Update app.py

app.py

@@ -1,91 +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
+from tensorflow.python.util.nest import is_sequence_or_composite
 
-# Ensure nltk downloads
-nltk.download('punkt')
 stemmer = LancasterStemmer()
 
-# URL for the background image
-flower_image_url = "https://i.postimg.cc/hG2FG85D/2.png"
-
-# Combined CSS for styling and blurred background
-css = f"""
-body {{
-    margin: 0;
-    padding: 0;
-    overflow: hidden;
-}}
-.gradio-container {{
-    position: relative;
-    z-index: 1; /* Ensure UI elements are above the background */
-}}
-/* Blurred background image */
-.blurred-background {{
-    position: fixed;
-    top: 0;
-    left: 0;
-    width: 100%;
-    height: 100%;
-    z-index: -1; /* Send background image behind all UI elements */
-    background-image: url("{flower_image_url}");
-    background-size: cover;
-    background-position: center;
-    filter: blur(10px); /* Adjust blur ratio here */
-    opacity: 0.8; /* Optional: Add slight transparency for a subtle effect */
-}}
-footer {{
-    display: none !important;
-}}
-div[data-testid="user"] {{
-    background-color: #253885 !important;
-}}
-.h-\[40vh\] {{
-    height: 70vh !important;
-}}
-.gr-button-primary {{
-    z-index: 14;
-    height: 43px;
-    width: 130px;
-    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;
-    border-radius: 12px !important;
-    transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
-}}
-.gr-button-primary:hover {{
-    background: none rgb(37, 56, 133) !important;
-}}
-"""
-
-#"""""""""""""""""""""""""   Application Code Starts Here   """""""""""""""""""""""""""""""""""""""""""""
-
-# Load data and handle errors
-try:
-    with open("intents.json") as file:
-        data = json.load(file)
-except FileNotFoundError:
-    raise FileNotFoundError("The file 'intents.json' was not found.")
+with open("intents.json") as file:
+    data = json.load(file)
 
-try:
-    with open("data.pickle", "rb") as f:
-        words, labels, training, output = pickle.load(f)
-except FileNotFoundError:
-    raise FileNotFoundError("The file 'data.pickle' was not found.")
+with open("data.pickle", "rb") as f:
+  words, labels, training, output = pickle.load(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)
@@ -93,17 +26,13 @@ net = tflearn.fully_connected(net, len(output[0]), activation="softmax")
 net = tflearn.regression(net)
 
 model = tflearn.DNN(net)
-try:
-    model.load("MentalHealthChatBotmodel.tflearn")
-except Exception as e:
-    raise FileNotFoundError("Model file 'MentalHealthChatBotmodel.tflearn' could not be loaded.") from e
+model.load("MentalHealthChatBotmodel.tflearn")
+# print('model loaded successfully')
 
 
 def bag_of_words(s, words):
-    """
-    Convert a user input sentence into a bag-of-words representation.
-    """
     bag = [0 for _ in range(len(words))]
+
     s_words = nltk.word_tokenize(s)
     s_words = [stemmer.stem(word.lower()) for word in s_words]
 
@@ -111,46 +40,89 @@ def bag_of_words(s, words):
         for i, w in enumerate(words):
             if w == se:
                 bag[i] = 1
+            
     return np.array(bag)
 
 
 def chat(message, history):
-    """
-    Handle chat interaction.
-    """
     history = history or []
     message = message.lower()
-
-    try:
-        results = model.predict([bag_of_words(message, words)])
-        results_index = np.argmax(results)
-        tag = labels[results_index]
-    except Exception as e:
-        response = "I'm sorry, I couldn't understand your message."
-        history.append((message, response))
-        return history, history
+    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']
-            response = random.choice(responses)
-            break
-    else:
-        response = "I'm sorry, I don't have a response for that."
+      if tg['tag'] == tag:
+        responses = tg['responses']
 
+        # print(random.choice(responses))
+        response = random.choice(responses)
+  
     history.append((message, response))
     return history, history
 
-
-# Initialize 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()