Update app.py

app.py

@@ -1,23 +1,23 @@
 import gradio as gr
-import requests
-import time
-import re
-import csv
-import json
-import random
 import nltk
 import numpy as np
 import tflearn
-import os
+import random
+import json
 import pickle
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-from bs4 import BeautifulSoup
-import chromedriver_autoinstaller
-import pandas as pd
+import torch
 from nltk.tokenize import word_tokenize
 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')
@@ -25,14 +25,14 @@ nltk.download('punkt')
 # Initialize the stemmer
 stemmer = LancasterStemmer()
 
-# Load intents.json (directly in the app directory)
+# Load intents.json
 try:
     with open("intents.json") as file:
         data = json.load(file)
 except FileNotFoundError:
-    raise FileNotFoundError("Error: 'intents.json' file not found in the app directory.")
+    raise FileNotFoundError("Error: 'intents.json' file not found. Ensure it exists in the current directory.")
 
-# Load preprocessed data from pickle (directly in the app directory)
+# Load preprocessed data from pickle
 try:
     with open("data.pickle", "rb") as f:
         words, labels, training, output = pickle.load(f)
@@ -46,12 +46,12 @@ net = tflearn.fully_connected(net, 8)
 net = tflearn.fully_connected(net, len(output[0]), activation="softmax")
 net = tflearn.regression(net)
 
-# Load the trained model (directly in the app directory)
+# Load the trained model
 model = tflearn.DNN(net)
 try:
     model.load("MentalHealthChatBotmodel.tflearn")
 except FileNotFoundError:
-    raise FileNotFoundError("Error: Trained model file 'MentalHealthChatBotmodel.tflearn' not found in the app directory.")
+    raise FileNotFoundError("Error: Trained model file 'MentalHealthChatBotmodel.tflearn' not found.")
 
 # Function to process user input into a bag-of-words format
 def bag_of_words(s, words):
@@ -68,7 +68,7 @@ def bag_of_words(s, words):
 def chat(message, history):
     history = history or []
     message = message.lower()
-
+    
     try:
         # Predict the tag
         results = model.predict([bag_of_words(message, words)])
@@ -85,7 +85,7 @@ def chat(message, history):
             response = "I'm sorry, I didn't understand that. Could you please rephrase?"
     except Exception as e:
         response = f"An error occurred: {str(e)}"
-
+    
     history.append((message, response))
     return history, history
 
@@ -153,26 +153,13 @@ def provide_suggestions(emotion):
             "Article URL": "https://www.health.harvard.edu/health-a-to-z",
             "Video URL": "https://youtu.be/m1vaUGtyo-A"
         }, ignore_index=True)
-
+        
     return suggestions
 
