Update app.py

app.py

@@ -17,35 +17,40 @@ import torch
 # Disable GPU usage for TensorFlow
 os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
 
-# Suppress TensorFlow GPU warnings & logs
+# Suppress TensorFlow warnings
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
-# Download NLTK resources
+# Download necessary NLTK resources
 nltk.download("punkt")
 
-# Initialize Lancaster Stemmer
+# Initialize a stemmer for NLP
 stemmer = LancasterStemmer()
 
-# Load intents.json for the chatbot
+# Load chatbot intents and training data
 with open("intents.json") as file:
     intents_data = json.load(file)
 
-# Load preprocessed data for Well-Being Chatbot
 with open("data.pickle", "rb") as f:
     words, labels, training, output = pickle.load(f)
 
-# Build TFlearn Chatbot Model
+# Build the chatbot's neural network model
 net = tflearn.input_data(shape=[None, len(training[0])], dtype=tf.float32)
 net = tflearn.fully_connected(net, 8)
 net = tflearn.fully_connected(net, 8)
 net = tflearn.fully_connected(net, len(output[0]), activation="softmax")
 net = tflearn.regression(net)
-
-# Load and initialize the trained model
 chatbot_model = tflearn.DNN(net)
 chatbot_model.load("MentalHealthChatBotmodel.tflearn")
 
-# Function to process user input into a bag-of-words format
+# Tokenizer and model for sentiment analysis (Hugging Face)
+tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
+model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
+
+# Tokenizer and model for emotion detection (Hugging Face)
+tokenizer_emotion = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
+model_emotion = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
+
+# Function to process input for TensorFlow model
 def bag_of_words(s, words):
     bag = [0 for _ in range(len(words))]
     s_words = word_tokenize(s)
@@ -56,29 +61,26 @@ def bag_of_words(s, words):
                 bag[i] = 1
     return np.array(bag)
 
-# Chatbot Response Function
+# Chatbot response function
 def chatbot(message, history):
     history = history or []
-    message = message.lower()
     try:
         results = chatbot_model.predict([bag_of_words(message, words)])
         tag = labels[np.argmax(results)]
-
-        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
+        else:
+            response = "I'm not sure how to respond to that. 🤔"
     except Exception as e:
         response = f"Error: {str(e)} 💥"
+
     history.append({"role": "user", "content": message})
     history.append({"role": "assistant", "content": response})
     return history, response
 
-# Sentiment Analysis Function
-tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
-model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
-
+# Sentiment analysis
 def analyze_sentiment(user_input):
     inputs = tokenizer_sentiment(user_input, return_tensors="pt")
     with torch.no_grad():
@@ -87,10 +89,7 @@ def analyze_sentiment(user_input):
     sentiment_map = ["Negative 😔", "Neutral 😐", "Positive 😊"]
     return sentiment_map[sentiment_class]
 
-# Emotion Detection Function
-tokenizer_emotion = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
-model_emotion = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
-
+# Emotion detection
 def detect_emotion(user_input):
     pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion)
     result = pipe(user_input)
@@ -105,63 +104,44 @@ def detect_emotion(user_input):
     }
     return emotion_map.get(emotion, "Unknown Emotion 🤔")
 
