Update app.py

app.py

@@ -19,23 +19,20 @@ CO2_SAVED_PER_EV = DISTANCE_KM * CO2_PER_KM_PETROL
 FEEDBACK_FILE = "feedback.csv"
 
 if not os.path.exists(FEEDBACK_FILE):
-    pd.DataFrame(columns=["Predicted_Label", "Feedback"]).to_csv(FEEDBACK_FILE, index=False)
+    df_init = pd.DataFrame(columns=["Predicted_Label", "Feedback"])
+    df_init.to_csv(FEEDBACK_FILE, index=False)
 
 # ===============================
-# SIMULATED NUMBER PLATE COLOUR DETECTION
-# Replace with real color detection later
+# DUMMY CLASSIFIER (Replace with YOLO)
 # ===============================
-def detect_plate_colour():
-    return random.choice(["White", "Yellow", "Green", "Black"])
-
-def get_vehicle_type_from_colour(colour):
-    mapping = {
-        "White": "Private Vehicle",
-        "Yellow": "Commercial Vehicle",
-        "Green": "Electric Vehicle",
-        "Black": "Rental Vehicle"
-    }
-    return mapping.get(colour, "Unknown")
+def classify_vehicle():
+    return random.choice([
+        "Car",
+        "Bus",
+        "Truck",
+        "Motorcycle",
+        "Electric Vehicle"
+    ])
 
 # ===============================
 # DASHBOARD
@@ -48,41 +45,25 @@ def generate_dashboard():
     ax.bar(["EV", "Non-EV"], [ev_count, non_ev])
     ax.set_title("Vehicle Distribution")
     ax.set_ylabel("Count")
-    return fig
-
-# ===============================
-# EVALUATION SUMMARY
-# ===============================
-def get_evaluation_summary():
-    df = pd.read_csv(FEEDBACK_FILE)
-    total = len(df)
-    correct = len(df[df["Feedback"] == "Correct"])
-    incorrect = len(df[df["Feedback"] == "Incorrect"])
-
-    if total == 0:
-        return "Evaluation Summary:\nNo feedback yet."
 
-    precision = correct / total
-
-    return f"""Evaluation Summary
-Total: {total}
-Correct: {correct}
-Incorrect: {incorrect}
-Accuracy: {precision:.2f}
-"""
+    return fig
 
 # ===============================
 # DETECTION FUNCTION
 # ===============================
-def detect_image(image):
+def detect_image(image, threshold):
 
     global total_vehicles, ev_count, total_co2_saved
 
     if image is None:
-        return None, "Upload image first.", "", "", "", "", "", ""
-
-    plate_colour = detect_plate_colour()
-    vehicle_type = get_vehicle_type_from_colour(plate_colour)
+        return None, "Upload image first.", \
+               "### 🚘 Total Vehicles: 0", \
+               "### ⚡ EV Vehicles: 0", \
+               "### 📊 EV Adoption Rate: 0%", \
+               "### 🌱 CO₂ Saved: 0 kg", \
+               generate_dashboard(), ""
+
+    vehicle_type = classify_vehicle()
     plate_number = "KA01AB1234"
 
     total_vehicles += 1
@@ -105,36 +86,44 @@ def detect_image(image):
 
     draw.rectangle(vehicle_box, outline="red", width=4)
     draw.text((vehicle_box[0], vehicle_box[1]-20),
-              f"{vehicle_type}", fill="red")
+              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_number} ({plate_colour})", fill="green")
+              f"Plate: {plate_number}", fill="green")
 
     fig, ax = plt.subplots()
     ax.imshow(img)
     ax.axis("off")
+    ax.set_title("Detection Result")
 
     result_text = f"""
 Vehicle Type: {vehicle_type}
-Plate Colour: {plate_colour}
-Plate Number: {plate_number}
-CO₂ Saved: {co2_saved_this:.2f} kg
+Number Plate: {plate_number}
+Confidence Threshold: {threshold}
+CO₂ Saved (This Detection): {co2_saved_this:.2f} kg
 """
 
