Update app.py

app.py

@@ -17,15 +17,14 @@ import torch
 os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 
-# Download necessary NLTK resources
+# Ensure necessary NLTK resources
 nltk.download("punkt")
 stemmer = LancasterStemmer()
 
-# Load intents for chatbot
+# Load chatbot intents and training data
 with open("intents.json") as file:
     intents_data = json.load(file)
 
-# Load training data for chatbot
 with open("data.pickle", "rb") as f:
     words, labels, training, output = pickle.load(f)
 
@@ -38,18 +37,18 @@ net = tflearn.regression(net)
 chatbot_model = tflearn.DNN(net)
 chatbot_model.load("MentalHealthChatBotmodel.tflearn")
 
-# Hugging Face models for emotion and sentiment detection
+# Hugging Face emotion and sentiment detection models
 tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 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
+# Initialize Google Maps API client
 gmaps = googlemaps.Client(key=os.getenv("GOOGLE_API_KEY"))
 
-# Function: Bag of Words for Chatbot
+# Helper Functions
 def bag_of_words(s, words):
-    """Convert user input to bag-of-words representation."""
+    """Convert user input into bag-of-words vector."""
     bag = [0] * len(words)
     s_words = word_tokenize(s)
     s_words = [stemmer.stem(word.lower()) for word in s_words if word.isalnum()]
@@ -59,26 +58,26 @@ def bag_of_words(s, words):
                 bag[i] = 1
     return np.array(bag)
 
-# Chatbot Response
+# Chatbot response logic
 def chatbot(message, history):
-    """Generate chatbot response and append to chat history."""
+    """Generate chatbot response and update chat history."""
     history = history or []
     try:
         result = chatbot_model.predict([bag_of_words(message, words)])
         tag = labels[np.argmax(result)]
-        response = "I'm not sure how to respond to that. 🤔"
+        response = "I'm sorry, I'm not sure how to respond. 🤔"
         for intent in intents_data["intents"]:
             if intent["tag"] == tag:
                 response = random.choice(intent["responses"])
                 break
     except Exception as e:
         response = f"Error: {e}"
-    history.append((message, response))  # Append to history as a tuple
+    history.append((message, response))  # Append to the chatbot history
     return history, response
 
-# Sentiment Detection
+# Sentiment detection function
 def analyze_sentiment(user_input):
-    """Analyze sentiment using Hugging Face sentiment model."""
+    """Analyze sentiment and return emoji-mapped sentiment."""
     inputs = tokenizer_sentiment(user_input, return_tensors="pt")
     with torch.no_grad():
         outputs = model_sentiment(**inputs)
@@ -86,66 +85,67 @@ def analyze_sentiment(user_input):
     sentiment_map = ["Negative 😔", "Neutral 😐", "Positive 😊"]
     return sentiment_map[sentiment_class]
 
-# Emotion Detection
+# Emotion detection function
 def detect_emotion(user_input):
-    """Detect user emotion using Hugging Face emotion model."""
+    """Detect emotion from user input using Hugging Face emotion model."""
     pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion)
     result = pipe(user_input)
-    emotion = result[0]["label"].lower().strip()  # Normalize and clean label
+    emotion = result[0]["label"].lower().strip()
     emotion_map = {
         "joy": "😊 Joy",
         "anger": "😠 Anger",
         "sadness": "😢 Sadness",
         "fear": "😨 Fear",
         "surprise": "😲 Surprise",
-        "neutral": "😐 Neutral"
+        "neutral": "😐 Neutral",
     }
     return emotion_map.get(emotion, "Unknown 🤔")
 
-# Suggestion Generation
+# Generate suggestions based on emotion
 def generate_suggestions(emotion):
-    """Provide resources based on detected emotion."""
-    emotion_key = emotion.lower()  # Match key with normalized emotion
+    """Generate resources and videos to help based on the emotion detected."""
+    emotion_key = emotion.lower()
     suggestions = {
         "joy": [
             ["Relaxation Techniques", '<a href="https://www.helpguide.org/mental-health/meditation" target="_blank">Visit</a>'],
-            ["Stress Management", '<a href="https://www.helpguide.org/mental-health/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>']
+            ["Emotional Toolkit", '<a href="https://www.nih.gov/health-information/emotional-wellness-toolkit" target="_blank">Visit</a>'],
+            ["Stress Management", '<a href="https://www.health.harvard.edu" target="_blank">Visit</a>'],
         ],
         "anger": [
-            ["Stress Management Tips", '<a href="https://www.health.harvard.edu" target="_blank">Visit</a>'],
-            ["Relaxation Video", '<a href="https://youtu.be/MIc299Flibs" target="_blank">Watch</a>']
+            ["Calming Techniques", '<a href="https://youtu.be/MIc299Flibs" target="_blank">Watch</a>'],
+            ["Manage Anger", '<a href="https://www.helpguide.org/mental-health/anger-management.htm" target="_blank">Visit</a>'],
         ],
         "fear": [
             ["Coping with Anxiety", '<a href="https://www.helpguide.org/mental-health/anxiety" target="_blank">Visit</a>'],
-            ["Mindfulness Practices", '<a href="https://youtu.be/yGKKz185M5o" target="_blank">Watch</a>']
+            ["Mindfulness Meditation", '<a href="https://youtu.be/yGKKz185M5o" target="_blank">Watch</a>'],
         ],
         "sadness": [
             ["Overcoming Sadness", '<a href="https://youtu.be/-e-4Kx5px_I" target="_blank">Watch</a>'],
         ],
         "surprise": [
             ["Managing Surprises", '<a href="https://www.health.harvard.edu" target="_blank">Visit</a>'],
-            ["Calm Relaxation", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>']
+            ["Relaxation Video", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>'],
         ],
         "neutral": [
-            ["General Well-Being Tips", '<a href="https://www.psychologytoday.com" target="_blank">Visit</a>']
+            ["General Tips", '<a href="https://www.psychologytoday.com" target="_blank">Read More</a>']
         ],
     }
-    return suggestions.get(emotion_key, [["No suggestions available.", ""]])
+    return suggestions.get(emotion_key, [["No specific suggestions available.", ""]])
 
