Update app.py

app.py

@@ -1,24 +1,91 @@
 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)
+# 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("data.pickle", "rb") as f:
-  words, labels, training, output = pickle.load(f)
+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.")
 
+# Build the model
 net = tflearn.input_data(shape=[None, len(training[0])])
 net = tflearn.fully_connected(net, 8)
 net = tflearn.fully_connected(net, 8)
@@ -26,13 +93,17 @@ 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
 
 
 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]
 
@@ -40,89 +111,46 @@ 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()
-    results = model.predict([bag_of_words(message, words)])
-    results_index = np.argmax(results)
-    tag = labels[results_index]
+
+    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
 
     for tg in data["intents"]:
-      if tg['tag'] == tag:
-        responses = tg['responses']
+        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."
 
-        # print(random.choice(responses))
-        response = random.choice(responses)
-  
     history.append((message, response))
     return history, history
 
-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;
-}
-"""
+
+# Initialize Gradio interface
 demo = gr.Interface(
-    chat,
-    [gr.Textbox(lines=1, label="Message"), "state"],
-    [chatbot, "state"],
+    fn=chat,
+    inputs=[gr.Textbox(lines=1, label="Message"), gr.State()],
+    outputs=[gr.Chatbot(label="Chat"), gr.State()],
     allow_flagging="never",
-    title="Mental Health Bot | Data Science Dojo",
+    title="Wellbeing for All | Generative AI Enthusiasts",
     css=css
 )
+
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()