app.py

#!/usr/bin/env python
# coding: utf-8

# In[ ]:


import streamlit as st
import nltk
from nltk.stem import WordNetLemmatizer
import pickle
import numpy as np
from tensorflow.keras.models import load_model

nltk.download('punkt')
nltk.download('wordnet')

# Load saved model and other necessary files
model = load_model("chatbot_model.h5")
words = pickle.load(open('words.pkl', 'rb'))
classes = pickle.load(open('classes.pkl', 'rb'))
lemmatizer = WordNetLemmatizer()

# Function to preprocess user input
def clean_up_sentence(sentence):
    sentence_words = nltk.word_tokenize(sentence)
    sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words]
    return sentence_words

# Function to convert input to bag-of-words format
def bow(sentence, words, show_details=True):
    sentence_words = clean_up_sentence(sentence)
    bag = [0]*len(words)
    for s in sentence_words:
        for i, w in enumerate(words):
            if w == s:
                bag[i] = 1
                if show_details:
                    print(f"found in bag: {w}")
    return np.array(bag)

# Streamlit app
def main():
    st.title("Chatbot App")
    st.write("Welcome to the chatbot! Start a conversation.")

    user_input = st.text_input("You: ")
    if st.button("Send"):
        if user_input.strip() == "":
            st.write("Bot: Please enter a message.")
        else:
            p = bow(user_input, words)
            res = model.predict(np.array([p]))[0]
            ERROR_THRESHOLD = 0.25
            results = [[i, r] for i, r in enumerate(res) if r > ERROR_THRESHOLD]
            results.sort(key=lambda x: x[1], reverse=True)
            return_list = []
            for r in results:
                return_list.append({"intent": classes[r[0]], "probability": str(r[1])})
            for i in intents["intents"]:
                if i["tag"] == return_list[0]["intent"]:
                    response = np.random.choice(i["responses"])
                    break
            st.write("Bot:", response)

if __name__ == "__main__":
    main()