Update app.py

app.py

@@ -3,6 +3,7 @@ import numpy as np
 import cv2 
 import gradio as gr
 import pickle
+
 # function which is returning the number of object detected 
 def number_object_detected(image):
     
@@ -18,9 +19,13 @@ def number_object_detected(image):
     for e , count in zip(unique_elements,counts):
         a = dic[e]
         class_count[a] = count
-       
+    #print(class_count)
+
+    
+    
     return (class_count,results )
 
+
 def car_detection_and_Cropping(image_path):
     simple_yolo = YOLO('yolov8m.pt')
     r = simple_yolo(image_path,verbose = False)
@@ -54,7 +59,6 @@ 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,
@@ -81,7 +85,7 @@ def estimate_condition(detections):
     print("normalized Score",normalized_score)
     # Assign condition rating
     if normalized_score <= 4:  # If score is low, condition is Excellent
-        print("Condition is Excellent")
+        
         return "Excellent"
     elif (normalized_score >4 and normalized_score <=7):  # If score is moderately low, condition is Good
        
@@ -96,12 +100,9 @@ def estimate_condition(detections):
        
         return "Very Poor"
 
-
-
 with open('Price_prediction_decision_tree.pkl', 'rb') as file:
     loaded_pipe_lr = pickle.load(file)
 
-
 ## loading the model
 def process_data(files,car_brand, car_name, model_year, mileage, city_registered, color, engine_c, trans, fuel_type, Cate):
     
@@ -131,8 +132,9 @@ def process_data(files,car_brand, car_name, model_year, mileage, city_registered
     if price[0] >= 100:
           price[0] = price[0]/100
     
-    return (condition , str(price[0])+'lacs' , image_r)
+    return ( str(price[0])+'lacs' , image_r)
 
+    
 
 years_list = list(range(2024, 1899, -1))
 gr.Interface(fn = process_data,
@@ -148,5 +150,4 @@ gr.Interface(fn = process_data,
                      # gr.Radio(["imported", "local"], label="Assembly Type"),
                       gr.Radio(["hybrid", "petrol",'diesel'], label="Fuel Type"),
                       gr.Radio(["hatchback", "sedan",'suv','croosover','van'], label="Category")],
-                    outputs=[gr.Textbox(label="Predicted Price"),gr.Gallery(label='output',type='pil')]).launch()
-
+                    outputs=[gr.Textbox(label="Predicted Price"),gr.Gallery(label='output',type='pil')]).launch()