-    total_card = f"### 🚘 Total: {total_vehicles}"
-    ev_card = f"### ⚡ EV: {ev_count}"
-    percent_card = f"### 📊 EV Rate: {ev_percent:.2f}%"
+    total_card = f"### 🚘 Total Vehicles: {total_vehicles}"
+    ev_card = f"### ⚡ EV Vehicles: {ev_count}"
+    percent_card = f"### 📊 EV Adoption Rate: {ev_percent:.2f}%"
     co2_card = f"### 🌱 CO₂ Saved: {total_co2_saved:.2f} kg"
 
     dashboard_fig = generate_dashboard()
 
-    summary = get_evaluation_summary()
-
-    return fig, result_text, total_card, ev_card, percent_card, co2_card, dashboard_fig, vehicle_type, summary
+    return (
+        fig,
+        result_text,
+        total_card,
+        ev_card,
+        percent_card,
+        co2_card,
+        dashboard_fig,
+        vehicle_type
+    )
 
 # ===============================
-# FEEDBACK SAVE
+# FEEDBACK FUNCTIONS
 # ===============================
 def save_feedback(predicted_label, feedback):
     df = pd.read_csv(FEEDBACK_FILE)
@@ -142,85 +131,159 @@ def save_feedback(predicted_label, feedback):
         [{"Predicted_Label": predicted_label, "Feedback": feedback}]
     )], ignore_index=True)
     df.to_csv(FEEDBACK_FILE, index=False)
-    return "Saved!", get_evaluation_summary()
+    return "✅ Feedback Saved!"
+
+def calculate_metrics():
+
+    df = pd.read_csv(FEEDBACK_FILE)
+
+    if len(df) == 0:
+        return "No feedback data available.", None
+
+    tp = len(df[df["Feedback"] == "Correct"])
+    fp = len(df[df["Feedback"] == "Incorrect"])
+    total = len(df)
+
+    precision = tp / (tp + fp) if (tp + fp) else 0
+    recall = tp / total if total else 0
+
+    metrics_text = f"""
+Total Samples: {total}
+
+True Positives: {tp}
+False Positives: {fp}
+
+Precision: {precision:.2f}
+Recall: {recall:.2f}
+"""
+
+    fig, ax = plt.subplots()
+    matrix = [[tp, fp], [0, 0]]
+    ax.imshow(matrix)
+
+    ax.set_xticks([0,1])
+    ax.set_yticks([0,1])
+    ax.set_xticklabels(["Correct", "Incorrect"])
+    ax.set_yticklabels(["Predicted"])
+
+    for i in range(2):
+        for j in range(2):
+            ax.text(j, i, matrix[i][j], ha="center", va="center")
+
+    ax.set_title("Confusion Matrix")
+
+    return metrics_text, fig
 
-# ===============================
-# RESET DATABASE
-# ===============================
 def reset_database():
-    pd.DataFrame(columns=["Predicted_Label", "Feedback"]).to_csv(FEEDBACK_FILE, index=False)
-    return "Database Reset!", "Evaluation Summary:\nNo feedback yet."
+    df = pd.DataFrame(columns=["Predicted_Label", "Feedback"])
+    df.to_csv(FEEDBACK_FILE, index=False)
+    return "🗑 Database Reset Successfully!"
+
+def download_feedback():
+    return FEEDBACK_FILE
 
 # ===============================
-# UI
+# GRADIO UI
 # ===============================
 with gr.Blocks() as demo:
 
-    gr.Markdown("## 🚦 Smart Vehicle Detection Based on Number Plate Colour")
+    # ----------------------------
+    # DETECTION TAB
+    # ----------------------------
+    with gr.Tab("🚦 Detection System"):
 
-    with gr.Row():
-        img_input = gr.Image(type="pil", label="Upload Vehicle Image")
-        img_output = gr.Plot()
+        gr.Markdown("## Smart Vehicle Classification & EV CO₂ Dashboard")
 
-    detect_btn = gr.Button("Detect", size="sm")
+        slider = gr.Slider(0.3, 1.0, 0.5, step=0.05,
+                           label="Confidence Threshold")
 
-    result_box = gr.Textbox(label="Detection Result", lines=5)
+        with gr.Row():
+            img_input = gr.Image(type="pil", label="Upload Vehicle Image")
+            img_output = gr.Plot(label="Detection Output")
 
