Update app.py

app.py

@@ -12,20 +12,20 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipe
 import pandas as pd
 import torch
 
-# Disable TensorFlow GPU warnings (safe since we are using CPU)
+# Disable TensorFlow GPU warnings
 os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 
 # Download necessary NLTK resources
 nltk.download("punkt")
 
-# Initialize Lancaster Stemmer for text preprocessing
+# Initialize Lancaster Stemmer
 stemmer = LancasterStemmer()
 
-# Load intents.json for the chatbot
+# Load intents.json for chatbot
 with open("intents.json") as file:
     intents_data = json.load(file)
 
-# Load tokenized training data for chatbot
+# Load tokenized training data
 with open("data.pickle", "rb") as f:
     words, labels, training, output = pickle.load(f)
 
@@ -42,7 +42,7 @@ def build_chatbot_model():
 
 chatbot_model = build_chatbot_model()
 
-# Function: Bag of Words
+# Bag of Words Function
 def bag_of_words(s, words):
     bag = [0 for _ in range(len(words))]
     s_words = word_tokenize(s)
@@ -55,13 +55,12 @@ def bag_of_words(s, words):
 
 # Chatbot Response Function
 def chatbot_response(message, history):
-    """Generates a chatbot response."""
     history = history or []
     try:
         result = chatbot_model.predict([bag_of_words(message, words)])
         idx = np.argmax(result)
         tag = labels[idx]
-        response = "I didn't understand that. 🤔"
+        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"])
@@ -73,7 +72,7 @@ def chatbot_response(message, history):
     history.append({"role": "assistant", "content": response})
     return history, response
 
-# Emotion Detection Function
+# Emotion Detection
 emotion_tokenizer = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
 emotion_model = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
 
@@ -94,12 +93,11 @@ def detect_emotion(user_input):
     except Exception as e:
         return f"Error detecting emotion: {str(e)} 💥"
 
-# Sentiment Analysis Function
+# Sentiment Analysis
 sentiment_tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 sentiment_model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 
 def analyze_sentiment(user_input):
-    """Analyze sentiment based on input."""
     inputs = sentiment_tokenizer(user_input, return_tensors="pt")
     try:
         with torch.no_grad():
@@ -128,64 +126,46 @@ def generate_suggestions(emotion):
     }
     return suggestions_map.get(emotion, [{"Title": "General Wellness Resources 🌈", "Link": "https://www.helpguide.org/wellness"}])
 
-# Dummy Nearby Professionals Function
+# Nearby Professionals Function
 def search_nearby_professionals(location, query):
-    """Simulates the search for nearby professionals."""
+    """Returns a list of professionals as a list of lists for compatibility with DataFrame."""
     if location and query:
-        return [
+        results = [
             {"Name": "Wellness Center", "Address": "123 Wellness Way"},
             {"Name": "Mental Health Clinic", "Address": "456 Recovery Road"},
             {"Name": "Therapy Hub", "Address": "789 Peace Avenue"},
         ]
+        return [[item["Name"], item["Address"]] for item in results]
     return []
 
 # Main App Logic
 def well_being_app(user_input, location, query, history):
-    """Handles chatbot interaction, emotion detection, sentiment analysis, and professional search results."""
-    # Chatbot Response
     history, _ = chatbot_response(user_input, history)
-
-    # Emotion Detection
     emotion = detect_emotion(user_input)
-
-    # Sentiment Analysis
     sentiment = analyze_sentiment(user_input)
-
-    # Emotion-based Suggestions
     emotion_name = emotion.split(": ")[-1]
     suggestions = generate_suggestions(emotion_name)
     suggestions_df = pd.DataFrame(suggestions)
-
-    # Nearby Professionals Lookup
     professionals = search_nearby_professionals(location, query)
-
     return history, sentiment, emotion, suggestions_df, professionals
 
 # Gradio Interface
 with gr.Blocks() as interface:
     gr.Markdown("## 🌱 Well-being Companion")
-    gr.Markdown("> Empowering Your Health! 💚")
+    gr.Markdown("> Empowering Your Mental Health! 💚")
 
     with gr.Row():
-        user_input = gr.Textbox(label="Your Message", placeholder="How are you feeling today? (e.g. I feel happy)")
-        location_input = gr.Textbox(label="Location", placeholder="Enter your city (e.g., New York)")
-        query_input = gr.Textbox(label="Search Query", placeholder="What are you searching for? (e.g., therapists)")
-        submit_button = gr.Button("Submit", variant="primary")
+        user_input = gr.Textbox(label="Your Message")
+        location_input = gr.Textbox(label="Location")
+        query_input = gr.Textbox(label="Search Query")
+        submit_button = gr.Button("Submit")
 
-    # Chatbot Section
     chatbot_output = gr.Chatbot(label="Chatbot Interaction", type="messages", value=[])
-
-    # Sentiment and Emotion Outputs
     sentiment_output = gr.Textbox(label="Sentiment Analysis")
     emotion_output = gr.Textbox(label="Emotion Detected")
-
-    # Suggestions Table
     suggestions_output = gr.DataFrame(label="Suggestions", value=[], headers=["Title", "Link"])
+    nearby_professionals_output = gr.DataFrame(label="Nearby Professionals", headers=["Name", "Address"])
 
-    # Professionals Table
-    nearby_professionals_output = gr.DataFrame(label="Nearby Professionals", value=[], headers=["Name", "Address"])
-
-    # Connect Inputs to Outputs
     submit_button.click(
         well_being_app,
         inputs=[user_input, location_input, query_input, chatbot_output],
@@ -198,5 +178,4 @@ with gr.Blocks() as interface:
         ],
     )
 
-# Run Gradio Application
 interface.launch()