message
+        if os.path.exists(tmp_path):
+            os.remove(tmp_path)
+        return None, f"<p style='color:red;'>Error processing image: {e}</p>"
     finally:
-        os.remove(tmp_path)
+        # Always remove the temp file
+        if os.path.exists(tmp_path):
+            os.remove(tmp_path)
 
+    # 3) Gather faces and report_images
     faces = result.get("faces", [])
-    if not faces:
-        return None, "<p>No face detected.</p>"
-
-    # 3) Grab the first annotated image (PIL.Image)
-    report_images = result.get("report_images", [])
-    annotated_img = report_images[0]
-
-    # 4) Extract dominant skin-tone hex codes
-    colors = [c["color"] for c in faces[0]["dominant_colors"]]
-    swatches_html = "".join(
-        f'<div style="background:{hexcode}; width:50px; height:50px; '
-        'display:inline-block; margin:2px; border-radius:4px;"></div>'
-        for hexcode in colors
-    )
-    swatches_html = f"<div style='display:flex; flex-wrap:wrap;'>{swatches_html}</div>"
-
-    # Return the annotated face image + HTML swatches
-    return annotated_img, swatches_html
-
-# Build the Gradio interface
-with gr.Blocks() as demo:
-    gr.Markdown("## 🤳 Face‑Based Skin Tone Palette")
-    gr.Markdown("Upload a clear portrait to detect your face and extract your skin‑tone palette.")
-
-    with gr.Row():
-        inp = gr.Image(type="pil", label="Your Photo")
-        btn = gr.Button("Analyze")
-
-    img_out = gr.Image(type="pil", label="Detected Face Region")
-    swatches_out = gr.HTML(label="Skin‑Tone Palette")
-
-    btn.click(
-        fn=detect_and_palette,
-        inputs=inp,
-        outputs=[img_out, swatches_out]
-    )
-
-if __name__ == "__main__":
-    demo.launch()
-import gradio as gr
-import stone
-import tempfile, os
-
-def detect_and_palette(image):
-    # 1) Save upload to a temp file
-    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
-        image.save(tmp, format="PNG")
-        tmp_path = tmp.name
+    report_images = result.get("report_images", {})
 
-    try:
-        # 2) Run stone with return_report_image=True to get annotated face image
-        result = stone.process(
-            tmp_path,
-            image_type="auto",
-            return_report_image=True
-        )
-    finally:
-        os.remove(tmp_path)
-
-    faces = result.get("faces", [])
-    if not faces:
+    if not faces or not report_images:
         return None, "<p>No face detected.</p>"
 
-    # 3) Grab the first annotated image (PIL.Image)
-    report_images = result.get("report_images", [])
-    annotated_img = report_images[0]
+    # 4) Pick the first annotated image
+    if isinstance(report_images, dict):
+        annotated_img = next(iter(report_images.values()))
+    elif isinstance(report_images, list):
+        annotated_img = report_images[0]
+    else:
+        annotated_img = report_images
+
+    # 5) Extract dominant color hex codes
+    dominant = faces[0].get("dominant_colors", [])
+    colors = [c.get("color") for c in dominant if c.get("color")]
 
-    # 4) Extract dominant skin-tone hex codes
-    colors = [c["color"] for c in faces[0]["dominant_colors"]]
-    swatches_html = "".join(
+    # 6) Build HTML swatches
+    swatches = "".join(
         f'<div style="background:{hexcode}; width:50px; height:50px; '
         'display:inline-block; margin:2px; border-radius:4px;"></div>'
         for hexcode in colors
     )
-    swatches_html = f"<div style='display:flex; flex-wrap:wrap;'>{swatches_html}</div>"
+    swatches_html = f"<div style='display:flex; flex-wrap:wrap; gap:4px;'>{swatches}</div>"
 
-    # Return the annotated face image + HTML swatches
     return annotated_img, swatches_html
 
-# Build the Gradio interface
+# 7) Build Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("## 🤳 Face‑Based Skin Tone Palette")
     gr.Markdown("Upload a clear portrait to detect your face and extract your skin‑tone palette.")