-    with gr.Row():
-        total_card = gr.Markdown("### 🚘 Total: 0")
-        ev_card = gr.Markdown("### ⚡ EV: 0")
-        percent_card = gr.Markdown("### 📊 EV Rate: 0%")
-        co2_card = gr.Markdown("### 🌱 CO₂ Saved: 0 kg")
+        result_box = gr.Textbox(label="Detection Result", lines=6)
 
-    dashboard_plot = gr.Plot()
+        with gr.Row():
+            total_card = gr.Markdown("### 🚘 Total Vehicles: 0")
+            ev_card = gr.Markdown("### ⚡ EV Vehicles: 0")
+            percent_card = gr.Markdown("### 📊 EV Adoption Rate: 0%")
+            co2_card = gr.Markdown("### 🌱 CO₂ Saved: 0 kg")
 
-    predicted_label_state = gr.State()
+        dashboard_plot = gr.Plot(label="Analytics Dashboard")
 
-    detect_btn.click(
-        fn=detect_image,
-        inputs=[img_input],
-        outputs=[
-            img_output,
-            result_box,
-            total_card,
-            ev_card,
-            percent_card,
-            co2_card,
-            dashboard_plot,
-            predicted_label_state,
-            gr.Markdown()  # evaluation summary placeholder
-        ]
-    )
+        predicted_label_state = gr.State()
 
-    # -------------------------
-    # FEEDBACK + EVALUATION ROW
-    # -------------------------
-    with gr.Row():
+        detect_btn = gr.Button("🔍 Detect Vehicle")
 
-        with gr.Column(scale=1):
-            correct_btn = gr.Button("✔", size="sm")
-            incorrect_btn = gr.Button("✘", size="sm")
-            feedback_status = gr.Textbox(label="Feedback", lines=1)
+        detect_btn.click(
+            fn=detect_image,
+            inputs=[img_input, slider],
+            outputs=[
+                img_output,
+                result_box,
+                total_card,
+                ev_card,
+                percent_card,
+                co2_card,
+                dashboard_plot,
+                predicted_label_state
+            ]
+        )
 
-        with gr.Column(scale=1):
-            evaluation_summary = gr.Textbox(label="Evaluation Summary", lines=6)
+        gr.Markdown("### Provide Feedback")
 
-    correct_btn.click(
-        fn=save_feedback,
-        inputs=[predicted_label_state, gr.State("Correct")],
-        outputs=[feedback_status, evaluation_summary]
-    )
+        correct_btn = gr.Button("✔ Correct")
+        incorrect_btn = gr.Button("✘ Incorrect")
+        feedback_status = gr.Textbox(label="Feedback Status")
 
-    incorrect_btn.click(
-        fn=save_feedback,
-        inputs=[predicted_label_state, gr.State("Incorrect")],
-        outputs=[feedback_status, evaluation_summary]
-    )
+        correct_btn.click(
+            fn=save_feedback,
+            inputs=[predicted_label_state, gr.State("Correct")],
+            outputs=feedback_status
+        )
 
-    reset_btn = gr.Button("Reset Database", size="sm")
-    reset_btn.click(
-        fn=reset_database,
-        outputs=[feedback_status, evaluation_summary]
-    )
+        incorrect_btn.click(
+            fn=save_feedback,
+            inputs=[predicted_label_state, gr.State("Incorrect")],
+            outputs=feedback_status
+        )
+
+    # ----------------------------
+    # ADMIN DASHBOARD TAB
+    # ----------------------------
+    with gr.Tab("📊 Admin Dashboard"):
+
+        gr.Markdown("## Model Evaluation Dashboard")
+
+        metrics_box = gr.Textbox(label="Evaluation Metrics", lines=10)
+        confusion_plot = gr.Plot()
+
+        evaluate_btn = gr.Button("Calculate Metrics")
+
+        evaluate_btn.click(
+            fn=calculate_metrics,
+            outputs=[metrics_box, confusion_plot]
+        )
+
+        gr.Markdown("### Download Feedback CSV")
+
+        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
+        )
 
+# Gradio 6 theme must be passed here
 demo.launch(theme=gr.themes.Soft())