Update app.py

app.py

@@ -13,7 +13,7 @@ import googlemaps
 import folium
 import torch
 
-# Suppress TensorFlow GPU usage and warnings
+# Suppress TensorFlow warnings
 os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 
@@ -21,14 +21,14 @@ os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 nltk.download("punkt")
 stemmer = LancasterStemmer()
 
-# Load intents.json and chatbot training data
+# Load intents and chatbot training data
 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 model
 net = tflearn.input_data(shape=[None, len(training[0])])
 net = tflearn.fully_connected(net, 8)
 net = tflearn.fully_connected(net, 8)
@@ -37,7 +37,7 @@ net = tflearn.regression(net)
 chatbot_model = tflearn.DNN(net)
 chatbot_model.load("MentalHealthChatBotmodel.tflearn")
 
-# Hugging Face Models for Sentiment and Emotion Detection
+# Hugging Face sentiment and emotion 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")
@@ -58,8 +58,8 @@ def bag_of_words(s, words):
                 bag[i] = 1
     return np.array(bag)
 
-def chatbot(message, history):
-    """Generate chatbot response and maintain chat history."""
+def generate_chatbot_response(message, history):
+    """Generate chatbot response and maintain conversation history."""
     history = history or []
     try:
         result = chatbot_model.predict([bag_of_words(message, words)])
@@ -75,7 +75,7 @@ def chatbot(message, history):
     return history, response
 
 def analyze_sentiment(user_input):
-    """Analyze sentiment of user input."""
+    """Analyze sentiment and map to emojis."""
     inputs = tokenizer_sentiment(user_input, return_tensors="pt")
     with torch.no_grad():
         outputs = model_sentiment(**inputs)
@@ -84,7 +84,7 @@ def analyze_sentiment(user_input):
     return f"Sentiment: {sentiment_map[sentiment_class]}"
 
 def detect_emotion(user_input):
-    """Detect emotion from user input."""
+    """Detect emotions based on input."""
     pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion)
     result = pipe(user_input)
     emotion = result[0]["label"].lower().strip()
@@ -96,46 +96,48 @@ def detect_emotion(user_input):
         "surprise": "Surprise 😲",
         "neutral": "Neutral 😐",
     }
-    return emotion_map.get(emotion, "Unknown 🤔"), emotion  # Text + clean emotion for matching keys
+    return emotion_map.get(emotion, "Unknown 🤔"), emotion  # Text with matching key
 
 def generate_suggestions(emotion):
-    """Generate helpful resources based on emotion."""
+    """Return relevant suggestions based on detected emotions."""
+    emotion_key = emotion.lower()
     suggestions = {
         "joy": [
-            ["Relaxation Techniques", '<a href="https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation" target="_blank">Visit</a>'],
+            ["Relaxation Techniques", '<a href="https://www.helpguide.org/mental-health/meditation" target="_blank">Visit</a>'],
             ["Emotional Toolkit", '<a href="https://www.nih.gov" target="_blank">Visit</a>'],
             ["Stress Management", '<a href="https://www.health.harvard.edu" target="_blank">Visit</a>'],
         ],
         "anger": [
-            ["Calm Down Exercises", '<a href="https://youtu.be/MIc299Flibs" target="_blank">Watch</a>'],
-            ["Handle Anger", '<a href="https://www.helpguide.org" target="_blank">Visit</a>']
+            ["Handle Anger", '<a href="https://youtu.be/MIc299Flibs" target="_blank">Watch</a>'],
+            ["Stress Tips", '<a href="https://www.helpguide.org/mental-health/anger-management.htm" target="_blank">Visit</a>'],
         ],
         "fear": [
-            ["Overcoming Fear", '<a href="https://youtu.be/yGKKz185M5o" target="_blank">Watch</a>'],
+            ["Coping with Anxiety", '<a href="https://www.helpguide.org/mental-health/anxiety" target="_blank">Visit</a>'],
+            ["Mindfulness", '<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>']
+            ["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>'],
             ["Relaxation Video", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>'],
         ],
         "neutral": [
-            ["General Wellness Tips", '<a href="https://www.psychologytoday.com" target="_blank">Visit</a>'],
-        ]
+            ["General Well-Being Tips", '<a href="https://www.psychologytoday.com" target="_blank">Visit</a>'],
+        ],
     }
-    return suggestions.get(emotion, [["No specific suggestions available.", ""]])
+    return suggestions.get(emotion_key, [["No specific suggestions available.", ""]])
 
 def get_health_professionals_and_map(location, query):
-    """Search for healthcare professionals and create an interactive map."""
+    """Search nearby healthcare professionals using Google Maps API."""
     try:
         if not location or not query:
-            return ["Please provide both a location and search query."], ""
+            return ["Please provide both 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"]
-
             professionals = []
             map_ = folium.Map(location=(lat, lng), zoom_start=13)
             for place in places_result:
@@ -144,87 +146,44 @@ def get_health_professionals_and_map(location, query):
                     location=[place["geometry"]["location"]["lat"], place["geometry"]["location"]["lng"]],
                     popup=f"{place['name']}"
                 ).add_to(map_)
-
             return professionals, map_._repr_html_()
 
