Update app.py

app.py

@@ -17,20 +17,20 @@ import torch
 os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
-# Download NLTK resources
+# Download necessary NLTK resources
 nltk.download("punkt")
 
 # Initialize Lancaster Stemmer
 stemmer = LancasterStemmer()
 
-# Load chatbot intents and training data
+# Load chatbot training data and intents
 with open("intents.json") as file:
     intents_data = json.load(file)
 
 with open("data.pickle", "rb") as f:
     words, labels, training, output = pickle.load(f)
 
-# Build Chatbot Model
+# Build the chatbot's neural network model
 net = tflearn.input_data(shape=[None, len(training[0])])
 net = tflearn.fully_connected(net, 8)
 net = tflearn.fully_connected(net, 8)
@@ -39,18 +39,17 @@ net = tflearn.regression(net)
 chatbot_model = tflearn.DNN(net)
 chatbot_model.load("MentalHealthChatBotmodel.tflearn")
 
-# Model for sentiment detection
+# Hugging Face models for sentiment and emotion detection
 tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 
-# Model for emotion detection
 tokenizer_emotion = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
 model_emotion = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
 
-# Google Maps API client
+# Google Maps API Client
 gmaps = googlemaps.Client(key=os.getenv('GOOGLE_API_KEY'))
 
-# Chatbot logic
+# Function to process text input into a bag-of-words format
 def bag_of_words(s, words):
     bag = [0] * len(words)
     s_words = word_tokenize(s)
@@ -61,23 +60,24 @@ def bag_of_words(s, words):
                 bag[i] = 1
     return np.array(bag)
 
+# Chatbot Logic
 def chatbot(message, history):
     """Generate chatbot response and append to history."""
     history = history or []
     try:
-        results = chatbot_model.predict([bag_of_words(message, words)])
-        tag = labels[np.argmax(results)]
+        result = chatbot_model.predict([bag_of_words(message, words)])
+        tag = labels[np.argmax(result)]
         response = "I'm not sure how to respond to that. 🤔"
         for intent in intents_data["intents"]:
             if intent["tag"] == tag:
                 response = random.choice(intent["responses"])
                 break
     except Exception as e:
-        response = f"Error: {str(e)} 💥"
+        response = f"Error: {str(e)}"
     history.append((message, response))
     return history, response
 
-# Sentiment analysis
+# Sentiment Analysis
 def analyze_sentiment(user_input):
     inputs = tokenizer_sentiment(user_input, return_tensors="pt")
     with torch.no_grad():
@@ -86,33 +86,50 @@ def analyze_sentiment(user_input):
     sentiment_map = ["Negative 😔", "Neutral 😐", "Positive 😊"]
     return sentiment_map[sentiment_class]
 
-# Emotion detection
+# Emotion Detection
 def detect_emotion(user_input):
     pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion)
     result = pipe(user_input)
-    emotion = result[0]["label"]
+    emotion = result[0]['label']
     return emotion
 
 # Generate Suggestions
 def generate_suggestions(emotion):
+    """Return suggestions aligned with the detected emotion."""
     suggestions = {
         "joy": [
             ["Relaxation Techniques", '<a href="https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation" target="_blank">Visit</a>'],
             ["Dealing with Stress", '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Visit</a>'],
             ["Emotional Wellness Toolkit", '<a href="https://www.nih.gov/health-information/emotional-wellness-toolkit" target="_blank">Visit</a>'],
-            ["Relaxation Video", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>'],
+            ["Relaxation Videos", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>']
         ],
         "anger": [
             ["Emotional Wellness Toolkit", '<a href="https://www.nih.gov/health-information/emotional-wellness-toolkit" target="_blank">Visit</a>'],
-            ["Stress Management Tips", '<a href="https://www.health.harvard.edu/health-a-to-z" target="_blank">Visit</a>'],
-            ["Dealing with Anger", '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Visit</a>'],
-            ["Relaxation Video", '<a href="https://youtu.be/MIc299Flibs" target="_blank">Watch</a>'],
+            ["Stress Management Tips", '<a href="https://www.health.harvard.edu" target="_blank">Visit</a>'],
+            ["Dealing with Anger", '<a href="https://www.helpguide.org/mental-health/anger-management" target="_blank">Visit</a>'],
+            ["Relaxation Videos", '<a href="https://youtu.be/MIc299Flibs" target="_blank">Watch</a>']
+        ],
+        "fear": [
+            ["Mindfulness Practices", '<a href="https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation" target="_blank">Visit</a>'],
+            ["Coping with Anxiety", '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Visit</a>'],
+            ["Relaxation Videos", '<a href="https://youtu.be/yGKKz185M5o" target="_blank">Watch</a>']
+        ],
+        "sadness": [
+            ["Emotional Wellness Toolkit", '<a href="https://www.nih.gov/health-information/emotional-wellness-toolkit" target="_blank">Visit</a>'],
+            ["Dealing with Anxiety", '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Visit</a>'],
+            ["Relaxation Videos", '<a href="https://youtu.be/-e-4Kx5px_I" target="_blank">Watch</a>']
+        ],
+        "surprise": [
+            ["Managing Stress", '<a href="https://www.health.harvard.edu" target="_blank">Visit</a>'],
+            ["Coping Strategies", '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Visit</a>'],
+            ["Relaxation Videos", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>']
         ],
     }
-    return suggestions.get(emotion, [["No suggestions available", ""]])
+    return suggestions.get(emotion.lower(), [["No suggestions available", ""]])
 
