decision tree integration fix + end text

src/main.py

@@ -244,24 +244,24 @@ print(len(check_path_A))
 current_node = 0
 step = 1
 comeback = False
-weather = random.choice(['Sunny', 'Rainy', 'Cloudy'])
-temperature = random.choice(['High', 'Medium', 'Low'])
-windspeed = random.choice(['Strong', 'Weak'])
-pollenavailability = random.choice(['Low', 'Medium', 'High'])
-beepopulation = random.choice(['Low','Moderate', 'High'])
-timeofday = random.choice(['Morning', 'Afternoon', 'Evening'])
+weather = random.choice([2, 1, 0])
+temperature = random.choice([0,1,2])
+windspeed = random.choice([1,0])
+pollenavailability = random.choice([0,1,2])
+beepopulation = random.choice([0,1,2])
+timeofday = random.choice([0,1,2])
 if len(check_path_A)>80:
-    flowerdistance = 'Far'
+    flowerdistance = 1
 elif len(check_path_A)>50:
-    flowerdistance = 'Medium'
+    flowerdistance = 2
 else:
-    flowerdistance = 'Close'
-humidity = random.choice(['High','Normal'])
+    flowerdistance = 0
+humidity = random.choice([1,2])
 predictiondata = [[weather, temperature, windspeed, pollenavailability, beepopulation, timeofday, flowerdistance, humidity]]
 feature_cols = ['Weather','Temperature','WindSpeed','PollenAvailability','BeePopulation','TimeOfDay','FlowerDistance','Humidity']
 le = LabelEncoder()
 le.fit(feature_cols)
-encoded_data = pd.DataFrame(predictiondata, columns=feature_cols).apply(le.fit_transform)
+encoded_data = pd.DataFrame(predictiondata, columns=feature_cols)
 prediction = clf.predict(encoded_data)
 font = pygame.font.Font(None,32)
 while True:
@@ -312,6 +312,10 @@ while True:
         current_node = 0
         comeback = True
     elif current_node + 1 == len(path):
+        endtext = font.render('ДЕЛО СДЕЛАНО', True, (255,0,0))
+        endtext_rect = endtext.get_rect(center=(disX // 2, disY // 2))
+        dis.blit(endtext, endtext_rect)
+        pygame.display.update()
         pygame.time.wait(1000000)
     if(step == -1 and current_node == 0):
         step *= -1