Update app.py

app.py

@@ -3,7 +3,6 @@ import gradio as gr
 import nltk
 import numpy as np
 import tflearn
-import tensorflow
 import random
 import json
 import pickle
@@ -25,12 +24,18 @@ nltk.download('punkt')
 stemmer = LancasterStemmer()
 
 # Load intents.json for Well-Being Chatbot
-with open("intents.json") as file:
-    data = json.load(file)
+try:
+    with open("intents.json") as file:
+        data = json.load(file)
+except FileNotFoundError:
+    print("Error: 'intents.json' file not found.")
 
 # Load preprocessed data for Well-Being Chatbot
-with open("data.pickle", "rb") as f:
-    words, labels, training, output = pickle.load(f)
+try:
+    with open("data.pickle", "rb") as f:
+        words, labels, training, output = pickle.load(f)
+except FileNotFoundError:
+    print("Error: 'data.pickle' file not found.")
 
 # Build the model structure for Well-Being Chatbot
 net = tflearn.input_data(shape=[None, len(training[0])])
@@ -41,7 +46,10 @@ net = tflearn.regression(net)
 
 # Load the trained model
 model = tflearn.DNN(net)
-model.load("MentalHealthChatBotmodel.tflearn")
+try:
+    model.load("MentalHealthChatBotmodel.tflearn")
+except IOError:
+    print("Error: Model file not found or corrupted.")
 
 # Function to process user input into a bag-of-words format for Chatbot
 def bag_of_words(s, words):
@@ -86,121 +94,104 @@ tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-senti
 model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 
 def analyze_sentiment(user_input):
-    inputs = tokenizer(user_input, return_tensors="pt")
-    with torch.no_grad():
-        outputs = model_sentiment(**inputs)
-    predicted_class = torch.argmax(outputs.logits, dim=1).item()
-    sentiment = ["Negative", "Neutral", "Positive"][predicted_class]  # Assuming 3 classes
-    return f"Predicted Sentiment: {sentiment}"
+    try:
+        inputs = tokenizer(user_input, return_tensors="pt")
+        with torch.no_grad():
+            outputs = model_sentiment(**inputs)
+        predicted_class = torch.argmax(outputs.logits, dim=1).item()
+        sentiment = ["Negative", "Neutral", "Positive"][predicted_class]
+        return f"Predicted Sentiment: {sentiment}"
+    except Exception as e:
+        return f"Sentiment analysis error: {str(e)}"
 
 # Emotion Detection using Hugging Face model
 tokenizer_emotion = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
 model_emotion = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
 
 def detect_emotion(user_input):
-    pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion)
-    result = pipe(user_input)
-    emotion = result[0]['label']
-    return f"Emotion Detected: {emotion}"
+    try:
+        pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion)
+        result = pipe(user_input)
+        emotion = result[0]['label']
+        return f"Emotion Detected: {emotion}"
+    except Exception as e:
+        return f"Emotion detection error: {str(e)}"
 
 # Initialize Google Maps API client securely
 gmaps = googlemaps.Client(key=os.getenv('GOOGLE_API_KEY'))
 
 # Function to search for health professionals
 def search_health_professionals(query, location, radius=10000):
-    places_result = gmaps.places_nearby(location, radius=radius, type='doctor', keyword=query)
-    return places_result.get('results', [])
+    try:
+        places_result = gmaps.places_nearby(location, radius=radius, type='doctor', keyword=query)
+        if 'results' in places_result:
+            return places_result['results']
+        else:
+            return []
+    except Exception as e:
+        print(f"Error fetching health professionals: {str(e)}")
+        return []
 
 # Function to get directions and display on Gradio UI
 def get_health_professionals_and_map(current_location, health_professional_query):
-    # ... (Your function definition remains unchanged)
+    # Get health professionals using the search function
+    health_professionals = search_health_professionals(health_professional_query, current_location)
+    
+    # Generate the map with the professionals' locations
+    map_obj = folium.Map(location=current_location, zoom_start=13)
+    for professional in health_professionals:
+        location = professional['geometry']['location']
+        folium.Marker([location['lat'], location['lng']], popup=professional['name']).add_to(map_obj)
+    
+    # Save the map to an HTML file
+    map_html = "health_professionals_map.html"
+    map_obj.save(map_html)
+    
+    # Generate route information (basic for now)
+    route_info = [f"{hp['name']} - {hp['vicinity']}" for hp in health_professionals]
+    
+    return route_info, map_html
 
