Create app.py

app.py

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+import cv2 
+from ultralytics import YOLO ## for Yolov8
+import matplotlib.pyplot as plt
+import gradio as gr
+import numpy as np
+
+# function which is returning the number of object detected 
+def number_object_detected(image):
+    custom_model = YOLO("runs/detect/train9/weights/best.pt")
+    results = custom_model(image)
+
+    dic = results[0].names
+    cls = results[0].boxes.cls.cpu().numpy()
+    class_count = {}
+    unique_elements, counts = np.unique(cls, return_counts=True)
+    for e , count in zip(unique_elements,counts):
+        a = dic[e]
+        class_count[a] = count
+        print(f"the car has {count} {a}")
+    
+    return class_count , results
+
+
+# Define function to process input image and return annotated image
+def detect_objects(input_image):
+    cls , result = number_object_detected(input_image)
+    # Plot the results and return annotated image
+    for r in result:
+        im_array = r.plot(pil = True)  # plot a BGR numpy array of predictions
+        array = im_array[..., ::-1]  # Convert BGR to RGB PIL image
+        plt.axis("off")
+        plt.imshow(array)
+        plt.show()
+    
+    return array , cls
+
+# Define Gradio interface
+interface = gr.Interface(fn=detect_objects, inputs=gr.Image(type= 'pil', label='Upload Image of Car'), outputs=[gr.Image(),gr.Textbox(label="Number of Objects detected ")], title=" 🚘Car Scratch and Dent Detection")
+interface.launch()