Update app.py

app.py

@@ -1,3 +1,4 @@
+import gradio as gr
 import nltk
 import numpy as np
 import tflearn
@@ -5,14 +6,14 @@ import tensorflow
 import random
 import json
 import pickle
-import gradio as gr
 from nltk.tokenize import word_tokenize
 from nltk.stem.lancaster import LancasterStemmer
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
 import googlemaps
 import folium
 import os
-import torch  # Add this for sentiment analysis
+import pandas as pd
+import torch
 
 # Ensure necessary NLTK resources are downloaded
 nltk.download('punkt')
@@ -20,15 +21,15 @@ nltk.download('punkt')
 # Initialize the stemmer
 stemmer = LancasterStemmer()
 
-# Load intents.json for Mental Health Chatbot
+# Load intents.json for Well-Being Chatbot
 with open("intents.json") as file:
     data = json.load(file)
 
-# Load preprocessed data for Mental Health Chatbot
+# Load preprocessed data for Well-Being Chatbot
 with open("data.pickle", "rb") as f:
     words, labels, training, output = pickle.load(f)
 
-# Build the model structure for Mental Health Chatbot
+# Build the model structure for Well-Being Chatbot
 net = tflearn.input_data(shape=[None, len(training[0])])
 net = tflearn.fully_connected(net, 8)
 net = tflearn.fully_connected(net, 8)
@@ -50,7 +51,7 @@ def bag_of_words(s, words):
                 bag[i] = 1
     return np.array(bag)
 
-# Chat function for Mental Health Chatbot
+# Chat function for Well-Being Chatbot
 def chatbot(message, history):
     history = history or []
     message = message.lower()
@@ -74,7 +75,6 @@ def chatbot(message, history):
     history.append((message, response))
     return history, history
 
-
 # Sentiment Analysis using Hugging Face model
 tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
@@ -142,8 +142,46 @@ def get_health_professionals_and_map(current_location, health_professional_query
     
     return route_info, m._repr_html_()
 
+# Function to generate suggestions based on the detected emotion
+def generate_suggestions(emotion):
+    if emotion == 'joy':
+        return [
+            {"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 [
+            {"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 [
+            {"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 [
+            {"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 [
+            {"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, history, state):
+def gradio_app(message, location, health_query, submit_button, history, state):
     # Chatbot interaction
     history, _ = chatbot(message, history)
     
@@ -156,29 +194,38 @@ def gradio_app(message, location, health_query, history, state):
     # Health professional search and map display
     route_info, map_html = get_health_professionals_and_map(location, health_query)
     
-    return history, sentiment_response, emotion_response, route_info, map_html, state
+    # 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 for triggering the app function
 
-chat_history = gr.Chatbot(label="Chat History")
+chat_history = gr.Chatbot(label="Well-Being Chat History")  # Renamed chatbot label
 
 # 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"])  # Renamed suggestions output
 
 # Create Gradio interface
 iface = gr.Interface(
     fn=gradio_app, 
-    inputs=[message_input, location_input, health_query_input, gr.State()],  # Updated state input
-    outputs=[chat_history, sentiment_output, emotion_output, route_info_output, map_output, gr.State()],  # Updated state output
+    inputs=[message_input, location_input, health_query_input, submit_button, gr.State(), gr.State()],  # Updated inputs to include submit button
+    outputs=[chat_history, sentiment_output, emotion_output, route_info_output, map_output, suggestions_output, gr.State()],  # Outputs remain unchanged
     allow_flagging="never",
     live=True,
-    title="Wellbeing App: Mental Health, Sentiment, Emotion Detection & Health Professional Search"
+    title="Well-Being App: Support, Sentiment, Emotion Detection & Health Professional Search"
 )
 
-iface.launch()
+# Launch the Gradio interface
+iface.launch()