import gradio as gr
import nltk
import numpy as np
import tflearn
import random
import json
import pickle
import torch
from nltk.tokenize import word_tokenize
from nltk.stem.lancaster import LancasterStemmer
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import requests
import re
from bs4 import BeautifulSoup
import time
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, state):
    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?"

        # Add emoticons to the response
        emoticon_dict = {
            "joy": "😊",
            "anger": "😡",
            "fear": "😨",
            "sadness": "😔",
            "surprise": "😲",
            "neutral": "😐"
        }

        # Add the emotion-related emoticon to the response
        for tg in data["intents"]:
            if tg['tag'] == tag:
                emotion = tg.get('emotion', 'neutral')  # Default to neutral if no emotion is defined
                response = f"{response} {emoticon_dict.get(emotion, '😐')}"
                break
        
        history.append((message, response))

        # Transition to the next feature (sentiment analysis)
        state['step'] = 2  # Move to sentiment analysis
    except Exception as e:
        response = f"An error occurred: {str(e)}"

    return history, history, state

# Load pre-trained model and tokenizer for sentiment analysis
tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
sentiment_model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")

# Function for sentiment analysis
def analyze_sentiment(text, state):
    inputs = tokenizer(text, return_tensors="pt")
    with torch.no_grad():
        outputs = sentiment_model(**inputs)
    predicted_class = torch.argmax(outputs.logits, dim=1).item()
    sentiment = ["Negative", "Neutral", "Positive"][predicted_class]
    
    # Add emoticon to sentiment
    sentiment_emojis = {
        "Negative": "😞",
        "Neutral": "😐",
        "Positive": "😊"
    }
    sentiment_with_emoji = f"{sentiment} {sentiment_emojis.get(sentiment, '😐')}"
    
    # Transition to emotion detection
    state['step'] = 3  # Move to emotion detection and suggestions
    return sentiment_with_emoji, state

# Load pre-trained model and tokenizer for emotion detection
emotion_tokenizer = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
emotion_model = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")

# Function for emotion detection and suggestions
def detect_emotion(text, state):
    pipe = pipeline("text-classification", model=emotion_model, tokenizer=emotion_tokenizer)
    result = pipe(text)
    emotion = result[0]['label']
    
    # Provide suggestions based on detected emotion
    suggestions = provide_suggestions(emotion)
    
    # Transition to wellness professional search
    state['step'] = 4  # Move to wellness professional search
    return emotion, suggestions, state

# Suggestions based on detected emotion
def provide_suggestions(emotion):
    resources = {
        'joy': {
            'message': "You're feeling happy! Keep up the great mood! 😊",
            'articles': [
                "[Relaxation Techniques](https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation)",
                "[Dealing with Stress](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)"
            ],
            'videos': "[Watch Relaxation Video](https://youtu.be/m1vaUGtyo-A)"
        },
        'anger': {
            'message': "You're feeling angry. It's okay to feel this way. Let's try to calm down. 😡",
            'articles': [
                "[Emotional Wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)",
                "[Stress Management Tips](https://www.health.harvard.edu/health-a-to-z)"
            ],
            'videos': "[Watch Anger Management Video](https://youtu.be/MIc299Flibs)"
        },
        'fear': {
            'message': "You're feeling fearful. Take a moment to breathe and relax. 😨",
            'articles': [
                "[Mindfulness Practices](https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation)",
                "[Coping with Anxiety](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)"
            ],
            'videos': "[Watch Coping Video](https://youtu.be/yGKKz185M5o)"
        },
        'sadness': {
            'message': "You're feeling sad. It's okay to take a break. 😔",
            'articles': [
                "[Emotional Wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)",
                "[Dealing with Anxiety](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)"
            ],
            'videos': "[Watch Sadness Relief Video](https://youtu.be/-e-4Kx5px_I)"
        },
        'surprise': {
            'message': "You're feeling surprised. It's okay to feel neutral! 😲",
            'articles': [
                "[Managing Stress](https://www.health.harvard.edu/health-a-to-z)",
                "[Coping Strategies](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)"
            ],
            'videos': "[Watch Stress Relief Video](https://youtu.be/m1vaUGtyo-A)"
        }
    }
    return resources.get(emotion, {'message': "Stay calm. 🙂", 'articles': [], 'videos': []})