-        return ["No professionals found near this location."], ""
+        return ["No professionals found for the given location."], ""
     except Exception as e:
-        return [f"Error: {e}"], ""
+        return [f"An error occurred: {e}"], ""
 
-# Main Application
+# Main Application Logic
 def app_function(user_input, location, query, history):
-    chatbot_history, _ = chatbot(user_input, history)  # Chatbot response
-    sentiment_output = analyze_sentiment(user_input)  # Sentiment analysis
-    emotion_detected, clean_emotion = detect_emotion(user_input)  # Emotion detection
-    suggestions = generate_suggestions(clean_emotion)  # Emotion-matched suggestions
-    professionals, map_html = get_health_professionals_and_map(location, query)  # Map of professionals
-    return chatbot_history, sentiment_output, emotion_detected, suggestions, professionals, map_html
-
-# Custom CSS for Polished UI
+    chatbot_history, _ = generate_chatbot_response(user_input, history)  # Generate chatbot response
+    sentiment_result = analyze_sentiment(user_input)  # Sentiment detection
+    emotion_result, cleaned_emotion = detect_emotion(user_input)  # Emotion detection
+    suggestions = generate_suggestions(cleaned_emotion)  # Fetch suggestions for emotion
+    professionals, map_html = get_health_professionals_and_map(location, query)  # Nearby professionals with map
+    return chatbot_history, sentiment_result, emotion_result, suggestions, professionals, map_html
+
+# Gradio Interface
 custom_css = """
-body {
-    background: linear-gradient(135deg, #000000, #ff5722);
-    font-family: 'Roboto', sans-serif;
-    color: white;
-}
-h1 {
-    font-size: 4.5rem;
-    font-weight: bold;
-    text-align: center;
-    margin: 20px auto;
-}
-h2 {
-    font-size: 2rem;
-    text-align: center;
-    margin-bottom: 30px;
-    color: white;
-    font-weight: lighter;
-}
-button {
-    background: linear-gradient(45deg, #ff5722, #ff9800);
-    border: none;
-    color: white;
-    padding: 12px 20px;
-    font-size: 16px;
-    border-radius: 8px;
-    cursor: pointer;
-}
-textarea, input {
-    background: black;
-    color: white;
-    border: 1px solid #ff5722;
-    padding: 12px;
-    border-radius: 8px;
-}
-.gr-dataframe {
-    background-color: black !important;
-    color: white !important;
-    overflow: auto;
-    border: 1px solid orange;
-}
+body { background: linear-gradient(135deg,#0d0d0d,#ff5722); color: white; }
+textarea, input { background: black; color: white; border: 2px solid orange; padding: 10px }
 """
 
-# Gradio UI Interface
 with gr.Blocks(css=custom_css) as app:
-    gr.HTML("<h1>🌟 Well-Being Companion</h1>")
-    gr.HTML("<h2>Empowering Your Mental Health Journey 💚</h2>")
-
+    gr.HTML("<h1 style='text-align: center'>🌟 Well-Being Companion</h1>")
     with gr.Row():
-        user_message = gr.Textbox(label="Your Message", placeholder="Enter your message here...")
-        location = gr.Textbox(label="Your Location", placeholder="Enter a city...")
-        query = gr.Textbox(label="Search Query", placeholder="e.g., therapist, doctor")
-
+        user_input = gr.Textbox(label="Your Message")
+        location = gr.Textbox(label="Your Location")
+        query = gr.Textbox(label="Search Query")
     chatbot = gr.Chatbot(label="Chat History")
     sentiment = gr.Textbox(label="Detected Sentiment")
     emotion = gr.Textbox(label="Detected Emotion")
-    suggestions = gr.DataFrame(headers=["Suggestion Title", "Link"], label="Suggestions")
+    suggestions = gr.DataFrame(headers=["Title", "Link"])
     professionals = gr.Textbox(label="Nearby Professionals", lines=6)
     map_html = gr.HTML(label="Interactive Map")
+    submit = gr.Button("Submit")
 
-    submit_button = gr.Button("Submit")
-    submit_button.click(
+    submit.click(
         app_function,
-        inputs=[user_message, location, query, chatbot],
+        inputs=[user_input, location, query, chatbot],
         outputs=[chatbot, sentiment, emotion, suggestions, professionals, map_html]
     )