Update app.py

app.py

@@ -1,77 +1,63 @@
+# app.py
+
 import gradio as gr
 import stone
-import tempfile, os
-
-def detect_and_palette(image):
-    # 1) Save upload to a temp file
+import tempfile
+import os
+
+def get_primary_color(image):
+    """
+    Detects the most dominant skin color in the first face detected
+    and returns a single color swatch plus its hex code.
+    """
+    # Save upload to temp file
     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
-        result = stone.process(
-            tmp_path,
-            image_type="auto",
-            return_report_image=True
-        )
+        # Run stone to get dominant colors
+        result = stone.process(tmp_path, image_type="auto", return_report_image=False)
     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>"
+        os.remove(tmp_path)
+        return f"<p style='color:red;'>Error: {e}</p>"
     finally:
-        # 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", [])
-    report_images = result.get("report_images", {})
-
-    if not faces or not report_images:
-        return None, "<p>No face detected.</p>"
-
-    # 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")]
-
-    # 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
+    if not faces:
+        return "<p>No face detected.</p>"
+
+    # Grab the first dominant color of the first face
+    dominant_colors = faces[0].get("dominant_colors", [])
+    if not dominant_colors:
+        return "<p>No skin color found.</p>"
+
+    hexcode = dominant_colors[0].get("color")
+    if not hexcode:
+        return "<p>No valid color found.</p>"
+
+    # Render a single swatch and show its hex code
+    swatch_html = (
+        f"<div style='display:flex; align-items:center; gap:8px;'>"
+        f"<div style='background:{hexcode}; width:50px; height:50px; "
+        "border-radius:4px; border:1px solid #ccc;'></div>"
+        f"<span style='font-family:monospace; font-size:1.2em;'>{hexcode}</span>"
+        "</div>"
     )
-    swatches_html = f"<div style='display:flex; flex-wrap:wrap; gap:4px;'>{swatches}</div>"
-
-    return annotated_img, swatches_html
+    return swatch_html
 
-# 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.")
-
+    gr.Markdown("## 🎯 Single Face Skin Color")
+    gr.Markdown("Upload a portrait and get the single most dominant skin-tone color.")
     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 = gr.Button("Detect Color")
+    out = gr.HTML(label="Primary Skin Color")
 
-    btn.click(
-        fn=detect_and_palette,
-        inputs=inp,
-        outputs=[img_out, swatches_out]
-    )
+    btn.click(fn=get_primary_color, inputs=inp, outputs=out)
 
 if __name__ == "__main__":
     demo.launch()