-# Health Professionals Search
-gmaps = googlemaps.Client(key=os.getenv('GOOGLE_API_KEY'))
-
-def get_health_professionals_and_map(location, query):
-    """Search for health professionals and generate a map."""
-    try:
-        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, type="doctor", keyword=query
-            ).get("results", [])
-
-            # Create map
-            m = folium.Map(location=(lat, lng), zoom_start=13)
-            for place in places_result:
-                folium.Marker(
-                    location=[
-                        place["geometry"]["location"]["lat"],
-                        place["geometry"]["location"]["lng"],
-                    ],
-                    popup=place["name"],
-                ).add_to(m)
-            map_html = m._repr_html_()
-            professionals_info = [
-                f"{place['name']} - {place.get('vicinity', 'No address available')}"
-                for place in places_result
-            ]
-            return "\n".join(professionals_info), map_html
-        return "Unable to find location", ""
-    except Exception as e:
-        return f"Error: {e}", ""
-
-# Suggestions Based on Emotion
+# Generate emotion-based suggestions
 def generate_suggestions(emotion):
     suggestions = {
         "😊 Joy": [
-            {"Title": "Meditation 🧘", "Subject": "Relaxation", "Link": "https://example.com/meditation"},
-            {"Title": "Learn a skill 🚀", "Subject": "Growth", "Link": "https://example.com/skills"},
+            ["Relaxation Techniques", "Relaxation", '<a href="https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation" target="_blank">Visit</a>'],
+            ["Dealing with Stress", "Stress Management", '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Visit</a>'],
+            ["Emotional Wellness Toolkit", "Wellness", '<a href="https://www.nih.gov/health-information/emotional-wellness-toolkit" target="_blank">Visit</a>'],
+            ["Relaxation Videos", "Video", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>']
+        ],
+        "😠 Anger": [
+            ["Emotional Wellness Toolkit", "Wellness", '<a href="https://www.nih.gov/health-information/emotional-wellness-toolkit" target="_blank">Visit</a>'],
+            ["Stress Management Tips", "Stress Management", '<a href="https://www.health.harvard.edu/health-a-to-z" target="_blank">Visit</a>'],
+            ["Dealing with Anger", "Anger Management", '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Visit</a>'],
+            ["Relaxation Videos", "Video", '<a href="https://youtu.be/MIc299Flibs" target="_blank">Watch</a>']
         ],
         "😢 Sadness": [
-            {"Title": "Therapist Help 💬", "Subject": "Support", "Link": "https://example.com/therapist"},
-            {"Title": "Stress Management 🌿", "Subject": "Wellness", "Link": "https://example.com/stress"},
+            ["Emotional Wellness Toolkit", "Wellness", '<a href="https://www.nih.gov/health-information/emotional-wellness-toolkit" target="_blank">Visit</a>'],
+            ["Dealing with Anxiety", "Stress Management", '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Visit</a>'],
+            ["Relaxation Videos", "Video", '<a href="https://youtu.be/-e-4Kx5px_I" target="_blank">Watch</a>']
+        ],
+        "😨 Fear": [
+            ["Mindfulness Practices", "Mindfulness", '<a href="https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation" target="_blank">Visit</a>'],
+            ["Coping with Anxiety", "Stress Management", '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Visit</a>'],
+            ["Emotional Wellness Toolkit", "Wellness", '<a href="https://www.nih.gov/health-information/emotional-wellness-toolkit" target="_blank">Visit</a>'],
+            ["Relaxation Videos", "Video", '<a href="https://youtu.be/yGKKz185M5o" target="_blank">Watch</a>']
         ],
     }
-    return suggestions.get(emotion.split(" ")[1].lower(), [])
+    return suggestions.get(emotion.split(" ")[1], [["No specific suggestions available", "", ""]])
 
-# Main Gradio App Function
+# Main app function
 def app_function(message, location, query, history):
     chatbot_history, _ = chatbot(message, history)
     sentiment = analyze_sentiment(message)
     emotion = detect_emotion(message)
     suggestions = generate_suggestions(emotion)
-    places_info, map_html = get_health_professionals_and_map(location, query)
-    return chatbot_history, sentiment, emotion, suggestions, map_html, places_info
+    return chatbot_history, sentiment, emotion, suggestions
 
-# Gradio Interface
+# Gradio UI
 with gr.Blocks() as demo:
     gr.Markdown("# 🌟 Well-being Companion")
     gr.Markdown("Empowering your mental health journey 💚")
@@ -172,29 +152,15 @@ with gr.Blocks() as demo:
         query_input = gr.Textbox(label="Search Query", placeholder="Enter query (e.g., therapist)...", lines=1)
         submit_btn = gr.Button("Submit")
 
-    with gr.Row():
-        chatbot_output = gr.Chatbot(label="Chat History", type="messages")
-    with gr.Row():
-        sentiment_output = gr.Textbox(label="Sentiment Analysis")
-        emotion_output = gr.Textbox(label="Emotion Detected")
-    with gr.Row():
-        suggestions_output = gr.DataFrame(label="Suggestions", headers=["Title", "Subject", "Link"])
-    with gr.Row():
-        map_display = gr.HTML(label="Map of Nearby Professionals")
-        health_info_output = gr.Textbox(label="Health Professionals Info", lines=5)
+    chatbot_output = gr.Chatbot(label="Chat History", type="messages")
+    sentiment_output = gr.Textbox(label="Sentiment Analysis")
+    emotion_output = gr.Textbox(label="Emotion Detected")
+    suggestions_output = gr.DataFrame(label="Suggestions", headers=["Title", "Subject", "Link"])
 
-    # Button interaction
     submit_btn.click(
         app_function,
         inputs=[user_input, location_input, query_input, chatbot_output],
-        outputs=[
-            chatbot_output,
-            sentiment_output,
-            emotion_output,
-            suggestions_output,
-            map_display,
-            health_info_output,
-        ],
+        outputs=[chatbot_output, sentiment_output, emotion_output, suggestions_output],
     )
 
 demo.launch()