Update app.py

app.py

@@ -3,6 +3,7 @@ import matplotlib.pyplot as plt
 import pandas as pd
 import os
 import random
+from PIL import ImageDraw
 
 # ===============================
 # GLOBAL VARIABLES
@@ -12,51 +13,41 @@ ev_count = 0
 total_co2_saved = 0
 
 DISTANCE_KM = 10
-CO2_PER_KM_PETROL = 0.150  # 150g = 0.15kg per km
-CO2_SAVED_PER_EV = DISTANCE_KM * CO2_PER_KM_PETROL  # 1.5 kg
+CO2_PER_KM_PETROL = 0.150
+CO2_SAVED_PER_EV = DISTANCE_KM * CO2_PER_KM_PETROL
 
 FEEDBACK_FILE = "feedback.csv"
 
-# Create CSV if not exists
 if not os.path.exists(FEEDBACK_FILE):
     df_init = pd.DataFrame(columns=["Predicted_Label", "Feedback"])
     df_init.to_csv(FEEDBACK_FILE, index=False)
 
-
 # ===============================
-# DUMMY VEHICLE CLASSIFIER
-# Replace with YOLO model
+# DUMMY CLASSIFIER (Replace with YOLO)
 # ===============================
 def classify_vehicle():
-    vehicle_types = [
+    return random.choice([
         "Car",
         "Bus",
         "Truck",
         "Motorcycle",
         "Electric Vehicle"
-    ]
-    return random.choice(vehicle_types)
-
+    ])
 
 # ===============================
-# DASHBOARD PLOT
+# DASHBOARD
 # ===============================
 def generate_dashboard():
     global total_vehicles, ev_count
-
     non_ev = total_vehicles - ev_count
 
     fig, ax = plt.subplots()
-    labels = ["EV", "Non-EV"]
-    values = [ev_count, non_ev]
-
-    ax.bar(labels, values)
+    ax.bar(["EV", "Non-EV"], [ev_count, non_ev])
     ax.set_title("Vehicle Distribution")
     ax.set_ylabel("Count")
 
     return fig
 
-
 # ===============================
 # DETECTION FUNCTION
 # ===============================
@@ -65,7 +56,7 @@ def detect_image(image, threshold):
     global total_vehicles, ev_count, total_co2_saved
 
     if image is None:
-        return None, "Upload an image first.", \
+        return None, "Upload image first.", \
                "### Total Vehicles: 0", \
                "### EV Vehicles: 0", \
                "### EV Adoption Rate: 0%", \
@@ -73,6 +64,7 @@ def detect_image(image, threshold):
                generate_dashboard(), ""
 
     vehicle_type = classify_vehicle()
+    plate_number = "KA01AB1234"
 
     total_vehicles += 1
     co2_saved_this = 0
@@ -84,13 +76,30 @@ def detect_image(image, threshold):
 
     ev_percent = (ev_count / total_vehicles) * 100
 
+    # Draw boxes
+    img = image.copy()
+    draw = ImageDraw.Draw(img)
+    w, h = img.size
+
+    vehicle_box = [w*0.1, h*0.2, w*0.9, h*0.8]
+    plate_box = [w*0.4, h*0.6, w*0.7, h*0.75]
+
+    draw.rectangle(vehicle_box, outline="red", width=4)
+    draw.text((vehicle_box[0], vehicle_box[1]-20),
+              f"Vehicle: {vehicle_type}", fill="red")
+
+    draw.rectangle(plate_box, outline="green", width=4)
+    draw.text((plate_box[0], plate_box[1]-20),
+              f"Plate: {plate_number}", fill="green")
+
     fig, ax = plt.subplots()
-    ax.imshow(image)
-    ax.set_title(f"Detected: {vehicle_type}")
+    ax.imshow(img)
     ax.axis("off")
+    ax.set_title("Detection Result")
 
     result_text = f"""
 Vehicle Type: {vehicle_type}
+Number Plate: {plate_number}
 Confidence Threshold: {threshold}
 CO₂ Saved (This Detection): {co2_saved_this:.2f} kg
 """
@@ -113,24 +122,16 @@ CO₂ Saved (This Detection): {co2_saved_this:.2f} kg
         vehicle_type
     )
 
