chatbot_model_h5

import numpy as np from keras.models import load_model from keras.preprocessing.sequence import pad_sequences import pickle

Load the model and tokenizer

model = load_model('chatbot_model.h5')

with open('tokenizer.pkl', 'rb') as tokenizer_file: tokenizer = pickle.load(tokenizer_file)

Function to generate a response with adjustable temperature

def generate_response(seed_text, num_words, temperature=1.0): for _ in range(num_words): token_list = tokenizer.texts_to_sequences([seed_text])[0]

    # Ensure the sequence length does not exceed the model's input shape
    token_list = pad_sequences([token_list], maxlen=model.input_shape[1], padding='pre')
    
    # Predict the next word probabilities
    predicted_probs = model.predict(token_list, verbose=0)[0]
    
    # Adjust probabilities with temperature
    scaled_probs = np.log(predicted_probs) / temperature
    exp_probs = np.exp(scaled_probs)
    predicted_probs = exp_probs / np.sum(exp_probs)
    
    # Sample the next word index based on adjusted probabilities
    predicted_id = np.random.choice(len(predicted_probs), size=1, p=predicted_probs)[0]
    
    # Map the index to the corresponding word
    output_word = tokenizer.index_word.get(predicted_id, 'unknown')
    
    seed_text += " " + output_word

return seed_text

Interactive chat

print("Chatbot: Hello! Type 'exit' to end the conversation.") while True: user_input = input("You: ").lower() # Convert to lowercase for consistency if user_input == 'exit': print("Chatbot: Goodbye!") break

response = generate_response(user_input, num_words=20, temperature=0.5)
print("Chatbot:", response)