Update app.py

app.py

@@ -1,24 +1,78 @@
 import nltk
-nltk.download('punkt')
-from nltk.stem.lancaster import LancasterStemmer
 import numpy as np
 import tflearn
-import tensorflow
+import tensorflow as tf
 import random
 import json
-import pandas as pd
 import pickle
 import gradio as gr
-from tensorflow.python.util.nest import is_sequence_or_composite
+from nltk.stem.lancaster import LancasterStemmer
 
+# Ensure nltk downloads
+nltk.download('punkt')
 stemmer = LancasterStemmer()
 
-with open("intents.json") as file:
-    data = json.load(file)
+# Load intents and check for errors
+try:
+    with open("intents.json") as file:
+        data = json.load(file)
+except FileNotFoundError:
+    raise FileNotFoundError("The file 'intents.json' was not found.")
+
+# Load or regenerate the data
+try:
+    with open("data.pickle", "rb") as f:
+        words, labels, training, output = pickle.load(f)
+except FileNotFoundError:
+    # Regenerate the data.pickle if not found
+    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"])
+
+    words = [stemmer.stem(w.lower()) for w in words if w not in ["?", "!", ".", ","]]
+    words = sorted(list(set(words)))
+    labels = sorted(labels)
+
+    training = []
+    output = []
+
+    out_empty = [0 for _ in range(len(labels))]
+
+    for x, doc in enumerate(docs_x):
+        bag = []
+
+        wrds = [stemmer.stem(w.lower()) for w in doc]
 
-with open("data.pickle", "rb") as f:
-  words, labels, training, output = pickle.load(f)
+        for w in words:
+            if w in wrds:
+                bag.append(1)
+            else:
+                bag.append(0)
 
+        output_row = out_empty[:]
+        output_row[labels.index(docs_y[x])] = 1
+
+        training.append(bag)
+        output.append(output_row)
+
+    training = np.array(training)
+    output = np.array(output)
+
+    with open("data.pickle", "wb") as f:
+        pickle.dump((words, labels, training, output), f)
+
+# Build the model
 net = tflearn.input_data(shape=[None, len(training[0])])
 net = tflearn.fully_connected(net, 8)
 net = tflearn.fully_connected(net, 8)
@@ -26,47 +80,35 @@ 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 Exception as e:
+    raise FileNotFoundError("Model file 'MentalHealthChatBotmodel.tflearn' could not be loaded.") from e
 
+# Function to convert user input into a bag-of-words representation
 def bag_of_words(s, words):
     bag = [0 for _ in range(len(words))]
     s_words = nltk.word_tokenize(s)
-    print("Tokenized words:", s_words)  # Debugging
     s_words = [stemmer.stem(word.lower()) for word in s_words]
-    print("Stemmed words:", s_words)  # Debugging
 
     for se in s_words:
         for i, w in enumerate(words):
             if w == se:
                 bag[i] = 1
-
-    if not any(bag):  # Check if bag is all zeros
-        print("Warning: No matching words found in vocabulary.")
     return np.array(bag)
 
-
+# Chat function
 def chat(message, history=None):
     history = history or []
     message = message.lower()
 
     try:
-        print("User message:", message)  # Debugging
         bag = bag_of_words(message, words)
-        print("Bag representation:", bag)  # Debugging
         results = model.predict([bag])
-        print("Prediction results:", results)  # Debugging
-
-        if not results.any():  # Handle empty or invalid predictions
-            raise ValueError("Model returned no valid predictions.")
-
         results_index = np.argmax(results)
         tag = labels[results_index]
-        print("Predicted tag:", tag)  # Debugging
-
     except Exception as e:
-        print(f"Error during processing: {e}")  # Log the error
+        print(f"Error during processing: {e}")  # Debugging
         response = "I'm sorry, I couldn't understand your message."
         history.append((message, response))
         return history, history
@@ -82,70 +124,17 @@ def chat(message, history=None):
     history.append((message, response))
     return history, history
 
-chatbot = gr.Chatbot(label="Chat")
+# Gradio Interface
 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;
-}
-
+div[data-testid="user"] {background-color: #253885 !important;}
 """
 demo = gr.Interface(
     fn=chat,
     inputs=[gr.Textbox(lines=1, label="Message"), gr.State([])],
     outputs=[gr.Chatbot(label="Chat"), gr.State()],
     allow_flagging="never",
-    title="Wellbeing for All | Generative AI Enthusiasts",
+    title="Wellbeing Chatbot",
     css=css
 )