no output.

[rahul55552003]

hello, the model training is done successfully. but when i type 'hello', there is no response. i have also changed the line in assistants.py with " self.model.add(InputLayer(input_shape=(X.shape[1],))) " as suggested by some user. can you please help me guys. iam new to this and iam stuck. i dont know how to resolve this. thank you for your patience.

intents.json

`from neuralintents import BasicAssistant
import matplotlib.pyplot as plt
import pandas as pd
import pandas_datareader as web
import mplfinance as mpf
from tensorflow.keras.layers import Dense, Dropout

import pickle
import sys
import datetime as dt

#{

GenericAssistant = BasicAssistant,

#train_model = fit_model,
#request() = process_input().

def myfunction():

pass

mappings = {

'greetings': myfunction

}

# Initialize the assistant with the intents file

assistant = BasicAssistant('intents.json', intent_methods = mappings)

# Train the model using the new method

assistant.fit_model()

# Request user input to interact with the assistant

assistant.process_input()

}

#{ YOU CAN DELETE THIS LINE ONCE THE PORTFOLIO FILE IS CREATED :

portfolio = {'AAPL': 20, 'TSLA': 5, 'GS': 10}

with open('portfolio.pkl', 'wb') as f:

pickle.dump(portfolio, f)

#}

with open('portfolio.pkl', 'rb') as f:
portfolio = pickle.load(f)

#print(portfolio)

def save_portfolio():
with open('portfolio.pkl', 'wb') as f:
pickle.dump(portfolio, f)

def add_portfolio():
ticker = input("Which Stock Do You Want to add: ")
amount = input("How many shares do you want to add: ")

if ticker in portfolio.keys():
    portfolio[ticker] += amount
else:
    portfolio[ticker] = amount

save_portfolio()

def remove_portfolio():
ticker = input("Which stock do you want to sell: ")
amount = input("How many shares do you want to sell: ")

if ticker in portfolio.keys():
    if amount <= portfolio[ticker]:
        portfolio[ticker] -= amount
        save_portfolio()
    else:
        print("You dont have enough shares!")
else:
    print(f"You dont own any shares of {ticker}")

def show_portfolio():
print("Your portfolio: ")
for ticker in portfolio.keys():
print(f"You own {portfolio[ticker]} shares of {ticker}")

def portfolio_worth():
sum = 0
for ticker in portfolio.keys():
data = web.DataReader(ticker, 'yahoo')
price = data['Close'].iloc[-1]
sum += price
print(f"Your portfolio is worth {sum} USD")

def portfolio_gains():
starting_date = input("Enter a date for comparision (YYYY-MM-DD): ")

sum_now = 0
sum_then = 0

try:
    for ticker in portfolio.keys():
        data = web.DataReader(ticker, 'yahoo')
        price_now = data['Close'].iloc[-1]
        price_then = data.loc[data.index == starting_date]['Close'].values[0]
        sum_now += price_now
        sum_then += price_then

    print(f"Relative Gains: {((sum_now - sum_then) / sum_then) * 100}%")
    print(f"Absolute Gains: {sum_now - sum_then} USD")

except IndexError:
    print("There was no trading on this day.")

def plot_chart():
ticker = input("Choose a ticker symbol: ")
starting_string = input("Choose a stating date (DD/MM/YYYY): ")

plt.style.use('dark_background')

start = dt.datetime.strptime(starting_string, "%d/%m/Y")
end = dt.datetime.now()

data = web.DataReader(ticker, 'yahoo', start, end)

colors = mpf.make_marketcolors(up='#00ff00', down='ff0000', wick='inherit', edge='inherit', volume='in')
mpf_style = mpf.make_mpf_style(base_mpf_style='nightclouds', marketcolors=colors)
mpf.plot(data, type='candle', style=mpf_style, volume=True)

def bye():
print("Good Bye")
sys.exit(0)

mappings = {
'plot_chart': plot_chart,
'add_portfolio': add_portfolio,
'remove_portfolio': remove_portfolio,
'show_portfolio': show_portfolio,
'portfolio_worth': portfolio_worth,
'portfolio_gains': portfolio_gains,
'bye': bye
}

Define any custom hidden layers if needed

hidden_layers = [

Dense(128, activation='relu'),

Dropout(0.5),

Dense(64, activation='relu')

]

Correct constructor call

assistant = BasicAssistant('intents.json', model_name="financial_assistant_model")

assistant.fit_model()
assistant.save_model()

while True:
message = input("")
assistant.process_input(message)

# Test the model prediction

test_input = "Add a stock to my portfolio"

predicted_response = assistant.process_input(test_input)

print(predicted_response)

`