Update app.py

app.py

@@ -1,52 +1,24 @@
 import nltk
+nltk.download('punkt')
+from nltk.stem.lancaster import LancasterStemmer
 import numpy as np
 import tflearn
-import tensorflow as tf
+import tensorflow
 import random
 import json
+import pandas as pd
+import pickle
 import gradio as gr
-from nltk.stem.lancaster import LancasterStemmer
-
-# Ensure required NLTK resources are downloaded
-nltk.download('punkt')
-nltk.download('punkt_tab')
+from tensorflow.python.util.nest import is_sequence_or_composite
 
 stemmer = LancasterStemmer()
 
-# Load intents file
 with open("intents.json") as file:
     data = json.load(file)
 
-# Data preprocessing
-words, labels, docs_x, docs_y = [], [], [], []
-for intent in data["intents"]:
-    for pattern in intent["patterns"]:
-        wrds = nltk.word_tokenize(pattern)
-        words.extend(wrds)
-        docs_x.append(wrds)
-        docs_y.append(intent["tag"])
-    if intent["tag"] not in labels:
-        labels.append(intent["tag"])
-
-# Stem and sort words
-words = sorted(set(stemmer.stem(w.lower()) for w in words if w not in ["?", ".", ",", "!"]))
-labels = sorted(labels)
-
-# Create training data
-training, output = [], []
-out_empty = [0] * len(labels)
+with open("data.pickle", "rb") as f:
+  words, labels, training, output = pickle.load(f)
 
-for x, doc in enumerate(docs_x):
-    bag = [1 if stemmer.stem(w.lower()) in [stemmer.stem(word) for word in doc] else 0 for w in words]
-    output_row = out_empty[:]
-    output_row[labels.index(docs_y[x])] = 1
-    training.append(bag)
-    output.append(output_row)
-
-training, output = np.array(training), np.array(output)
-
-# Build and train the model
-tf.compat.v1.reset_default_graph()
 net = tflearn.input_data(shape=[None, len(training[0])])
 net = tflearn.fully_connected(net, 8)
 net = tflearn.fully_connected(net, 8)
@@ -54,16 +26,13 @@ net = tflearn.fully_connected(net, len(output[0]), activation="softmax")
 net = tflearn.regression(net)
 
 model = tflearn.DNN(net)
+model.load("MentalHealthChatBotmodel.tflearn")
+# print('model loaded successfully')
 
-try:
-    model.load("MentalHealthChatBotmodel.tflearn")
-except FileNotFoundError:
-    model.fit(training, output, n_epoch=1000, batch_size=8, show_metric=True)
-    model.save("MentalHealthChatBotmodel.tflearn")
 
-# Function to preprocess user input
 def bag_of_words(s, words):
     bag = [0 for _ in range(len(words))]
+
     s_words = nltk.word_tokenize(s)
     s_words = [stemmer.stem(word.lower()) for word in s_words]
 
@@ -71,36 +40,89 @@ def bag_of_words(s, words):
         for i, w in enumerate(words):
             if w == se:
                 bag[i] = 1
+            
     return np.array(bag)
 
-# Chat function
-def chat(message, history=None):
-    history = history or []
-    try:
-        bag = bag_of_words(message, words)
-        results = model.predict([bag])
-        results_index = np.argmax(results)
-        tag = labels[results_index]
 
-        for tg in data["intents"]:
-            if tg['tag'] == tag:
-                response = random.choice(tg['responses'])
-                break
-        else:
-            response = "I'm sorry, I don't have a response for that."
-    except Exception as e:
-        response = f"An error occurred: {str(e)}"
+def chat(message, history):
+    history = history or []
+    message = message.lower()
+    results = model.predict([bag_of_words(message, words)])
+    results_index = np.argmax(results)
+    tag = labels[results_index]
+
+    for tg in data["intents"]:
+      if tg['tag'] == tag:
+        responses = tg['responses']
+
+        # print(random.choice(responses))
+        response = random.choice(responses)
+  
     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(
-    fn=chat,
-    inputs=[gr.Textbox(lines=1, label="Message"), gr.State()],
-    outputs=[gr.Chatbot(label="Chat"), gr.State()],
+    chat,
+    [gr.Textbox(lines=1, label="Message"), "state"],
+    [chatbot, "state"],
     allow_flagging="never",
-    title="Wellbeing for All | Generative AI Enthusiasts"
+    title="Mental Health Bot | Data Science Dojo",
+    css=css
 )
-
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()