-# Get Nearby Professionals and Generate Map
+# Get Health Professionals and Generate Map
 def get_health_professionals_and_map(location, query):
+    """Search professionals and return details + map as HTML."""
     try:
         geo_location = gmaps.geocode(location)
         if geo_location:
@@ -130,112 +147,64 @@ def get_health_professionals_and_map(location, query):
     except Exception as e:
         return [f"Error: {e}"], ""
 
-# App Main Function
-def app_function(message, location, query, history):
-    chatbot_history, _ = chatbot(message, history)
-    sentiment = analyze_sentiment(message)
-    emotion = detect_emotion(message.lower())
+# Main Application Logic
+def app_function(user_input, location, query, history):
+    chatbot_history, response = chatbot(user_input, history)
+    emotion = detect_emotion(user_input)
     suggestions = generate_suggestions(emotion)
     professionals, map_html = get_health_professionals_and_map(location, query)
-    return chatbot_history, sentiment, emotion, suggestions, professionals, map_html
+    return chatbot_history, emotion, suggestions, professionals, map_html
 
-# Enhanced CSS for Custom Title and Styling
+# Enhanced CSS for Custom UI
 custom_css = """
-@import url('https://fonts.googleapis.com/css2?family=Roboto:wght@400;700&display=swap');
 body {
     background: linear-gradient(135deg, #000000, #ff5722);
     color: white;
     font-family: 'Roboto', sans-serif;
 }
-button {
-    background-color: #ff5722 !important;
-    border: none !important;
-    color: white !important;
-    padding: 12px 20px;
-    font-size: 16px;
-    border-radius: 8px;
-    cursor: pointer;
-}
-button:hover {
-    background-color: #e64a19 !important;
-}
 textarea, input[type="text"], .gr-chatbot {
     background: #000000 !important;
     color: white !important;
     border: 2px solid #ff5722 !important;
+    border-radius: 5px;
     padding: 12px !important;
-    border-radius: 8px !important;
-    font-size: 14px;
 }
-.gr-dataframe, .gr-textbox {
+.gr-dataframe {
     background: #000000 !important;
     color: white !important;
+    height: 350px !important;
     border: 2px solid #ff5722 !important;
-    border-radius: 8px !important;
-    font-size: 14px;
-}
-.suggestions-title {
-    font-size: 1.5rem !important;
-    font-weight: bold;
-    color: white;
-    margin-top: 20px;
+    overflow-y: auto;
 }
-h1 {
-    font-size: 4rem;
-    font-weight: bold;
-    margin-bottom: 10px;
+h1, h2, h3 {
     color: white;
     text-align: center;
-    text-shadow: 2px 2px 8px rgba(0, 0, 0, 0.6);
-}
-h2 {
-    font-weight: 400;
-    font-size: 1.8rem;
-    color: white;
-    text-shadow: 2px 2px 5px rgba(0, 0, 0, 0.4);
-}
-.input-title, .output-title {
-    font-size: 1.5rem;
     font-weight: bold;
-    color: black;
-    margin-bottom: 10px;
 }
 """
 
-# Gradio Interface
+# Gradio Application
 with gr.Blocks(css=custom_css) as app:
-    gr.HTML("<h1>🌟 Well-Being Companion</h1>")
-    gr.HTML("<h2>Empowering Your Well-Being Journey 💚</h2>")
+    gr.Markdown("<h1>🌟 Well-Being Companion</h1>")
+    gr.Markdown("<h2>Empowering Your Well-Being Journey 💚</h2>")
 
     with gr.Row():
-        gr.Markdown("<div class='input-title'>Your Message</div>")
-        user_message = gr.Textbox(label=None, placeholder="Enter your message...")
-        gr.Markdown("<div class='input-title'>Your Location</div>")
-        user_location = gr.Textbox(label=None, placeholder="Enter your location...")
-        gr.Markdown("<div class='input-title'>Your Query</div>")
-        search_query = gr.Textbox(label=None, placeholder="Search for professionals...")
-
-    chatbot_box = gr.Chatbot(label="Chat History")
-    gr.Markdown("<div class='output-title'>Detected Emotion</div>")
-    emotion_output = gr.Textbox(label=None)
-    gr.Markdown("<div class='output-title'>Detected Sentiment</div>")
-    sentiment_output = gr.Textbox(label=None)
-    gr.Markdown("<div class='suggestions-title'>Suggestions</div>")
-    suggestions_output = gr.DataFrame(headers=["Title", "Links"], label=None)
-
-    gr.Markdown("<h2 class='suggestions-title'>Health Professionals Nearby</h2>")
-    map_output = gr.HTML(label=None)
-    professional_display = gr.Textbox(label=None, lines=5)
-
-    submit_btn = gr.Button("Submit")
-
-    submit_btn.click(
+        user_input = gr.Textbox(label="Your Message", placeholder="Enter your message...")
+        location = gr.Textbox(label="Your Location", placeholder="Enter your location...")
+        query = gr.Textbox(label="Query (e.g., therapists)", placeholder="Search...")
+
+    chatbot_history = gr.Chatbot(label="Chat History")
+    emotion_box = gr.Textbox(label="Detected Emotion")
+    suggestions_table = gr.DataFrame(headers=["Suggestion", "Link"])
+    map_box = gr.HTML(label="Map of Health Professionals")
+    professionals_list = gr.Textbox(label="Health Professionals Nearby", lines=5)
+
+    submit_button = gr.Button("Submit")
+
+    submit_button.click(
         app_function,
-        inputs=[user_message, user_location, search_query, chatbot_box],
-        outputs=[
-            chatbot_box, sentiment_output, emotion_output,
-            suggestions_output, professional_display, map_output,
-        ],
+        inputs=[user_input, location, query, chatbot_history],
+        outputs=[chatbot_history, emotion_box, suggestions_table, professionals_list, map_box],
     )
 
 app.launch()