-
 # ===============================
 # FEEDBACK FUNCTIONS
 # ===============================
 def save_feedback(predicted_label, feedback):
-
     df = pd.read_csv(FEEDBACK_FILE)
-
-    new_row = {
-        "Predicted_Label": predicted_label,
-        "Feedback": feedback
-    }
-
-    df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True)
+    df = pd.concat([df, pd.DataFrame(
+        [{"Predicted_Label": predicted_label, "Feedback": feedback}]
+    )], ignore_index=True)
     df.to_csv(FEEDBACK_FILE, index=False)
-
-    return "✅ Feedback Saved!"
-
+    return "Feedback Saved!"
 
 def calculate_metrics():
 
@@ -141,27 +142,25 @@ def calculate_metrics():
 
     tp = len(df[df["Feedback"] == "Correct"])
     fp = len(df[df["Feedback"] == "Incorrect"])
-
     total = len(df)
 
-    precision = tp / (tp + fp) if (tp + fp) != 0 else 0
-    recall = tp / total if total != 0 else 0
+    precision = tp / (tp + fp) if (tp + fp) else 0
+    recall = tp / total if total else 0
 
     metrics_text = f"""
 Total Samples: {total}
 
-True Positives (Correct): {tp}
-False Positives (Incorrect): {fp}
+True Positives: {tp}
+False Positives: {fp}
 
 Precision: {precision:.2f}
 Recall: {recall:.2f}
 """
 
     fig, ax = plt.subplots()
-    matrix = [[tp, fp],
-              [0, 0]]
-
+    matrix = [[tp, fp], [0, 0]]
     ax.imshow(matrix)
+
     ax.set_xticks([0,1])
     ax.set_yticks([0,1])
     ax.set_xticklabels(["Correct", "Incorrect"])
@@ -175,21 +174,18 @@ Recall: {recall:.2f}
 
     return metrics_text, fig
 
-
 def reset_database():
     df = pd.DataFrame(columns=["Predicted_Label", "Feedback"])
     df.to_csv(FEEDBACK_FILE, index=False)
-    return "🗑 Database Reset Successfully!"
-
+    return "Database Reset Successfully!"
 
 def download_feedback():
     return FEEDBACK_FILE
 
-
 # ===============================
 # GRADIO UI
 # ===============================
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
+with gr.Blocks() as demo:
 
     # ----------------------------
     # DETECTION TAB
@@ -198,13 +194,14 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
 
         gr.Markdown("## Smart Vehicle Classification & EV CO₂ Dashboard")
 
-        slider = gr.Slider(0.3, 1.0, 0.5, step=0.05, label="Confidence Threshold")
+        slider = gr.Slider(0.3, 1.0, 0.5, step=0.05,
+                           label="Confidence Threshold")
 
         with gr.Row():
             img_input = gr.Image(type="pil", label="Upload Vehicle Image")
             img_output = gr.Plot(label="Detection Output")
 
-        result_box = gr.Textbox(label="Detection Result", lines=5)
+        result_box = gr.Textbox(label="Detection Result", lines=6)
 
         with gr.Row():
             total_card = gr.Markdown("### Total Vehicles: 0")
@@ -269,14 +266,24 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         )
 
         gr.Markdown("### Download Feedback CSV")
-        download_btn = gr.File()
-        download_btn.click(fn=download_feedback, outputs=download_btn)
+
+        download_button = gr.Button("Download CSV")
+        download_file = gr.File()
+
+        download_button.click(
+            fn=download_feedback,
+            outputs=download_file
+        )
 
         gr.Markdown("### Reset Database")
+
         reset_btn = gr.Button("Reset Database")
         reset_output = gr.Textbox()
 
-        reset_btn.click(fn=reset_database, outputs=reset_output)
-
+        reset_btn.click(
+            fn=reset_database,
+            outputs=reset_output
+        )
 
-demo.launch()
+# Gradio 6 theme passed here
+demo.launch(theme=gr.themes.Soft())