Update app.py

app.py

@@ -16,8 +16,13 @@ from transformers import YolosImageProcessor, YolosForObjectDetection
 
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
-BASE_TOLL = 100
-
+base_amt = 100
+#--- calculate discount 
+def compute_discount(vehicle_type):
+    if vehicle_type == "EV":
+        return base_amt * 0.9, "10% discount applied (EV)"
+    return toll_parking_amt, "No discount"
+#------------
 COLORS = [
     [0.000, 0.447, 0.741],
     [0.850, 0.325, 0.098],
@@ -176,23 +181,19 @@ def visualize_prediction(img, output_dict, threshold=0.5, id2label=None):
     ax = plt.gca()
     colors = COLORS * 100
 
+    result_lines = []
+
     for score, (xmin, ymin, xmax, ymax), label, color in zip(scores, boxes, labels, colors):
         if "plate" in label.lower():
             crop = img.crop((int(xmin), int(ymin), int(xmax), int(ymax)))
-            plate_type = classify_plate_color(crop)
-
-            if plate_type == "EV":
-                amount = BASE_TOLL * 0.9
-                price_text = f"EV |Congratulations !! 10% off on the amount "
-            else:
-                amount = BASE_TOLL
-                price_text = f"{plate_type} | ₹{amount:.0f}"
-
-            cursor.execute(
-                "INSERT INTO vehicles VALUES (?, ?, ?, datetime('now'))",
-                ("UNKNOWN", plate_type, amount)
+
+            plate_text = read_plate(crop)
+            vehicle_type = classify_plate_color(crop)
+            toll, discount_msg = compute_discount(vehicle_type)
+
+            result_lines.append(
+                f"License: {plate_text} | Type: {vehicle_type} | Toll: ₹{int(toll)} | {discount_msg}"
             )
-            conn.commit()
 
             ax.add_patch(
                 plt.Rectangle(
@@ -200,20 +201,29 @@ def visualize_prediction(img, output_dict, threshold=0.5, id2label=None):
                     fill=False, color=color, linewidth=4
                 )
             )
+
             ax.text(
                 xmin, ymin - 10,
-                f"{price_text} | {score:0.2f}",
+                f"{plate_text} | {vehicle_type} | ₹{int(toll)}",
                 fontsize=12,
                 bbox=dict(facecolor="yellow", alpha=0.8)
             )
 
     plt.axis("off")
-    return fig2img(plt.gcf())
+    final_img = fig2img(plt.gcf())
 
+    if result_lines:
+        result_text = "\n".join(result_lines)
+    else:
+        result_text = "No license plate detected."
+
+    return final_img, result_text
 
 # ---------------- Image Detection ----------------
 
-def detect_objects_image(url_input, image_input, webcam_input, threshold):
+def detect_objects_image(model_name, url_input, image_input, webcam_input, threshold):
+    processor, model = load_model(model_name)
+
     if url_input and is_valid_url(url_input):
         image = get_original_image(url_input)
     elif image_input is not None:
@@ -221,30 +231,32 @@ def detect_objects_image(url_input, image_input, webcam_input, threshold):
     elif webcam_input is not None:
         image = webcam_input
     else:
-        return None, None
+        return None, "No image provided."
+
+    processed_outputs = make_prediction(image, processor, model)
 
-    processed_outputs = make_prediction(image)
-    viz_img = visualize_prediction(image, processed_outputs, threshold, load_model()[1].config.id2label)
-    dashboard_fig = get_dashboard()
+    viz_img, result_text = visualize_prediction(
+        image, processed_outputs, threshold, model.config.id2label
+    )
 
-    return viz_img, dashboard_fig
+    return viz_img, result_text
 
 
 # ---------------- UI ----------------
 
-title = """<h1 id="title">License Plate Detection + Toll Billing</h1>"""
+title = """<h1 id="title">Smart Vehicle classification</h1>"""
 
 description = """
 Detect license plates using YOLOS.
 Features:
-- Image URL
-- Image Upload
-- Webcam
-- Vehicle type classification by plate color
-- EV vehicles get 10% discount
-- Billing dashboard
+- Image URL, Image Upload, Webcam,Vehicle type classification by plate color
+- EV vehicles get 10% discount on Tolls, Tax, parking
 """
-
+result_box = gr.Textbox(
+    label="Detection Result",
+    lines=5,
+    interactive=False
+)
 demo = gr.Blocks()
 
 with demo:
@@ -281,27 +293,30 @@ with demo:
                     height=200,
                     streaming=True
                 )
-                img_output_from_webcam = gr.Image(height=400)
+                img_output_from_webcam = gr.Image(height=200)
             dashboard_output_webcam = gr.Plot()
             cam_but = gr.Button('Detect')
 
     url_but.click(
-        detect_objects_image,
-        inputs=[url_input, img_input, web_input, slider_input],
-        outputs=[img_output_from_url, dashboard_output_url]
-    )
+    detect_objects_image,
+    inputs=[options, url_input, img_input, web_input, slider_input],
+    outputs=[img_output_from_url, result_box],
+    queue=True
+)
 
-    img_but.click(
-        detect_objects_image,
-        inputs=[url_input, img_input, web_input, slider_input],
-        outputs=[img_output_from_upload, dashboard_output_upload]
-    )
+img_but.click(
+    detect_objects_image,
+    inputs=[options, url_input, img_input, web_input, slider_input],
+    outputs=[img_output_from_upload, result_box],
+    queue=True
+)
 
-    cam_but.click(
-        detect_objects_image,
-        inputs=[url_input, img_input, web_input, slider_input],
-        outputs=[img_output_from_webcam, dashboard_output_webcam]
-    )
+cam_but.click(
+    detect_objects_image,
+    inputs=[options, url_input, img_input, web_input, slider_input],
+    outputs=[img_output_from_webcam, result_box],
+    queue=True
+)
 
 
 demo.queue()