Update app.py

app.py

@@ -1,10 +1,8 @@
 from ultralytics import YOLO
 import numpy as np
-import matplotlib.pyplot as plt
 import cv2 
 import gradio as gr
 import pickle
-
 # function which is returning the number of object detected 
 def number_object_detected(image):
     
@@ -20,12 +18,9 @@ def number_object_detected(image):
     for e , count in zip(unique_elements,counts):
         a = dic[e]
         class_count[a] = count
-
-    
+       
     return (class_count,results )
 
-
-
 def car_detection_and_Cropping(image_path):
     simple_yolo = YOLO('yolov8m.pt')
     r = simple_yolo(image_path,verbose = False)
@@ -59,6 +54,7 @@ def car_detection_and_Cropping(image_path):
       class_c ,result= number_object_detected(image_path)
     return class_c ,result
 
+
 severity_points = {
     'scratch': 1,
     'dent': 2,
@@ -100,6 +96,8 @@ def estimate_condition(detections):
        
         return "Very Poor"
 
+
+
 with open('Price_prediction_decision_tree.pkl', 'rb') as file:
     loaded_pipe_lr = pickle.load(file)
 
@@ -110,7 +108,7 @@ def process_data(files,car_brand, car_name, model_year, mileage, city_registered
     file_names = [f[0] for f in files]
     image_r = []
     
-    print("HEllo")
+    
     damage_dic = {}
 
     for f in file_names:
@@ -135,6 +133,7 @@ def process_data(files,car_brand, car_name, model_year, mileage, city_registered
     
     return (condition , str(price[0])+'lacs' , image_r)
 
+
 years_list = list(range(2024, 1899, -1))
 gr.Interface(fn = process_data,
              inputs=[gr.Gallery(label="Upload Files", type="filepath"),