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import streamlit as st
import nltk
import pickle
import numpy as np
from tensorflow.keras.models import load_model
from nltk.stem import WordNetLemmatizer
# Load the pre-trained model and other data
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("Healthcare Chatbot")
st.write("Welcome to the Healthcare Chatbot! Enter your symptoms below.")
user_input = st.text_input("You:")
if st.button("Predict"):
if user_input.strip() == "":
st.write("Bot: Please enter your symptoms.")
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)
for r in results:
return_class = classes[r[0]]
break
st.write("Bot: Based on your symptoms, you might have:", return_class)
if __name__ == "__main__":
main()
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