Update app.py

app.py

@@ -6,16 +6,17 @@ import tensorflow as tf
 # ------------------------------
 # 1) 모델 파일 경로
 # ------------------------------
-MODEL_PATH = "crack_detection.h5"
+MODEL_PATH = "model.h5"    # GitHub 모델 그대로 사용
 
 # ------------------------------
-# 2) 모델 로드
+# 2) TensorFlow 모델 로드
 # ------------------------------
 model = tf.keras.models.load_model(MODEL_PATH)
-print("🔥 Loaded TensorFlow crack classifier")
+print("🔥 Loaded Concrete Crack Classification Model")
 print("   Input shape :", model.input_shape)
 print("   Output shape:", model.output_shape)
 
+# expected input: (None, 227, 227, 3)
 _, H, W, C = model.input_shape
 
 
@@ -23,84 +24,47 @@ _, H, W, C = model.input_shape
 # 3) 예측 함수
 # ------------------------------
 def predict(img: Image.Image):
-    try:
-        img = img.convert("RGB")
-        img_resized = img.resize((W, H))
-
-        arr = np.array(img_resized).astype("float32") / 255.0
-        X = np.expand_dims(arr, axis=0)  # (1, H, W, C)
-
-        raw = model.predict(X)[0]
-        probs = np.array(raw, dtype="float32").flatten()
-
-        # ------------------------------
-        # 1-output (sigmoid) 모델
-        # ------------------------------
-        if probs.shape[0] == 1:
-            p_crack = float(probs[0])
-            p_normal = 1.0 - p_crack
-
-            if p_crack >= 0.5:
-                label = "crack"
-                conf = p_crack
-            else:
-                label = "normal"
-                conf = p_normal
-
-        # ------------------------------
-        # 2-output (softmax) 모델
-        # probs = [p_normal, p_crack]
-        # ------------------------------
-        elif probs.shape[0] >= 2:
-            p_normal = float(probs[0])
-            p_crack = float(probs[1])
-
-            if p_crack >= p_normal:
-                label = "crack"
-                conf = p_crack
-            else:
-                label = "normal"
-                conf = p_normal
-
-        else:
-            idx = int(np.argmax(probs))
-            label = f"class_{idx}"
-            conf = float(probs[idx])
-
-        # ------------------------------
-        # 프론트 요구 구조
-        # ------------------------------
-        return {
-            "data": [
-                {
-                    "label": label,
-                    "confidence": float(conf),
-                }
-            ]
-        }
-
-    except Exception as e:
-        print("❌ Error:", e)
-        return {
-            "data": [
-                {
-                    "label": "error",
-                    "confidence": 0.0,
-                    "message": str(e),
-                }
-            ]
-        }
+    # 1) 이미지 전처리
+    img = img.convert("RGB")
+    img_resized = img.resize((W, H))
+    arr = np.array(img_resized).astype("float32") / 255.0
+    X = np.expand_dims(arr, axis=0)  # (1, 227, 227, 3)
+
+    # 2) 모델 추론
+    raw = model.predict(X)[0]
+    probs = np.array(raw).flatten()
+
+    # Output structure: [no_crack, crack]
+    p_normal = float(probs[0])
+    p_crack = float(probs[1])
+
+    if p_crack > p_normal:
+        label = "crack"
+        conf = p_crack
+    else:
+        label = "normal"
+        conf = p_normal
+
+    # 3) JSON 구조 (프론트 요구사항대로)
+    return {
+        "data": [
+            {
+                "label": label,
+                "confidence": float(conf)
+            }
+        ]
+    }
 
 
 # ------------------------------
-# 4) Gradio API UI
+# 4) Gradio API Interface
 # ------------------------------
 demo = gr.Interface(
     fn=predict,
-    inputs=gr.Image(type="pil", label="Input image"),
+    inputs=gr.Image(type="pil", label="Input Image"),
     outputs=gr.JSON(label="Detection Result"),
-    title="Crack Detection Classifier (Keras .h5)",
-    description="사진을 업로드하면 균열/정상 여부와 확률(%)을 반환합니다.",
+    title="Concrete Crack Classification (227×227 CNN)",
+    description="균열 여부를 판단하고 crack일 때만 확률(%)을 반환합니다.",
     flagging_mode="never",
 )