Update app.py

app.py

@@ -13,7 +13,7 @@ import googlemaps
 import folium
 import torch
 
-# Disable GPU usage for TensorFlow and suppress TensorFlow warnings
+# Disable GPU usage for TensorFlow
 os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
@@ -23,7 +23,7 @@ nltk.download("punkt")
 # Initialize Lancaster Stemmer
 stemmer = LancasterStemmer()
 
-# Load intents.json and training data for the chatbot
+# Load chatbot intents and training data
 with open("intents.json") as file:
     intents_data = json.load(file)
 
@@ -39,9 +39,11 @@ net = tflearn.regression(net)
 chatbot_model = tflearn.DNN(net)
 chatbot_model.load("MentalHealthChatBotmodel.tflearn")
 
-# Hugging Face models for sentiment and emotion detection
+# Model for sentiment detection
 tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
+
+# Model for emotion detection
 tokenizer_emotion = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
 model_emotion = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
 
@@ -60,6 +62,7 @@ def bag_of_words(s, words):
     return np.array(bag)
 
 def chatbot(message, history):
+    """Generate chatbot response and append to history (as tuples)."""
     history = history or []
     try:
         results = chatbot_model.predict([bag_of_words(message, words)])
@@ -71,8 +74,9 @@ def chatbot(message, history):
                 break
     except Exception as e:
         response = f"Error: {str(e)} 💥"
-    history.append({"role": "user", "content": message})
-    history.append({"role": "assistant", "content": response})
+
+    # Append the message and response as a tuple
+    history.append((message, response))
     return history, response
 
 # Sentiment analysis
@@ -107,7 +111,7 @@ def generate_suggestions(emotion):
             ["Relaxation Video", '<a href="https://youtu.be/MIc299Flibs" target="_blank">Watch</a>'],
         ],
     }
-    return suggestions.get(emotion, [["No suggestions available", "", ""]])
+    return suggestions.get(emotion, [["No suggestions available", ""]])
 
 # Search professionals and generate map
 def get_health_professionals_and_map(location, query):
@@ -130,17 +134,17 @@ def get_health_professionals_and_map(location, query):
 
 # Main app function
 def app_function(message, location, query, history):
-    chatbot_history, _ = chatbot(message, history)
-    sentiment = analyze_sentiment(message)
-    emotion = detect_emotion(message.lower())
-    suggestions = generate_suggestions(emotion)
-    professionals, map_html = get_health_professionals_and_map(location, query)
+    chatbot_history, _ = chatbot(message, history)  # Generate chatbot response
+    sentiment = analyze_sentiment(message)          # Detect sentiment
+    emotion = detect_emotion(message.lower())       # Detect emotion
+    suggestions = generate_suggestions(emotion)     # Generate suggestions
+    professionals, map_html = get_health_professionals_and_map(location, query)  # Find professionals & map
     return chatbot_history, sentiment, emotion, suggestions, professionals, map_html
 
 # Gradio app interface
 with gr.Blocks() as app:
     gr.Markdown("# 🌟 Well-Being Companion")
-    gr.Markdown("Empowering your Well-Being journey 💚")
+    gr.Markdown("Empowering Your Mental Health Journey 💚")
 
     with gr.Row():
         user_message = gr.Textbox(label="Your Message", placeholder="Enter your message...")
@@ -148,19 +152,19 @@ with gr.Blocks() as app:
         search_query = gr.Textbox(label="Query", placeholder="Search for professionals...")
         submit_btn = gr.Button("Submit")
 
-    chatbot_box = gr.Chatbot(label="Chat History")
+    chatbot_box = gr.Chatbot(label="Chat History")  # Corrected history format (list of tuples)
     emotion_output = gr.Textbox(label="Detected Emotion")
     sentiment_output = gr.Textbox(label="Detected Sentiment")
     suggestions_output = gr.DataFrame(headers=["Title", "Links"], label="Suggestions")
     map_output = gr.HTML(label="Nearby Professionals Map")
-    professional_list = gr.Textbox(label="Nearby Professionals", lines=5)
+    professional_display = gr.Textbox(label="Nearby Professionals", lines=5)
 
     submit_btn.click(
         app_function,
         inputs=[user_message, user_location, search_query, chatbot_box],
         outputs=[
             chatbot_box, sentiment_output, emotion_output,
-            suggestions_output, professional_list, map_output,
+            suggestions_output, professional_display, map_output,
         ],
     )