Create app.py

app.py

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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()
+