Update app.py

app.py

@@ -21,14 +21,15 @@ os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 nltk.download("punkt")
 stemmer = LancasterStemmer()
 
-# Load chatbot training data
+# Load intents for chatbot
 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)
 
-# Build the Chatbot Model
+# Build chatbot model
 net = tflearn.input_data(shape=[None, len(training[0])])
 net = tflearn.fully_connected(net, 8)
 net = tflearn.fully_connected(net, 8)
@@ -37,19 +38,18 @@ net = tflearn.regression(net)
 chatbot_model = tflearn.DNN(net)
 chatbot_model.load("MentalHealthChatBotmodel.tflearn")
 
-# Sentiment and Emotion Detection Models
+# Hugging Face models for emotion and sentiment detection
 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
 gmaps = googlemaps.Client(key=os.getenv("GOOGLE_API_KEY"))
 
-# Helper Functions
+# Function: Bag of Words for Chatbot
 def bag_of_words(s, words):
-    """Convert user input to bag-of-words vector."""
+    """Convert user input to bag-of-words representation."""
     bag = [0] * len(words)
     s_words = word_tokenize(s)
     s_words = [stemmer.stem(word.lower()) for word in s_words if word.isalnum()]
@@ -59,6 +59,7 @@ def bag_of_words(s, words):
                 bag[i] = 1
     return np.array(bag)
 
+# Chatbot Response
 def chatbot(message, history):
     """Generate chatbot response and append to chat history."""
     history = history or []
@@ -72,11 +73,12 @@ def chatbot(message, history):
                 break
     except Exception as e:
         response = f"Error: {e}"
-    history.append((message, response))
+    history.append((message, response))  # Append to history as a tuple
     return history, response
 
+# Sentiment Detection
 def analyze_sentiment(user_input):
-    """Analyze sentiment from user input."""
+    """Analyze sentiment using Hugging Face sentiment model."""
     inputs = tokenizer_sentiment(user_input, return_tensors="pt")
     with torch.no_grad():
         outputs = model_sentiment(**inputs)
@@ -84,61 +86,66 @@ def analyze_sentiment(user_input):
     sentiment_map = ["Negative 😔", "Neutral 😐", "Positive 😊"]
     return sentiment_map[sentiment_class]
 
+# Emotion Detection
 def detect_emotion(user_input):
-    """Detect user emotion with emoji representation."""
+    """Detect user emotion using Hugging Face emotion model."""
     pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion)
     result = pipe(user_input)
-    emotion = result[0]["label"].lower()
+    emotion = result[0]["label"].lower().strip()  # Normalize and clean label
     emotion_map = {
         "joy": "😊 Joy",
         "anger": "😠 Anger",
         "sadness": "😢 Sadness",
         "fear": "😨 Fear",
         "surprise": "😲 Surprise",
-        "neutral": "😐 Neutral",
+        "neutral": "😐 Neutral"
     }
     return emotion_map.get(emotion, "Unknown 🤔")
 
+# Suggestion Generation
 def generate_suggestions(emotion):
-    """Provide suggestions for the detected emotion."""
+    """Provide resources based on detected emotion."""
+    emotion_key = emotion.lower()  # Match key with normalized emotion
     suggestions = {
         "joy": [
             ["Relaxation Techniques", '<a href="https://www.helpguide.org/mental-health/meditation" target="_blank">Visit</a>'],
-            ["Dealing with Stress", '<a href="https://www.helpguide.org/mental-health/anxiety" 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>'],
+            ["Relaxation Video", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</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>'],
+            ["Relaxation Video", '<a href="https://youtu.be/MIc299Flibs" target="_blank">Watch</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 Practices", '<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>'],
+            ["Calm Relaxation", '<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>']
         ],
     }
-    return suggestions.get(emotion.lower(), [["No suggestions are available.", ""]])
+    return suggestions.get(emotion_key, [["No suggestions available.", ""]])
 
