Update app.py

app.py

@@ -11,13 +11,6 @@ from nltk.stem.lancaster import LancasterStemmer
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
 import requests
 import pandas as pd
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-import chromedriver_autoinstaller
-import os
-import time
-import re
-from bs4 import BeautifulSoup
 
 # Ensure necessary NLTK resources are downloaded
 nltk.download('punkt')
@@ -37,7 +30,7 @@ try:
     with open("data.pickle", "rb") as f:
         words, labels, training, output = pickle.load(f)
 except FileNotFoundError:
-    raise FileNotFoundError("Error: 'data.pickle' file not found in the app directory.")
+    raise FileNotFoundError("Error: 'data.pickle' file not found. Ensure it exists and matches the model.")
 
 # Build the model structure
 net = tflearn.input_data(shape=[None, len(training[0])])
@@ -157,7 +150,7 @@ def provide_suggestions(emotion):
     return suggestions
 
 # Google Places API to get nearby wellness professionals
-api_key = "GOOGLE_API_KEY"  # Replace with your API key
+api_key = "YOUR_GOOGLE_API_KEY"  # Replace with your actual Google API key
 
 def get_places_data(query, location, radius, api_key, next_page_token=None):
     url = "https://maps.googleapis.com/maps/api/place/textsearch/json"
@@ -227,21 +220,21 @@ iface = gr.Interface(
     fn=gradio_interface,
     inputs=[
         gr.Textbox(label="Enter your message", placeholder="How are you feeling today?"),
-        gr.Textbox(label="Enter your location (e.g., Hawaii, Oahu)", placeholder="Your location"),
-        gr.State()  # One state input
+        gr.Textbox(label="Enter your location (e.g., 'Hawaii, USA')", placeholder="Enter your location"),
+        gr.State()  # To maintain state (chat history)
     ],
     outputs=[
-        gr.Chatbot(label="Chatbot History"),
-        gr.Textbox(label="Sentiment"),
-        gr.Textbox(label="Emotion"),
-        gr.Dataframe(label="Wellness Professionals"),
-        gr.State()  # State output (maintains conversation history)
+        gr.Chatbot(label="Chatbot Responses"),
+        gr.Textbox(label="Sentiment Analysis"),
+        gr.Textbox(label="Emotion Detected"),
+        gr.DataFrame(label="Suggested Articles & Videos"),
+        gr.DataFrame(label="Nearby Wellness Professionals"),
+        gr.State()  # To maintain state (chat history)
     ],
-    title="Mental Health Chatbot",
-    description="This chatbot helps with mental health inquiries and provides suggestions for wellness professionals.",
-    live=True
+    live=True,
+    title="Mental Health Chatbot with Wellness Professional Search",
+    description="This chatbot provides mental health support with sentiment analysis, emotion detection, suggestions, and a list of nearby wellness professionals."
 )
 
-# Run the interface
-if __name__ == "__main__":
-    iface.launch(debug=True)
+# Launch the interface
+iface.launch(debug=True, share=True)