Update app.py

app.py

@@ -6,18 +6,13 @@ import numpy as np
 import tflearn
 import gradio as gr
 import requests
-import time
-from bs4 import BeautifulSoup
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
 import torch
 import pandas as pd
-import os
-import chromedriver_autoinstaller
-import streamlit as st  # Add Streamlit import
+from bs4 import BeautifulSoup
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
 from nltk.tokenize import word_tokenize
 from nltk.stem.lancaster import LancasterStemmer
+import os
 
 # Ensure necessary NLTK resources are downloaded
 nltk.download('punkt')
@@ -96,7 +91,6 @@ tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-
 model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 
 # Emotion detection setup
-@st.cache_resource
 def load_emotion_model():
     tokenizer = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
     model = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
@@ -109,18 +103,6 @@ url = "https://maps.googleapis.com/maps/api/place/textsearch/json"
 places_details_url = "https://maps.googleapis.com/maps/api/place/details/json"
 api_key = os.getenv("GOOGLE_API_KEY")  # Use environment variable for security
 
-# Install Chrome and Chromedriver for web scraping
-def install_chrome_and_driver():
-    os.system("apt-get update")
-    os.system("apt-get install -y wget curl")
-    os.system("wget -q https://dl.google.com/linux/direct/google-chrome-stable_current_amd64.deb")
-    os.system("dpkg -i google-chrome-stable_current_amd64.deb")
-    os.system("apt-get install -y -f")
-    os.system("google-chrome-stable --version")
-    chromedriver_autoinstaller.install()
-
-install_chrome_and_driver()
-
 # Function to get places data using Google Places API
 def get_places_data(query, location, radius, api_key, next_page_token=None):
     params = {
@@ -139,10 +121,38 @@ def get_places_data(query, location, radius, api_key, next_page_token=None):
     else:
         return None
 
+# Web scraping function to get wellness professional data (alternative to API)
+def scrape_wellness_professionals(query, location):
+    # User-Agent header to simulate a browser request
+    headers = {
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+    }
+    
+    search_url = f"https://www.google.com/search?q={query}+near+{location}"
+    
+    # Make a request to the search URL with headers
+    response = requests.get(search_url, headers=headers)
+    if response.status_code == 200:
+        soup = BeautifulSoup(response.text, 'html.parser')
+        
+        # Parse and extract wellness professionals from the HTML
+        wellness_data = []
+        results = soup.find_all("div", class_="BVG0Nb")  # Adjust class based on the actual HTML structure
+        for result in results:
+            name = result.get_text()
+            link = result.find("a")["href"] if result.find("a") else None
+            wellness_data.append([name, link])
+        
+        return wellness_data
+    else:
+        return []
+
 # Main function to fetch wellness professional data and display on map
 def get_wellness_professionals(location):
     query = "therapist OR counselor OR mental health professional OR marriage and family therapist OR psychotherapist OR psychiatrist OR psychologist OR nutritionist OR wellness doctor OR holistic practitioner OR integrative medicine OR chiropractor OR naturopath"
     radius = 50000  # 50 km radius
+    
+    # Using Google Places API if available
     data = get_places_data(query, location, radius, api_key)
 
     if data:
@@ -155,7 +165,9 @@ def get_wellness_professionals(location):
             longitude = place.get("geometry", {}).get("location", {}).get("lng")
             wellness_data.append([name, address, latitude, longitude])
         return wellness_data
-    return []
+
+    # Fallback to scraping if API is not available or fails
+    return scrape_wellness_professionals(query, location)
 
 # Gradio interface setup for user interaction
 def user_interface(message, location, history):