Added all the files

app.py

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+import numpy as np
+import pandas as pd
+import cv2
+import gradio as gr
+import torch
+from ultralyticsplus import YOLO, render_result
+import matplotlib.pyplot as plt
+
+# Creating a function to perform predictions
+def prediction(image: gr.Image = None, 
+                image_size: gr.Slider = 640,
+                conf_threshold: gr.Slider = 0.4,
+                iou_threshold: gr.Slider = 0.50):
+
+    model = YOLO("best.pt")
+
+    results = model.predict(
+        source=image,
+        conf=conf_threshold,
+        iou=iou_threshold,
+        imgsz=image_size
+    )
+    
+    image = cv2.imread(image)
+        
+    for result in results[0].obb:
+        point_1_x = float(result.xyxyxyxy[0][0][0])
+        point_1_y = float(result.xyxyxyxy[0][0][1])
+        point_2_x = float(result.xyxyxyxy[0][1][0])
+        point_2_y = float(result.xyxyxyxy[0][1][1])
+        point_3_x = float(result.xyxyxyxy[0][2][0])
+        point_3_y = float(result.xyxyxyxy[0][2][1])
+        point_4_x = float(result.xyxyxyxy[0][3][0])
+        point_4_y = float(result.xyxyxyxy[0][3][1])
+        
+        cls = int(result.cls)
+        
+        if cls == 1:
+            color = (0, 255, 0)
+        else:
+            color = (255, 0, 0)
+        
+        conf = float(result.conf)
+        
+        text = f"{cls} : {np.round(conf) * 100}%" 
+        
+        points = np.array([[point_1_x, point_1_y], 
+                           [point_2_x, point_2_y], 
+                           [point_3_x, point_3_y], 
+                           [point_4_x, point_4_y]], np.int32)
+         
+        points = points.reshape((-1, 1, 2))
+        
+        cv2.polylines(image, [points], isClosed = True, color = color, thickness = 2)   
+
+    return image
+
+inputs = [
+    gr.Image(type="filepath", label="Select an image"),
+    gr.Slider(minimum=320, maximum=1280, value=640, step=32, label="Image Size"),
+    gr.Slider(minimum=0.0, maximum=1.0, value=0.25, step=0.05, label="Confidence Threshold"),
+    gr.Slider(minimum=0.0, maximum=1.0, value=0.45, step=0.05, label="IOU Threshold")
+]
+
+outputs = gr.Image(type = "filepath", label="Output Image")  
+
+yolo_app = gr.Interface(
+    fn = prediction,
+    inputs = inputs,
+    outputs = outputs,
+    title = "VPS Model"
+)
+
+yolo_app.launch(debug = True, share = True)

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e0fc4b78a2d09baa3a84fd7abbc981a963d31d3986c2cbcbdc0a7ab872c57e9a
+size 6673154

requirements.txt

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+gradio==4.31.5
+matplotlib==3.7.2
+numpy==1.24.3
+opencv_python==4.7.0.72
+opencv_python_headless==4.8.0.74
+opencv_python_rolling==4.6.0.20220924
+pandas==2.0.3
+torch==2.1.2
+ultralyticsplus==0.1.0