+# Google Maps Integration
 def get_health_professionals_and_map(location, query):
-    """Search for nearby healthcare professionals and generate a map."""
+    """Search nearby professionals and display on interactive map."""
     try:
         if not location or not query:
-            return ["Please provide a valid location and query."], ""
-
+            return ["Please provide both location and search 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"]
-
-            professionals = []
             map_ = folium.Map(location=(lat, lng), zoom_start=13)
+            professionals = []
             for place in places_result:
                 professionals.append(f"{place['name']} - {place.get('vicinity', 'No address available')}")
                 folium.Marker(
@@ -146,24 +153,23 @@ 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."], ""
     except Exception as e:
         return [f"An error occurred: {str(e)}"], ""
 
-# Application Logic
-def app_function(user_message, location, query, history):
-    chatbot_history, _ = chatbot(user_message, history)
-    sentiment = analyze_sentiment(user_message)
-    emotion = detect_emotion(user_message)
+# 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 chatbot_history, sentiment, emotion, suggestions, professionals, map_html
 
-# CSS Styling for Centered and Bigger Titles
+# Custom CSS
 custom_css = """
 body {
-    background: linear-gradient(135deg, #000000, #ff5722);
+    background: linear-gradient(135deg, #000, #ff5722);
     font-family: 'Roboto', sans-serif;
     color: white;
 }
@@ -171,57 +177,49 @@ h1 {
     font-size: 4rem;
     font-weight: bold;
     text-align: center;
-    margin-bottom: 10px;
-    text-shadow: 3px 3px 8px rgba(0, 0, 0, 0.7);
+    margin-bottom: 20px;
 }
-h3 {
+h2 {
     font-size: 2rem;
-    text-align: center;
-    margin-bottom: 40px;
     font-weight: lighter;
+    text-align: center;
+    margin-bottom: 30px;
     color: white;
 }
 button {
     background: linear-gradient(45deg, #ff5722, #ff9800) !important;
     border: none;
-    padding: 12px 20px;
-    font-size: 16px;
     border-radius: 8px;
-    color: white;
+    padding: 12px 20px;
     cursor: pointer;
-    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.3);
-}
-textarea, input {
-    background: black !important;
-    color: white !important;
-    border: 1px solid #ff5722;
-    border-radius: 8px;
+    color: white;
+    font-size: 16px;
+    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
 }
 """
 
 # Gradio Application
 with gr.Blocks(css=custom_css) as app:
     gr.HTML("<h1>🌟 Well-Being Companion</h1>")
-    gr.HTML("<h3>Empowering Your Mental Health Journey 💚</h3>")
+    gr.HTML("<h2>Empowering Your Mental Health Journey 💚</h2>")
 
     with gr.Row():
-        user_message = gr.Textbox(label="Your Message", placeholder="Enter your message...")
-        location = gr.Textbox(label="Your Location", placeholder="Enter your location...")
-        query = gr.Textbox(label="Health Query", placeholder="Search for professionals like therapists...")
+        user_message = gr.Textbox(label="Your Message", placeholder="Type your message...")
+        location = gr.Textbox(label="Your Location", placeholder="Enter location...")
+        query = gr.Textbox(label="Query (e.g., therapist, doctor)", placeholder="Search for professionals...")
 
     chatbot_history = gr.Chatbot(label="Chat History")
     sentiment_output = gr.Textbox(label="Detected Sentiment")
     emotion_output = gr.Textbox(label="Detected Emotion")
-    suggestions_table = gr.DataFrame(headers=["Title", "Link"], label="Suggestions")
-    professionals_output = gr.Textbox(label="Nearby Health Professionals", lines=5)
-    map_output = gr.HTML(label="Map of Nearby Professionals")
+    suggestions_output = gr.DataFrame(headers=["Title", "Link"], label="Suggestions")
+    professionals_output = gr.Textbox(label="Nearby Professionals", lines=5)
+    map_output = gr.HTML(label="Interactive Map")
 
     submit_button = gr.Button("Submit")
-
     submit_button.click(
         app_function,
         inputs=[user_message, location, query, chatbot_history],
-        outputs=[chatbot_history, sentiment_output, emotion_output, suggestions_table, professionals_output, map_output]
+        outputs=[chatbot_history, sentiment_output, emotion_output, suggestions_output, professionals_output, map_output]
     )
 
 app.launch()