 # Function to generate suggestions based on the detected emotion
 def generate_suggestions(emotion):
-    if emotion == 'joy':
-        return [
+    suggestions = {
+        'joy': [
             {"Title": "Relaxation Techniques", "Subject": "Relaxation", "Link": "https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation"},
             {"Title": "Dealing with Stress", "Subject": "Stress Management", "Link": "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"},
             {"Title": "Emotional Wellness Toolkit", "Subject": "Wellness", "Link": "https://www.nih.gov/health-information/emotional-wellness-toolkit"},
             {"Title": "Relaxation Video", "Subject": "Video", "Link": "https://youtu.be/m1vaUGtyo-A"}
-        ]
-    elif emotion == 'anger':
-        return [
+        ],
+        'anger': [
             {"Title": "Emotional Wellness Toolkit", "Subject": "Wellness", "Link": "https://www.nih.gov/health-information/emotional-wellness-toolkit"},
             {"Title": "Stress Management Tips", "Subject": "Stress Management", "Link": "https://www.health.harvard.edu/health-a-to-z"},
             {"Title": "Dealing with Anger", "Subject": "Anger Management", "Link": "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"},
             {"Title": "Relaxation Video", "Subject": "Video", "Link": "https://youtu.be/MIc299Flibs"}
-        ]
-    elif emotion == 'fear':
-        return [
+        ],
+        'fear': [
             {"Title": "Mindfulness Practices", "Subject": "Mindfulness", "Link": "https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation"},
             {"Title": "Coping with Anxiety", "Subject": "Anxiety Management", "Link": "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"},
             {"Title": "Emotional Wellness Toolkit", "Subject": "Wellness", "Link": "https://www.nih.gov/health-information/emotional-wellness-toolkit"},
             {"Title": "Relaxation Video", "Subject": "Video", "Link": "https://youtu.be/yGKKz185M5o"}
-        ]
-    elif emotion == 'sadness':
-        return [
+        ],
+        'sadness': [
             {"Title": "Emotional Wellness Toolkit", "Subject": "Wellness", "Link": "https://www.nih.gov/health-information/emotional-wellness-toolkit"},
             {"Title": "Dealing with Anxiety", "Subject": "Anxiety Management", "Link": "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"},
             {"Title": "Relaxation Video", "Subject": "Video", "Link": "https://youtu.be/-e-4Kx5px_I"}
-        ]
-    elif emotion == 'surprise':
-        return [
+        ],
+        'surprise': [
             {"Title": "Managing Stress", "Subject": "Stress Management", "Link": "https://www.health.harvard.edu/health-a-to-z"},
             {"Title": "Coping Strategies", "Subject": "Coping", "Link": "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"},
             {"Title": "Relaxation Video", "Subject": "Video", "Link": "https://youtu.be/m1vaUGtyo-A"}
         ]
-    else:
-        return []
-
-# Gradio interface
-def gradio_app(message, location, health_query, submit_button, history, state):
-    # Chatbot interaction
-    history, _ = chatbot(message, history)
-    
-    # Sentiment analysis
-    sentiment_response = analyze_sentiment(message)
+    }
     
-    # Emotion detection
-    emotion_response = detect_emotion(message)
-    
-    # Health professional search and map display
-    route_info, map_html = get_health_professionals_and_map(location, health_query)
-    
-    # Generate suggestions based on the detected emotion
-    suggestions = generate_suggestions(emotion_response.split(': ')[1])
-    
-    # Create a DataFrame for displaying suggestions
-    suggestions_df = pd.DataFrame(suggestions)
-    
-    return history, sentiment_response, emotion_response, route_info, map_html, gr.DataFrame(suggestions_df, headers=["Title", "Subject", "Link"]), state
-
-# Gradio UI components
-message_input = gr.Textbox(lines=1, label="Message")
-location_input = gr.Textbox(value="Honolulu, HI", label="Current Location")
-health_query_input = gr.Textbox(value="doctor", label="Health Professional Query (e.g., doctor, psychiatrist, psychologist)")
-submit_button = gr.Button("Submit")  # Submit button
-
-# Updated chat history component with 'messages' type
-chat_history = gr.Chatbot(label="Well-Being Chat History", type='messages')
-
-# Outputs
-sentiment_output = gr.Textbox(label="Sentiment Analysis Result")
-emotion_output = gr.Textbox(label="Emotion Detection Result")
-route_info_output = gr.Textbox(label="Health Professionals Information")
-map_output = gr.HTML(label="Map with Health Professionals")
-suggestions_output = gr.DataFrame(label="Well-Being Suggestions", headers=["Title", "Subject", "Link"])
-
-# Create Gradio interface
-# Ensure there is exactly one state input and one state output
-iface = gr.Interface(
-    fn=gradio_app, 
-    inputs=[message_input, location_input, health_query_input, submit_button, gr.State()],  # Updated to include only one state input
-    outputs=[chat_history, sentiment_output, emotion_output, route_info_output, map_output, suggestions_output, gr.State()],  # Updated to include only one state output
-    allow_flagging="never",
-    live=True,
-    title="Well-Being App: Support, Sentiment, Emotion Detection & Health Professional Search"
-)
-
-# Launch the Gradio interface
+    return suggestions.get(emotion.lower(), [])
+
+# Define the Gradio interface
+iface = gr.Interface(fn=chatbot,
+                     inputs=[gr.Textbox(label="Message"),
+                             gr.State()],
+                     outputs=[gr.Chatbot(), gr.State()],
+                     allow_flagging="never", theme="compact")
+
 iface.launch()