Update app.py

app.py

@@ -25,6 +25,7 @@ BASE_AMT = 100
 
 conn = sqlite3.connect("vehicles.db", check_same_thread=False)
 cursor = conn.cursor()
+
 cursor.execute("""
 CREATE TABLE IF NOT EXISTS vehicles (
     plate TEXT,
@@ -33,9 +34,17 @@ CREATE TABLE IF NOT EXISTS vehicles (
     time TEXT
 )
 """)
+
+cursor.execute("""
+CREATE TABLE IF NOT EXISTS feedback (
+    result TEXT,
+    feedback TEXT
+)
+""")
+
 conn.commit()
 
-# -------------------- MODEL (Lazy Load) --------------------
+# -------------------- MODEL --------------------
 
 processor = None
 model = None
@@ -61,15 +70,13 @@ def get_original_image(url):
     response = requests.get(url, stream=True)
     return Image.open(response.raw).convert("RGB")
 
-# -------------------- DISCOUNT LOGIC --------------------
+# -------------------- LOGIC --------------------
 
 def compute_discount(vehicle_type):
     if vehicle_type == "EV":
         return BASE_AMT * 0.9
     return BASE_AMT
 
-# -------------------- PLATE COLOR CLASSIFICATION --------------------
-
 def classify_plate_color(plate_img):
     img = np.array(plate_img)
     hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
@@ -83,8 +90,6 @@ def classify_plate_color(plate_img):
         return "Commercial"
     return "Personal"
 
-# -------------------- OCR --------------------
-
 def read_plate(plate_img):
     gray = cv2.cvtColor(np.array(plate_img), cv2.COLOR_RGB2GRAY)
     gray = cv2.threshold(gray, 120, 255, cv2.THRESH_BINARY)[1]
@@ -95,8 +100,6 @@ def read_plate(plate_img):
     )
     return text.strip() if text.strip() else "UNKNOWN"
 
-# -------------------- YOLOS INFERENCE --------------------
-
 def make_prediction(img):
     processor, model = load_model()
     inputs = processor(images=img, return_tensors="pt")
@@ -113,14 +116,6 @@ def make_prediction(img):
 
 # -------------------- VISUALIZATION --------------------
 
-def fig_to_img(fig):
-    buf = io.BytesIO()
-    fig.savefig(buf, bbox_inches="tight")
-    buf.seek(0)
-    img = Image.open(buf)
-    plt.close(fig)
-    return img
-
 def visualize(img, output, id2label, threshold):
     keep = output["scores"] > threshold
     boxes = output["boxes"][keep]
@@ -128,7 +123,6 @@ def visualize(img, output, id2label, threshold):
 
     fig, ax = plt.subplots(figsize=(8,8))
     ax.imshow(img)
-
     results_text = []
 
     for box, label in zip(boxes, labels):
@@ -161,33 +155,37 @@ def visualize(img, output, id2label, threshold):
     ax.axis("off")
 
     if not results_text:
-        return fig_to_img(fig), "No plate detected"
+        return fig, "No plate detected"
+
+    return fig, "\n".join(results_text)
 
-    return fig_to_img(fig), "\n".join(results_text)
+# -------------------- FEEDBACK --------------------
+
+def submit_feedback(result_text, feedback_choice):
+    if not result_text:
+        return "No result to evaluate."
+
+    cursor.execute(
+        "INSERT INTO feedback VALUES (?, ?)",
+        (result_text, feedback_choice)
+    )
+    conn.commit()
 
-# -------------------- DASHBOARD --------------------
+    return "Feedback recorded!"
 
-def get_dashboard():
-    df = pd.read_sql("SELECT * FROM vehicles", conn)
-    fig, ax = plt.subplots()
+def show_accuracy():
+    df = pd.read_sql("SELECT * FROM feedback", conn)
 
     if df.empty:
-        ax.text(0.5,0.5,"No data yet",ha="center")
-        ax.axis("off")
-        return fig
+        return "No feedback data available."
 
-    df["type"].value_counts().plot(kind="bar", ax=ax)
-    ax.set_title("Vehicle Types")
-    return fig
+    correct = len(df[df["feedback"] == "Correct"])
+    total = len(df)
 
-# -------------------- MAIN CALLBACK --------------------
+    accuracy = (correct / total) * 100
+    return f"Model Accuracy (based on user feedback): {accuracy:.2f}%"
 
-def detect_from_url(url, threshold):
-    if not url or not is_valid_url(url):
-        return None, "Invalid URL"
-    img = get_original_image(url)
-    output, id2label = make_prediction(img)
-    return visualize(img, output, id2label, threshold)
+# -------------------- CALLBACKS --------------------
 
 def detect_from_image(img, threshold):
     if img is None:
@@ -198,41 +196,45 @@ def detect_from_image(img, threshold):
 # -------------------- UI --------------------
 
 with gr.Blocks() as demo:
-    gr.Markdown("## Smart Vehicle Classification System")
+    gr.Markdown("## 🚦 Smart Vehicle Classification System")
 
     slider = gr.Slider(0.3, 1.0, 0.5, label="Confidence Threshold")
-    result_box = gr.Textbox(label="Result", lines=4)
-
-    with gr.Tabs():
-
-        with gr.Tab("Image URL"):
-            url_input = gr.Textbox(label="Image URL")
-            url_output = gr.Image()
-            url_btn = gr.Button("Detect")
-            url_btn.click(detect_from_url,
-                          inputs=[url_input, slider],
-                          outputs=[url_output, result_box])
-
-        with gr.Tab("Upload"):
-            img_input = gr.Image(type="pil")
-            img_output = gr.Image()
-            img_btn = gr.Button("Detect")
-            img_btn.click(detect_from_image,
-                          inputs=[img_input, slider],
-                          outputs=[img_output, result_box])
-
-        with gr.Tab("Webcam"):
-            cam_input = gr.Image(
-                sources=["webcam"],   # using web camera
-                type="pil"
-            )
-            cam_output = gr.Image()
-            cam_btn = gr.Button("Detect")
-            cam_btn.click(detect_from_image,
-                          inputs=[cam_input, slider],
-                          outputs=[cam_output, result_box])
-
-    gr.Markdown("###Dashboard")
-    gr.Plot(get_dashboard)
+    result_box = gr.Textbox(label="Detection Result", lines=4)
+
+    img_input = gr.Image(type="pil")
+    img_output = gr.Plot()
+
+    detect_btn = gr.Button("Detect")
+    detect_btn.click(
+        detect_from_image,
+        inputs=[img_input, slider],
+        outputs=[img_output, result_box]
+    )
+
+    gr.Markdown("### Provide Feedback")
+
+    feedback_radio = gr.Radio(
+        ["Correct", "Incorrect"],
+        label="Was the prediction correct?"
+    )
+
+    feedback_btn = gr.Button("Submit Feedback")
+    feedback_msg = gr.Textbox(label="Feedback Status")
+
+    feedback_btn.click(
+        submit_feedback,
+        inputs=[result_box, feedback_radio],
+        outputs=feedback_msg
+    )
+
+    gr.Markdown("### Model Evaluation")
+
+    accuracy_btn = gr.Button("Show Accuracy")
+    accuracy_box = gr.Textbox(label="Accuracy")
+
+    accuracy_btn.click(
+        show_accuracy,
+        outputs=accuracy_box
+    )
 
 demo.launch()