Create app.py

app.py

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+import cv2
+import numpy as np
+from PIL import Image
+import gradio as gr
+
+def detect_faces(image):
+    image_np = np.array(image)
+    gray_image = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
+    face_cascade = cv2.CascadeClassifier(
+        cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
+    )
+
+    scale_factors_found = []  # ✅ store scale factors that detect 14 faces
+
+    # Test scale factors from 1.01 to 1.20
+    for temp in np.arange(1.01, 1.21, 0.01):
+        faces = face_cascade.detectMultiScale(
+            gray_image,
+            scaleFactor=temp,
+            minNeighbors=5,
+            minSize=(20, 20)
+        )
+        if len(faces) == 14:
+            scale_factors_found.append(round(temp, 2))
+
+    # Draw faces using the last successful scale factor (if any)
+    if scale_factors_found:
+        best_scale = scale_factors_found[-1]
+        faces = face_cascade.detectMultiScale(
+            gray_image,
+            scaleFactor=best_scale,
+            minNeighbors=5,
+            minSize=(20, 20)
+        )
+        for (x, y, w, h) in faces:
+            cv2.rectangle(image_np, (x, y), (x + w, y + h), (0, 255, 0), 2)
+        msg = (
+            f"✅ Found {len(scale_factors_found)} scale factor(s) that detect 14 faces:\n"
+            f"{scale_factors_found}\n\n"
+            f"Image displayed uses the last one: {best_scale}"
+        )
+    else:
+        msg = "❌ No scale factor between 1.01 and 1.20 detected exactly 14 faces."
+
+    return image_np, msg
+
+# --- Gradio Interface ---
+iface = gr.Interface(
+    fn=detect_faces,
+    inputs=gr.Image(type="pil"),
+    outputs=["image", "text"],
+    title="Dynamic Face Detection App",
+    description="Tests multiple scale factors (1.01–1.20) and shows all that detect exactly 14 faces."
+)
+
+iface.launch(share=True)