Update app.py

app.py

@@ -65,9 +65,9 @@ def compute_discount(vehicle_type):
 def classify_plate_color(plate_img):
     try:
         img = np.array(plate_img)
+        img = cv2.GaussianBlur(img, (5,5), 0)
         hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
 
-        # Color masks
         green_mask = cv2.inRange(hsv, (35, 40, 40), (85, 255, 255))
         yellow_mask = cv2.inRange(hsv, (15, 50, 50), (35, 255, 255))
         white_mask = cv2.inRange(hsv, (0, 0, 200), (180, 40, 255))
@@ -85,6 +85,9 @@ def classify_plate_color(plate_img):
         else:
             return "Unknown"
 
+    except Exception as e:
+        return "Unknown"
+
 def read_plate(plate_img):
     try:
         gray = cv2.cvtColor(np.array(plate_img), cv2.COLOR_RGB2GRAY)
@@ -166,15 +169,24 @@ def visualize(img, output, id2label, threshold):
 
 def get_dashboard():
     df = pd.read_sql("SELECT * FROM vehicles", conn)
-    fig, ax = plt.subplots()
+    fig, ax = plt.subplots(figsize=(8,5))
 
     if df.empty:
-        ax.text(0.5,0.5,"No data yet",ha="center")
+        ax.text(0.5, 0.5, "No vehicle data yet",
+                ha="center", va="center", fontsize=12)
         ax.axis("off")
         return fig
 
-    df["type"].value_counts().plot(kind="bar", ax=ax)
-    ax.set_title("Vehicle Types")
+    counts = df["type"].value_counts()
+
+    counts.plot(kind="bar", ax=ax)
+
+    ax.set_title("Vehicle Classification Distribution", fontsize=12)
+    ax.set_xlabel("Vehicle Type")
+    ax.set_ylabel("Number of Vehicles")
+    ax.tick_params(axis='x', rotation=30)
+
+    plt.tight_layout()
     return fig
 
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