Update app.py

app.py

@@ -11,42 +11,40 @@ from nltk.stem.lancaster import LancasterStemmer
 nltk.download('punkt')
 stemmer = LancasterStemmer()
 
-# Load intents
+# Load intents file
 with open("intents.json") as file:
     data = json.load(file)
 
-# Load or regenerate data.pickle
-try:
-    with open("data.pickle", "rb") as f:
-        words, labels, training, output = pickle.load(f)
-except FileNotFoundError:
-    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 = sorted(set(stemmer.stem(w.lower()) for w in words if w not in ["?", ".", ",", "!"]))
-    labels = sorted(labels)
-
-    training, output = [], []
-    out_empty = [0] * len(labels)
-    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)
-    with open("data.pickle", "wb") as f:
-        pickle.dump((words, labels, training, output), f)
-
-# Build the model
+# 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)
+
+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)
@@ -54,22 +52,26 @@ net = tflearn.fully_connected(net, len(output[0]), activation="softmax")
 net = tflearn.regression(net)
 
 model = tflearn.DNN(net)
+
 try:
     model.load("MentalHealthChatBotmodel.tflearn")
 except FileNotFoundError:
     model.fit(training, output, n_epoch=1000, batch_size=8, show_metric=True)
     model.save("MentalHealthChatBotmodel.tflearn")
 
-# Define chat function
+# Function to preprocess user input
 def bag_of_words(s, words):
-    bag = [0] * len(words)
-    s_words = [stemmer.stem(w.lower()) for w in nltk.word_tokenize(s)]
+    bag = [0 for _ in range(len(words))]
+    s_words = nltk.word_tokenize(s)
+    s_words = [stemmer.stem(word.lower()) for word in s_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=None):
     history = history or []
     try:
@@ -77,6 +79,7 @@ def chat(message, history=None):
         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'])
@@ -93,7 +96,8 @@ demo = gr.Interface(
     fn=chat,
     inputs=[gr.Textbox(lines=1, label="Message"), gr.State()],
     outputs=[gr.Chatbot(label="Chat"), gr.State()],
-    allow_flagging="never"
+    allow_flagging="never",
+    title="Wellbeing for All | Generative AI Enthusiasts"
 )
 
 if __name__ == "__main__":