Update app.py

app.py

@@ -15,43 +15,50 @@ def load_model_cached():
 model = load_model_cached()
 face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
 
-def detect_and_predict(image_input):
+# Analyze all detected faces
+def detect_faces_and_predict_all(image_input):
     image_np = np.array(image_input.convert("RGB"))
     gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
     faces = face_cascade.detectMultiScale(gray, 1.1, 4)
 
+    results = []
     if len(faces) == 0:
-        return image_input, None, "No face detected"
-
-    x, y, w, h = faces[0]
-    face_roi = image_np[y:y+h, x:x+w]
-    face_pil = Image.fromarray(face_roi).resize((200, 200))
-    img_array = img_to_array(face_pil) / 255.0
-    img_array = np.expand_dims(img_array, axis=0)
-
-    prediction = model.predict(img_array)[0][0]
-    confidence = (1 - prediction) if prediction < 0.5 else prediction
-    label_text = "Mask Detected" if prediction < 0.5 else "No Mask Detected"
-
-    color = (0, 255, 0) if prediction < 0.5 else (0, 0, 255)
-    cv2.rectangle(image_np, (x, y), (x + w, y + h), color, 2)
-    cv2.putText(image_np, f"{label_text} ({confidence*100:.2f}%)", (x, y - 10),
-                cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
-
-    return Image.fromarray(image_np), confidence, label_text
-
-# Init session states
+        return image_input, results, "No face detected"
+
+    for (x, y, w, h) in faces:
+        face_roi = image_np[y:y + h, x:x + w]
+        face_pil = Image.fromarray(face_roi).resize((200, 200))
+        img_array = img_to_array(face_pil) / 255.0
+        img_array = np.expand_dims(img_array, axis=0)
+
+        prediction = model.predict(img_array)[0][0]
+        confidence = (1 - prediction) if prediction < 0.5 else prediction
+        label_text = "Mask Detected" if prediction < 0.5 else "No Mask Detected"
+        color = (0, 255, 0) if prediction < 0.5 else (255, 0, 0)
+
+        # Draw on image
+        cv2.rectangle(image_np, (x, y), (x + w, y + h), color, 2)
+        cv2.putText(image_np, f"{label_text} ({confidence*100:.2f}%)", (x, y - 10),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
+
+        results.append({
+            "label": label_text,
+            "confidence": confidence,
+            "position": (x, y, w, h),
+            "color": color
+        })
+
+    return Image.fromarray(image_np), results, f"{len(results)} face(s) detected"
+
+# Session states
+if "mode" not in st.session_state:
+    st.session_state["mode"] = "waiting"
 if "captured_image" not in st.session_state:
     st.session_state["captured_image"] = None
-if "show_camera" not in st.session_state:
-    st.session_state["show_camera"] = True
-
-st.title("😷 Face Mask Detection")
 
-# Input source selection
+st.markdown("## 😁 Face Mask Detection")
 input_method = st.selectbox("Choose Input Method", ["Upload Image", "Camera Capture"])
 
-# Upload Image
 if input_method == "Upload Image":
     uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
     if uploaded_file:
@@ -59,53 +66,48 @@ if input_method == "Upload Image":
         st.image(image_input, caption="Uploaded Image", use_container_width=True)
 
         with st.spinner("Analyzing..."):
-            result_img, confidence, label = detect_and_predict(image_input)
+            result_img, faces_data, label = detect_faces_and_predict_all(image_input)
 
         st.markdown("### 🧠 Detection Result")
         st.image(result_img, caption="Result Image", use_container_width=True)
-        st.metric("Confidence", f"{confidence*100:.2f}%" if confidence else "N/A")
 
-        # ✅ FIXED clean conditional
-        if label == "Mask Detected":
-            st.success(label)
-        elif label == "No Mask Detected":
-            st.error(label)
-        else:
-            st.warning(label)
+        for i, face in enumerate(faces_data):
+            st.markdown(f"**Face {i+1}**: `{face['label']}` ({face['confidence']*100:.2f}%)")
+            if face['label'] == "Mask Detected":
+                st.success(face['label'])
+            else:
+                st.error(face['label'])
 
-# Camera Capture
 elif input_method == "Camera Capture":
     col1, col2 = st.columns(2)
 
     with col1:
-        # Show camera input
-        if st.session_state["show_camera"]:
+        st.markdown("### 📷 Capturing Image")
+
+        if st.session_state["mode"] == "waiting":
             camera_image = st.camera_input("Take a photo")
             if camera_image:
                 st.session_state["captured_image"] = Image.open(camera_image)
-                st.session_state["show_camera"] = False  # Hide camera after capture
+                st.session_state["mode"] = "captured"
         else:
             st.image(st.session_state["captured_image"], caption="Captured Image", use_container_width=True)
             if st.button("🔁 Retake"):
-                # RESET: clear and reopen camera
                 st.session_state["captured_image"] = None
-                st.session_state["show_camera"] = True
+                st.session_state["mode"] = "waiting"
 
     with col2:
+        st.markdown("### 🧠 Detection Result")
         if st.session_state["captured_image"]:
             with st.spinner("Analyzing..."):
-                result_img, confidence, label = detect_and_predict(st.session_state["captured_image"])
+                result_img, faces_data, label = detect_faces_and_predict_all(st.session_state["captured_image"])
 
-            st.markdown("### 🧠 Detection Result")
             st.image(result_img, caption="Result Image", use_container_width=True)
-            st.metric("Confidence", f"{confidence*100:.2f}%" if confidence else "N/A")
 
-            # ✅ FIXED label output
-            if label == "Mask Detected":
-                st.success(label)
-            elif label == "No Mask Detected":
-                st.error(label)
-            else:
-                st.warning(label)
+            for i, face in enumerate(faces_data):
+                st.markdown(f"**Face {i+1}**: `{face['label']}` ({face['confidence']*100:.2f}%)")
+                if face['label'] == "Mask Detected":
+                    st.success(face['label'])
+                else:
+                    st.error(face['label'])
         else:
-            st.info("📷 Waiting for image capture...")
+            st.info("📷 Waiting for photo capture...")