 # Google Places API to get nearby wellness professionals
-api_key = "YOUR_GOOGLE_API_KEY"  # Replace with your actual API key
-
-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 Chromedriver
-install_chrome_and_driver()
-
-# Fetch places data using Google Places API
-def get_places_data(query, location, radius, api_key):
+api_key = "GOOGLE_API_KEY"  # Replace with your 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"
     params = {
         "query": query,
@@ -180,48 +167,62 @@ def get_places_data(query, location, radius, api_key):
         "radius": radius,
         "key": api_key
     }
+    if next_page_token:
+        params["pagetoken"] = next_page_token
     response = requests.get(url, params=params)
-    if response.status_code == 200:
-        return response.json()
-    return None
-
-# Scrape website URL from Google Maps results (using Selenium)
-def scrape_website_from_google_maps(place_name):
-    chrome_options = Options()
-    chrome_options.add_argument("--headless")
-    chrome_options.add_argument("--no-sandbox")
-    chrome_options.add_argument("--disable-dev-shm-usage")
-    driver = webdriver.Chrome(options=chrome_options)
-    search_url = f"https://www.google.com/maps/search/{place_name.replace(' ', '+')}"
-    driver.get(search_url)
-    time.sleep(5)
-    try:
-        website_element = driver.find_element_by_xpath('//a[contains(@aria-label, "Visit") and contains(@aria-label, "website")]')
-        website_url = website_element.get_attribute('href')
-    except:
-        website_url = "Not available"
-    driver.quit()
-    return website_url
-
-# Get all wellness professionals based on the location
-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"
-    radius = 50000  # 50 km radius
-    data = get_places_data(query, location, radius, api_key)
-    if data:
-        results = data.get('results', [])
-        wellness_data = []
-        for place in results:
-            name = place.get('name')
-            address = place.get('formatted_address')
-            website = place.get('website', 'Not available')
-            if website == 'Not available':
-                website = scrape_website_from_google_maps(name)
-            wellness_data.append([name, address, website])
-        return pd.DataFrame(wellness_data, columns=["Name", "Address", "Website"])
-    return pd.DataFrame()
-
-# Gradio Interface Setup
+    return response.json() if response.status_code == 200 else None
+
+def get_all_places(query, location, radius, api_key):
+    all_results = []
+    next_page_token = None
+    while True:
+        data = get_places_data(query, location, radius, api_key, next_page_token)
+        if data:
+            results = data.get('results', [])
+            for place in results:
+                place_id = place.get("place_id")
+                name = place.get("name")
+                address = place.get("formatted_address")
+                website = place.get("website", "Not available")
+                all_results.append([name, address, website])
+            next_page_token = data.get('next_page_token')
+            if not next_page_token:
+                break
+        else:
+            break
+    return all_results
+
+def search_wellness_professionals(location):
+    query = "therapist OR counselor OR mental health professional"
+    radius = 50000
+    google_places_data = get_all_places(query, location, radius, api_key)
+    if google_places_data:
+        df = pd.DataFrame(google_places_data, columns=["Name", "Address", "Website"])
+        return df
+    else:
+        return pd.DataFrame([["No data found.", "", ""]], columns=["Name", "Address", "Website"])
+
+# Gradio Interface
+def gradio_interface(message, location, state):
+    history = state or []  # If state is None, initialize it as an empty list
+    
+    # Stage 1: Mental Health Chatbot
+    history, _ = chat(message, history)
+    
+    # Stage 2: Sentiment Analysis
+    sentiment = analyze_sentiment(message)
+    
+    # Stage 3: Emotion Detection and Suggestions
+    emotion = detect_emotion(message)
+    suggestions = provide_suggestions(emotion)
+    
+    # Stage 4: Search for Wellness Professionals
+    wellness_results = search_wellness_professionals(location)
+    
+    # Return the results in a tabular form within the Gradio interface
+    return history, sentiment, emotion, suggestions, wellness_results, history  # Last 'history' is for state
+
+# Gradio interface setup
 iface = gr.Interface(
     fn=gradio_interface,
     inputs=[
@@ -230,16 +231,17 @@ iface = gr.Interface(
         gr.State()  # One state input
     ],
     outputs=[
-        gr.Chatbot(label="Chat History", type="messages"),  # Set type="messages"
-        gr.Textbox(label="Sentiment Analysis"),
-        gr.Textbox(label="Detected Emotion"),
-        gr.Dataframe(label="Suggestions & Resources"),
-        gr.Dataframe(label="Nearby Wellness Professionals"),
-        gr.State()  # One state output
+        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)
     ],
-    allow_flagging="never",
-    title="Mental Wellbeing App with AI Assistance",
-    description="This app provides a mental health chatbot, sentiment analysis, emotion detection, and wellness professional search functionality.",
+    title="Mental Health Chatbot",
+    description="This chatbot helps with mental health inquiries and provides suggestions for wellness professionals.",
+    live=True
 )
 
-iface.launch(debug=True, share=True)  # Launch with share=True to create a public link
+# Run the interface
+if __name__ == "__main__":
+    iface.launch(debug=True)