Update app.py

app.py

@@ -1,55 +1,62 @@
 import gradio as gr
 from PIL import Image
-import matplotlib.pyplot as plt
 import numpy as np
+import cv2
 from ultralytics import YOLO
 from transformers import pipeline
+import matplotlib.pyplot as plt
 
 # Load models
-license_model = YOLO('yolov8n.pt')  # public YOLOv8 model
+yolo_model = YOLO("yolov8n.pt")
 damage_pipe = pipeline("image-classification", model="beingamit99/car_damage_detection")
 
-# License plate detection
+# License Plate Detection
 def detect_license_plate(img: Image.Image):
-    results = license_model.predict(img)
+    results = yolo_model.predict(img)
     result = results[0]
+    img_array = np.array(img.convert("RGB"))
 
-    # Plot boxes
-    img = np.array(img.convert("RGB"))
     for box in result.boxes.xyxy:
-        x1, y1, x2, y2 = box.int().tolist()
-        img = img.copy()
-        img = cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2)
+        x1, y1, x2, y2 = map(int, box)
+        img_array = cv2.rectangle(img_array, (x1, y1), (x2, y2), (0, 255, 0), 2)
 
-    return Image.fromarray(img)
+    return Image.fromarray(img_array)
 
-# Damage classification with bar chart
-def classify_damage(img: Image.Image):
-    predictions = damage_pipe(img)
-
-    # Prepare bar graph
-    labels = [pred["label"] for pred in predictions]
-    scores = [pred["score"] for pred in predictions]
+# Car Damage Classification
+def classify_damage(image: Image.Image):
+    results = damage_pipe(image)
+    labels = [res["label"] for res in results]
+    scores = [res["score"] for res in results]
 
     fig, ax = plt.subplots()
-    ax.barh(labels, scores, color='orange')
-    ax.set_xlim(0, 1)
+    ax.barh(labels, scores, color="crimson")
     ax.set_xlabel("Confidence")
-    ax.set_title("Damage Classification")
+    ax.set_xlim(0, 1)
+    ax.set_title("Damage Type Classification")
     plt.tight_layout()
 
     return fig
 
-# Combined interface
+# Build UI
 with gr.Blocks() as app:
-    gr.Markdown("# 🚗 Car License Plate & Damage Detector")
-
-    with gr.Row():
-        image_input = gr.Image(type="pil", label="Upload Car Image")
-        license_output = gr.Image(label="License Plate Detection")
-        damage_output = gr.Plot(label="Damage Classification")
-
-    image_input.change(fn=detect_license_plate, inputs=image_input, outputs=license_output)
-    image_input.change(fn=classify_damage, inputs=image_input, outputs=damage_output)
+    gr.Markdown("# 🚘 Car Analyzer\nChoose a tool below:")
+
+    with gr.Tab("🛂 License Plate Detection"):
+        with gr.Row():
+            with gr.Column():
+                lp_input = gr.Image(type="pil", label="Upload Car Image")
+                lp_btn = gr.Button("Detect")
+            with gr.Column():
+                lp_output = gr.Image(label="Detected License Plate")
+        lp_btn.click(detect_license_plate, inputs=lp_input, outputs=lp_output)
+
+    with gr.Tab("💥 Car Damage Classification"):
+        with gr.Row():
+            with gr.Column():
+                dmg_input = gr.Image(type="pil", label="Upload Damaged Car Image")
+                dmg_btn = gr.Button("Classify")
+            with gr.Column():
+                dmg_output = gr.Plot(label="Classification Results")
+        dmg_btn.click(classify_damage, inputs=dmg_input, outputs=dmg_output)
 
 app.launch()