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#!/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()
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