Update app.py

app.py

@@ -16,25 +16,23 @@ import torch
 
 # Disable GPU usage for TensorFlow
 os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
-
-# Suppress TensorFlow warnings
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
-# Download necessary NLTK resources
+# Ensure necessary NLTK resources are downloaded
 nltk.download("punkt")
 
-# Initialize a stemmer for NLP
+# Initialize stemmer
 stemmer = LancasterStemmer()
 
-# Load chatbot intents and training data
+# Load intents.json and training data for chatbot
 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 the chatbot's neural network model
-net = tflearn.input_data(shape=[None, len(training[0])], dtype=tf.float32)
+# Build Chatbot Model
+net = tflearn.input_data(shape=[None, len(training[0])])
 net = tflearn.fully_connected(net, 8)
 net = tflearn.fully_connected(net, 8)
 net = tflearn.fully_connected(net, len(output[0]), activation="softmax")
@@ -42,17 +40,20 @@ net = tflearn.regression(net)
 chatbot_model = tflearn.DNN(net)
 chatbot_model.load("MentalHealthChatBotmodel.tflearn")
 
-# Tokenizer and model for sentiment analysis (Hugging Face)
+# Sentiment Analysis Model (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)
+# Emotion Detection Model
 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
+# Google Maps API Client
+gmaps = googlemaps.Client(key=os.getenv('GOOGLE_API_KEY'))
+
+# Process Text Input for Chatbot
 def bag_of_words(s, words):
-    bag = [0 for _ in range(len(words))]
+    bag = [0] * len(words)
     s_words = word_tokenize(s)
     s_words = [stemmer.stem(word.lower()) for word in s_words if word.isalnum()]
     for se in s_words:
@@ -61,26 +62,24 @@ def bag_of_words(s, words):
                 bag[i] = 1
     return np.array(bag)
 
-# Chatbot response function
+# Chatbot Functionality
 def chatbot(message, history):
     history = history or []
     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
+# Detect Sentiment
 def analyze_sentiment(user_input):
     inputs = tokenizer_sentiment(user_input, return_tensors="pt")
     with torch.no_grad():
@@ -89,7 +88,7 @@ def analyze_sentiment(user_input):
     sentiment_map = ["Negative 😔", "Neutral 😐", "Positive 😊"]
     return sentiment_map[sentiment_class]
 
-# Emotion detection
+# Detect Emotion
 def detect_emotion(user_input):
     pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion)
     result = pipe(user_input)
@@ -104,63 +103,83 @@ def detect_emotion(user_input):
     }
     return emotion_map.get(emotion, "Unknown Emotion 🤔")
 
-# Generate emotion-based suggestions
+# Generate Suggestions for Detected Emotion
 def generate_suggestions(emotion):
-    suggestions = {
+    resources = {
         "😊 Joy": [
             ["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": [
             ["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>'],
+            ["Dealing with Anxiety", "Anxiety 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>'],
+            ["Coping with Anxiety", "Anxiety 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], [["No specific suggestions available", "", ""]])
+    return resources.get(emotion.split(" ")[1], [["No specific suggestions available", "", ""]])
 
-# Main app function
+# Search Professionals and Generate Map
+def get_health_professionals_and_map(location, query):
+    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
+            )["results"]
+            map_ = folium.Map(location=(lat, lng), zoom_start=13)
+            professionals = []
+            for place in places_result:
+                professionals.append(f"{place['name']} - {place.get('vicinity', '')}")
+                lat, lng = place["geometry"]["location"]["lat"], place["geometry"]["location"]["lng"]
+                folium.Marker([lat, lng], popup=place["name"]).add_to(map_)
+            return professionals, map_._repr_html_()
+        return ["No professionals found"], ""
+    except Exception as e:
+        return [f"Error: {e}"], ""
+
+# Gradio 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)
-    return chatbot_history, sentiment, emotion, suggestions
+    professionals_info, map_html = get_health_professionals_and_map(location, query)
+    return chatbot_history, sentiment, emotion, suggestions, professionals_info, map_html
 
-# Gradio UI
+# Gradio Interface
 with gr.Blocks() as demo:
-    gr.Markdown("# 🌟 Well-being Companion")
+    gr.Markdown("# 🌟 Well-Being Companion")
     gr.Markdown("Empowering your mental health journey 💚")
-
+    
     with gr.Row():
-        user_input = gr.Textbox(label="Your Message", placeholder="Type your message...", lines=2)
-        location_input = gr.Textbox(label="Your Location", placeholder="Enter location...", lines=2)
-        query_input = gr.Textbox(label="Search Query", placeholder="Enter query (e.g., therapist)...", lines=1)
-        submit_btn = gr.Button("Submit")
+        user_input = gr.Textbox(label="Your Message")
+        location_input = gr.Textbox(label="Your Location")
+        query_input = gr.Textbox(label="Search Query")
+        submit_button = gr.Button("Submit")
 
     chatbot_output = gr.Chatbot(label="Chat History", type="messages")
-    sentiment_output = gr.Textbox(label="Sentiment Analysis")
+    sentiment_output = gr.Textbox(label="Sentiment Detected")
     emotion_output = gr.Textbox(label="Emotion Detected")
     suggestions_output = gr.DataFrame(label="Suggestions", headers=["Title", "Subject", "Link"])
+    professionals_output = gr.Textbox(label="Nearby Professionals", lines=5)
+    map_output = gr.HTML(label="Map of Nearby Professionals")
 
-    submit_btn.click(
+    submit_button.click(
         app_function,
         inputs=[user_input, location_input, query_input, chatbot_output],
-        outputs=[chatbot_output, sentiment_output, emotion_output, suggestions_output],
+        outputs=[
+            chatbot_output, sentiment_output, emotion_output,
+            suggestions_output, professionals_output, map_output
+        ],
     )
 
 demo.launch()