# Function to find wellness professionals
def find_wellness_professionals(location, state):
    query = "therapist OR counselor OR mental health professional OR marriage and family therapist OR psychotherapist OR psychiatrist OR psychologist in " + location
    api_key = "YOUR_GOOGLE_API_KEY"  # Replace with your own API key
    location_coords = "21.3,-157.8"  # Default to Oahu, Hawaii
    radius = 50000  # 50 km radius
    
    google_places_data = get_all_places(query, location_coords, radius, api_key)
    if google_places_data:
        df = pd.DataFrame(google_places_data, columns=[
            "Name", "Address", "Phone", "Rating", "Business Status",
            "User Ratings Total", "Website", "Types", "Latitude", "Longitude",
            "Opening Hours", "Reviews", "Email"
        ])
        # Display results in Gradio interface
        if not df.empty:
            df_html = df.to_html(classes="table table-striped", index=False)
            return f"Found wellness professionals in your area: \n{df_html}", state
        else:
            return "No wellness professionals found for your location. Try another search.", state
    else:
        return "Sorry, there was an issue fetching data. Please try again later.", state

# Function to fetch places data using Google Places API
def get_all_places(query, location, radius, api_key):
    url = f"https://maps.googleapis.com/maps/api/place/textsearch/json?query={query}&location={location}&radius={radius}&key={api_key}"
    response = requests.get(url)
    if response.status_code == 200:
        results = response.json().get("results", [])
        places = []
        for place in results:
            name = place.get("name")
            address = place.get("formatted_address")
            phone = place.get("formatted_phone_number", "Not available")
            rating = place.get("rating", "Not rated")
            business_status = place.get("business_status", "N/A")
            user_ratings_total = place.get("user_ratings_total", "N/A")
            website = place.get("website", "Not available")
            types = place.get("types", [])
            lat, lng = place.get("geometry", {}).get("location", {}).values()
            opening_hours = place.get("opening_hours", {}).get("weekday_text", [])
            reviews = place.get("reviews", [])
            email = "Not available"  # Assume email is not included in the API response
            
            # Adding the place data to the list
            places.append([name, address, phone, rating, business_status, user_ratings_total,
                           website, types, lat, lng, opening_hours, reviews, email])
        return places
    else:
        return []

# Gradio interface setup
def gradio_interface():
    with gr.Blocks() as demo:
        # Set title and description
        gr.Markdown("<h1 style='text-align: center;'>Mental Health Support Chatbot 🤖</h1>")
        gr.Markdown("<p style='text-align: center;'>Get emotional well-being suggestions and find wellness professionals nearby.</p>")
        
        # State to manage step transitions
        state = gr.State({"step": 1})
        
        # Chat interface
        with gr.Row():
            chatbot = gr.Chatbot(label="Chatbot")
            user_input = gr.Textbox(placeholder="Type your message here...", label="Your Message")
            send_button = gr.Button("Send")
        
        # Output for emotion, sentiment, suggestions
        with gr.Row():
            sentiment_output = gr.Textbox(label="Sentiment Analysis")
            emotion_output = gr.Textbox(label="Emotion Detection")
            suggestions_output = gr.Textbox(label="Suggestions")
        
        # Input for location for wellness professionals
        with gr.Row():
            location_input = gr.Textbox(label="Your Location (City/Region)", placeholder="Enter your city...")
            search_button = gr.Button("Search Wellness Professionals")
        
        # Button actions
        send_button.click(chat, inputs=[user_input, chatbot, state], outputs=[chatbot, chatbot, state])
        user_input.submit(chat, inputs=[user_input, chatbot, state], outputs=[chatbot, chatbot, state])
        
        send_button.click(analyze_sentiment, inputs=[user_input, state], outputs=[sentiment_output, state])
        user_input.submit(analyze_sentiment, inputs=[user_input, state], outputs=[sentiment_output, state])
        
        send_button.click(detect_emotion, inputs=[user_input, state], outputs=[emotion_output, suggestions_output, state])
        user_input.submit(detect_emotion, inputs=[user_input, state], outputs=[emotion_output, suggestions_output, state])
        
        search_button.click(find_wellness_professionals, inputs=[location_input, state], outputs=[suggestions_output, state])
    
    demo.launch()

# Run the Gradio interface
if __name__ == "__main__":
    gradio_interface()