-# Google Maps Integration
+# Google Maps integration
 def get_health_professionals_and_map(location, query):
-    """Search nearby professionals and display on interactive map."""
+    """Search nearby health professionals and generate map."""
     try:
         if not location or not query:
-            return ["Please provide both location and search query."], ""
+            return ["Please provide a valid location and query."], ""
+
         geo_location = gmaps.geocode(location)
         if geo_location:
             lat, lng = geo_location[0]["geometry"]["location"].values()
             places_result = gmaps.places_nearby(location=(lat, lng), radius=10000, keyword=query)["results"]
-            map_ = folium.Map(location=(lat, lng), zoom_start=13)
+
             professionals = []
+            map_ = folium.Map(location=(lat, lng), zoom_start=13)
             for place in places_result:
                 professionals.append(f"{place['name']} - {place.get('vicinity', 'No address available')}")
                 folium.Marker(
@@ -153,17 +153,18 @@ def get_health_professionals_and_map(location, query):
                     popup=f"{place['name']}"
                 ).add_to(map_)
             return professionals, map_._repr_html_()
-        return ["No professionals found for the given location."], ""
+
+        return ["No professionals found."], ""
     except Exception as e:
-        return [f"An error occurred: {str(e)}"], ""
-
-# Main Application Logic
-def app_function(user_input, location, query, history):
-    chatbot_history, _ = chatbot(user_input, history)
-    sentiment = analyze_sentiment(user_input)
-    emotion = detect_emotion(user_input)
-    suggestions = generate_suggestions(emotion)
-    professionals, map_html = get_health_professionals_and_map(location, query)
+        return [f"Error: {e}"], ""
+
+# Main application logic
+def app_function(user_message, location, query, history):
+    chatbot_history, _ = chatbot(user_message, history)
+    sentiment = analyze_sentiment(user_message)  # Sentiment detection
+    emotion = detect_emotion(user_message)  # Emotion detection
+    suggestions = generate_suggestions(emotion)  # Get emotion-based suggestions
+    professionals, map_html = get_health_professionals_and_map(location, query)  # Google Maps integration
     return chatbot_history, sentiment, emotion, suggestions, professionals, map_html
 
 # Custom CSS
@@ -174,27 +175,27 @@ body {
     color: white;
 }
 h1 {
-    font-size: 4rem;
+    font-size: 4.5rem;
     font-weight: bold;
     text-align: center;
-    margin-bottom: 20px;
+    color: white;
+    text-shadow: 2px 2px 8px rgba(0, 0, 0, 0.4);
 }
 h2 {
     font-size: 2rem;
-    font-weight: lighter;
     text-align: center;
-    margin-bottom: 30px;
+    font-weight: lighter;
     color: white;
+    margin-bottom: 30px;
 }
-button {
+.button {
     background: linear-gradient(45deg, #ff5722, #ff9800) !important;
-    border: none;
-    border-radius: 8px;
+    border: none !important;
     padding: 12px 20px;
+    border-radius: 8px;
+    color: white !important;
     cursor: pointer;
-    color: white;
     font-size: 16px;
-    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
 }
 """
 
@@ -204,9 +205,9 @@ with gr.Blocks(css=custom_css) as app:
     gr.HTML("<h2>Empowering Your Mental Health Journey 💚</h2>")
 
     with gr.Row():
-        user_message = gr.Textbox(label="Your Message", placeholder="Type your message...")
+        user_message = gr.Textbox(label="Your Message", placeholder="Enter your message...")
         location = gr.Textbox(label="Your Location", placeholder="Enter location...")
-        query = gr.Textbox(label="Query (e.g., therapist, doctor)", placeholder="Search for professionals...")
+        query = gr.Textbox(label="Search Query", placeholder="e.g., therapist")
 
     chatbot_history = gr.Chatbot(label="Chat History")
     sentiment_output = gr.Textbox(label="Detected Sentiment")
@@ -219,7 +220,7 @@ with gr.Blocks(css=custom_css) as app:
     submit_button.click(
         app_function,
         inputs=[user_message, location, query, chatbot_history],
-        outputs=[chatbot_history, sentiment_output, emotion_output, suggestions_output, professionals_output, map_output]
+        outputs=[chatbot_history, sentiment_output, emotion_output, suggestions_output, professionals_output, map_output],
     )
 
 app.launch()