app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import nltk
import numpy as np
import tflearn
import tensorflow as tf
import random
import json
import pickle
from nltk.tokenize import word_tokenize
from nltk.stem.lancaster import LancasterStemmer
import requests
import csv
import time
import re
from bs4 import BeautifulSoup
import pandas as pd
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
import chromedriver_autoinstaller
import os

# Ensure necessary NLTK resources are downloaded
nltk.download('punkt')

# Initialize the stemmer
stemmer = LancasterStemmer()

# Load intents.json
try:
    with open("intents.json") as file:
        data = json.load(file)
except FileNotFoundError:
    raise FileNotFoundError("Error: 'intents.json' file not found. Ensure it exists in the current directory.")

# Load preprocessed data from pickle
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. Ensure it exists and matches the model.")

# Build the model structure
net = tflearn.input_data(shape=[None, len(training[0])])
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, len(output[0]), activation="softmax")
net = tflearn.regression(net)

# 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.")

# Function to process user input into a bag-of-words format
def bag_of_words(s, words):
    bag = [0 for _ in range(len(words))]
    s_words = word_tokenize(s)
    s_words = [stemmer.stem(word.lower()) for word in s_words if word.lower() in words]
    for se in s_words:
        for i, w in enumerate(words):
            if w == se:
                bag[i] = 1
    return np.array(bag)

# Chat function
def chat(message, history):
    history = history or []
    message = message.lower()

    try:
        # Predict the tag
        results = model.predict([bag_of_words(message, words)])
        results_index = np.argmax(results)
        tag = labels[results_index]

        # Match tag with intent and choose a random response
        for tg in data["intents"]:
            if tg['tag'] == tag:
                responses = tg['responses']
                response = random.choice(responses)
                break
        else:
            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

# Function to send a request to Google Places API and fetch places data
def get_places_data(query, location, radius, api_key, next_page_token=None):
    params = {
        "query": query,
        "location": location,
        "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()
    else:
        return None

# Function to fetch detailed information for a specific place using its place_id
def get_place_details(place_id, api_key):
    details_url = places_details_url
    params = {
        "place_id": place_id,
        "key": api_key
    }
    response = requests.get(details_url, params=params)

    if response.status_code == 200:
        details_data = response.json().get("result", {})
        return {
            "opening_hours": details_data.get("opening_hours", {}).get("weekday_text", "Not available"),
            "reviews": details_data.get("reviews", "Not available"),
            "phone_number": details_data.get("formatted_phone_number", "Not available"),
            "website": details_data.get("website", "Not available")
        }
    else:
        return {}

# 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

# Scraping the website to extract phone number or email
def scrape_website_for_contact_info(website):
    phone_number = "Not available"
    email = "Not available"

    try:
        response = requests.get(website, timeout=5)
        soup = BeautifulSoup(response.content, 'html.parser')

        phone_match = re.search(r'\(?\+?[0-9]*\)?[0-9_\- \(\)]*', soup.get_text())
        if phone_match:
            phone_number = phone_match.group()

        email_match = re.search(r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}', soup.get_text())
        if email_match:
            email = email_match.group()

    except Exception as e:
        print(f"Error scraping website {website}: {e}")

    return phone_number, email

# Function to fetch all places data including pagination
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', [])
            if not results:
                break

            for place in results:
                place_id = place.get("place_id")
                name = place.get("name")
                address = place.get("formatted_address")
                rating = place.get("rating", "Not available")
                business_status = place.get("business_status", "Not available")
                user_ratings_total = place.get("user_ratings_total", "Not available")
                website = place.get("website", "Not available")
                types = ", ".join(place.get("types", []))
                location = place.get("geometry", {}).get("location", {})
                latitude = location.get("lat", "Not available")
                longitude = location.get("lng", "Not available")

                details = get_place_details(place_id, api_key)
                phone_number = details.get("phone_number", "Not available")
                if phone_number == "Not available" and website != "Not available":
                    phone_number, email = scrape_website_for_contact_info(website)
                else:
                    email = "Not available"

                if website == "Not available":
                    website = scrape_website_from_google_maps(name)

                all_results.append([name, address, phone_number, rating, business_status,
                                    user_ratings_total, website, types, latitude, longitude,
                                    details.get("opening_hours", "Not available"),
                                    details.get("reviews", "Not available"), email])

            next_page_token = data.get('next_page_token')
            if not next_page_token:
                break

            time.sleep(2)
        else:
            break

    return all_results

# Function to save results to CSV file
def save_to_csv(data, filename):
    with open(filename, mode='w', newline='', encoding='utf-8') as file:
        writer = csv.writer(file)
        writer.writerow(["Name", "Address", "Phone", "Rating", "Business Status", "User Ratings Total", "Website", "Types", "Latitude", "Longitude", "Opening Hours", "Reviews", "Email"])
        writer.writerows(data)
    print(f"Data saved to {filename}")

# Main function to execute script
def main():
    google_places_data = get_all_places(query, location, radius, api_key)
    if google_places_data:
        save_to_csv(google_places_data, "wellness_professionals_hawaii.csv")
    else:
        print("No data found.")

# Gradio UI setup
with gr.Blocks() as demo:
    # Load pre-trained model and tokenizer
    @gr.cache_resource
    def load_model():
        tokenizer = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
        model = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
        return tokenizer, model

    tokenizer, model = load_model()

    # Display header
    gr.Markdown("# Emotion Detection and Well-Being Suggestions")

    # User input for text (emotion detection)
    user_input = gr.Textbox(lines=1, label="How are you feeling today?")
    emotion_output = gr.Textbox(label="Emotion Detected")

    # Model prediction
    def predict_emotion(text):
        pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
        result = pipe(text)
        emotion = result[0]['label']
        return emotion

    user_input.change(predict_emotion, inputs=user_input, outputs=emotion_output)

    # Chatbot functionality
    chatbot = gr.Chatbot(label="Chat")
    message_input = gr.Textbox(lines=1, label="Message")
    history_state = gr.State([])

    def chat(message, history):
        history = history or []
        message = message.lower()

        try:
            # Predict the tag
            results = model.predict([bag_of_words(message, words)])
            results_index = np.argmax(results)
            tag = labels[results_index]

            # Match tag with intent and choose a random response
            for tg in data["intents"]:
                if tg['tag'] == tag:
                    responses = tg['responses']
                    response = random.choice(responses)
                    break
            else:
                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

    message_input.submit(chat, inputs=[message_input, history_state], outputs=[chatbot, history_state])

    # Button to fetch wellness professionals data
    fetch_button = gr.Button("Fetch Wellness Professionals Data")
    data_output = gr.File(label="Download Data")

    def fetch_data():
        all_results = get_all_places(query, location, radius, api_key)
        if all_results:
            df = pd.DataFrame(all_results, columns=["Name", "Address", "Phone", "Rating", "Business Status", "User Ratings Total", "Website", "Types", "Latitude", "Longitude", "Opening Hours", "Reviews", "Email"])
            csv_file = df.to_csv(index=False)
            return csv_file
        else:
            return "No data found."

    fetch_button.click(fetch_data, inputs=None, outputs=data_output)

# Launch Gradio interface
demo.launch()