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import nltk
nltk.download('punkt')
from nltk.stem.lancaster import LancasterStemmer
import numpy as np
import tflearn
import tensorflow
import random
import json
import pickle
import gradio as gr
from nltk.tokenize import word_tokenize
# Ensure necessary NLTK resources are downloaded
try:
nltk.data.find('tokenizers/punkt')
except LookupError:
nltk.download('punkt')
# Initialize the stemmer
stemmer = LancasterStemmer()
# Load intents.json
try:
with open("intents.json") as file:
data = json.load(file)
except FileNotFoundError:
raise FileNotFoundError("Error: 'intents.json' file not found. Ensure it exists in the current directory.")
# Load preprocessed data from pickle
try:
with open("data.pickle", "rb") as f:
words, labels, training, output = pickle.load(f)
except FileNotFoundError:
raise FileNotFoundError("Error: 'data.pickle' file not found. Ensure it exists and matches the model.")
# Build the model structure
net = tflearn.input_data(shape=[None, len(training[0])])
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, len(output[0]), activation="softmax")
net = tflearn.regression(net)
# Load the trained model
model = tflearn.DNN(net)
try:
model.load("MentalHealthChatBotmodel.tflearn")
except FileNotFoundError:
print("Error: Trained model file not found. Ensure 'MentalHealthChatBotmodel.tflearn' exists.")
# Function to process user input into a bag-of-words format
def bag_of_words(s, words):
bag = [0 for _ in range(len(words))]
s_words = word_tokenize(s) # Replaced nltk.word_tokenize(s)
s_words = [stemmer.stem(word.lower()) for word in s_words if word.lower() in words]
for se in s_words:
for i, w in enumerate(words):
if w == se:
bag[i] = 1
return np.array(bag)
# Chat function
def chat(message, history):
history = history or []
message = message.lower()
try:
# Predict the tag
results = model.predict([bag_of_words(message, words)])
results_index = np.argmax(results)
tag = labels[results_index]
# Match tag with intent and choose a random response
for tg in data["intents"]:
if tg['tag'] == tag:
responses = tg['responses']
response = random.choice(responses)
break
else:
response = "I'm sorry, I didn't understand that. Could you please rephrase?"
except Exception as e:
response = f"An error occurred: {str(e)}"
history.append((message, response))
return history, history
# Gradio interface
chatbot = gr.Chatbot(label="Chat")
css = """
footer {display:none !important}
.output-markdown{display:none !important}
.gr-button-primary {
z-index: 14;
height: 43px;
width: 130px;
left: 0px;
top: 0px;
padding: 0px;
cursor: pointer !important;
background: none rgb(17, 20, 45) !important;
border: none !important;
text-align: center !important;
font-family: Poppins !important;
font-size: 14px !important;
font-weight: 500 !important;
color: rgb(255, 255, 255) !important;
line-height: 1 !important;
border-radius: 12px !important;
transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
box-shadow: none !important;
}
.gr-button-primary:hover{
z-index: 14;
height: 43px;
width: 130px;
left: 0px;
top: 0px;
padding: 0px;
cursor: pointer !important;
background: none rgb(37, 56, 133) !important;
border: none !important;
text-align: center !important;
font-family: Poppins !important;
font-size: 14px !important;
font-weight: 500 !important;
color: rgb(255, 255, 255) !important;
line-height: 1 !important;
border-radius: 12px !important;
transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
box-shadow: rgb(0 0 0 / 23%) 0px 1px 7px 0px !important;
}
.hover\:bg-orange-50:hover {
--tw-bg-opacity: 1 !important;
background-color: rgb(229,225,255) !important;
}
div[data-testid="user"] {
background-color: #253885 !important;
}
.h-\[40vh\]{
height: 70vh !important;
}
"""
demo = gr.Interface(
chat,
[gr.Textbox(lines=1, label="Message"), "state"],
[chatbot, "state"],
allow_flagging="never",
title="Mental Health Bot | Data Science Dojo",
css=css
)
# Launch Gradio interface
if __name__ == "__main__":
demo.launch()
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