Python chat bot is not recognizing when I increase the content of the intents file

The python bot code:

import nltk

nltk.download('punkt')

from nltk.stem.lancaster import LancasterStemmer
stemmer=LancasterStemmer()
import numpy
import tflearn
import tensorflow
import random
import json
import pickle

with open("intents.json") as file:
     data=json.load(file)
     print(data['intents'])
     
try:
    with open("data.pickle","rb") as f:
        words,labels,training,output=pickle.load(f)
except: 
    words=[]
    labels=[]
    docs_x=[]
    docs_y=[]
    
    for intent in data['intents']:
        for pattern in intent['patterns']:
            wrds=nltk.word_tokenize(pattern)
            words.extend(wrds)
            docs_x.append(wrds)
            docs_y.append(intent["tag"])
             
            
            if intent["tag"] not in labels:
               labels.append(intent["tag"])
               
               
    #remove duplicate          
    words=[stemmer.stem(w.lower()) for w in words if w != "?"]
    
    words=sorted(list(set(words)))
    
    labels=sorted(labels)
     
    
    training=[] 
    output=[]
    
    out_empty=[0 for _ in range(len(labels))]
    
    for x, doc in enumerate(docs_x):
        bag=[]
        wrds=[stemmer.stem(w) for w in doc]
        
        for w in words:
          if w in wrds:
             bag.append(1)
          else:
             bag.append(0)
             
        output_row=out_empty[:]
             
        output_row[labels.index(docs_y[x])]=1
        
        training.append(bag)     
        output.append(output_row)
        
    training=numpy.array(training)
    output=numpy.array(output)
    
    with open("data.pickle","wb") as f:
        pickle.dump((words,labels,training,output),f)

tensorflow.compat.v1.reset_default_graph()

net=tflearn.input_data(shape=[None,len(training[0])])
net=tflearn.fully_connected(net,8) 
net=tflearn.fully_connected(net,8)
net=tflearn.fully_connected(net,len(output[0]),activation="softmax")
net=tflearn.regression(net)

model=tflearn.DNN(net)

model.fit(training, output,n_epoch=10000,batch_size=8,show_metric=True )   

model.save('C:/Users/Desktop/chatbot/model/model.tflearn')
model.load('C:/Users/Desktop/chatbot/model/model.tflearn')

    





def bag_of_words(s,words):

   bag=[0 for _ in range(len(words))]
   s_words=nltk.word_tokenize(s)
   s_words=[stemmer.stem(word.lower()) for word in s_words]

 
   for se in s_words:
       for i,w in enumerate(words):
           if w==se:
              bag[i]=1

   return numpy.array(bag)



def chat():
    print("start talking with the bot (type quit to stop!")
    while True:
        inp=input("You:")
        if inp.lower()=="quit":
           break
       
        results= model.predict([bag_of_words(inp,words)])[0]
        # print("results:",results)
       
        results_index=numpy.argmax(results)
        
        if results[results_index]>0.7:
            
                
            tag=labels[results_index]
            print("tag:", tag)
        
            for tg in data["intents"]:
                if tg["tag"]==tag:
                   responses=tg['responses']
            
            print("Bot:",random.choice(responses))
         
        else:
          print("I didn't get that. Please try again")  
chat() 

The json file where the code is located is as follows:

intent file's content (JSON file):
{"intents": [
        {"tag": "greeting",
         "patterns": ["Hi", "How are you", "Is anyone there?", "Hello", "Good day", "Whats up"],
         "responses": ["Hello!", "Good to see you again!", "Hi there, how can I help?"],
         "context_set": ""
        },
        {"tag": "goodbye",
         "patterns": ["cya", "See you later", "Goodbye", "I am Leaving", "Have a Good day"],
         "responses": ["Sad to see you go :(", "Talk to you later", "Goodbye!"],
         "context_set": ""
        },
        {"tag": "age",
         "patterns": ["how old", "what is your age", "how old are you", "age?"],
         "responses": ["good question", "I don't know"],
         "context_set": ""
        },
        {"tag": "name",
         "patterns": ["what is your name", "what should I call you", "whats your name?"],
         "responses": ["You can call me Bot.", "I'm Bot!"],
         "context_set": ""
        },
        {"tag": "shop",
         "patterns": ["Id like to buy something", "whats on the menu", "what do you reccommend?", "could i get something to eat"],
         "responses": ["We sell chocolate chip cookies for $2!", "Cookies are on the menu!"],
         "context_set": ""
        },
        {"tag": "hours",
         "patterns": ["when are you guys open", "what are your hours", "hours of operation"],
         "responses": ["We are open 7am-4pm Monday-Friday!"],
         "context_set": ""
        }
   ]
}

Hello friends, When adding "tag" "hours", "patterns", "responses" to the json file as the 7th,8th, etc., the bot does not recognize it and cannot answer the questions. How can I make the bot recognize them when I expand the contents of the json file?

I realized that actually, the code works correctly, but it is necessary to pay attention to if the content of the intent file changes, it is necessary to delete the ".pickle" file and run the code again.