app.py

import nltk
import numpy as np
import tflearn
import tensorflow
import random
import json
import pickle
import gradio as gr
from nltk.tokenize import word_tokenize
from nltk.stem.lancaster import LancasterStemmer
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import torch
import requests
import pandas as pd
import time
from bs4 import BeautifulSoup
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 (Chatbot)
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

# Sentiment Analysis (Code 2)
tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")

def analyze_sentiment(user_input):
    inputs = tokenizer_sentiment(user_input, return_tensors="pt")
    with torch.no_grad():
        outputs = model_sentiment(**inputs)
    predicted_class = torch.argmax(outputs.logits, dim=1).item()
    sentiment = ["Negative", "Neutral", "Positive"][predicted_class]
    return f"**Predicted Sentiment:** {sentiment}"

# Emotion Detection (Code 3)
tokenizer_emotion = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
model_emotion = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion)

def detect_emotion(user_input):
    result = pipe(user_input)
    emotion = result[0]['label']
    return emotion

def provide_suggestions(emotion):
    suggestions = ""
    if emotion == 'joy':
        suggestions += "You're feeling happy! Keep up the great mood!"
    elif emotion == 'anger':
        suggestions += "You're feeling angry. It's okay to feel this way."
    elif emotion == 'fear':
        suggestions += "You're feeling fearful. Take a moment to breathe."
    elif emotion == 'sadness':
        suggestions += "You're feeling sad. It's okay to take a break."
    elif emotion == 'surprise':
        suggestions += "You're feeling surprised. It's okay to feel neutral!"
    return suggestions

# Google Places API (Code 4)
api_key = "YOUR_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,
        "location": location,
        "radius": radius,
        "key": api_key
    }
    if next_page_token:
        params["pagetoken"] = next_page_token
    response = requests.get(url, params=params)
    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

# Search Wellness Professionals
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.to_csv(index=False)
    else:
        return "No data found."

# Gradio Interface
def gradio_interface(message, location, history):
    # 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 history, sentiment, emotion, suggestions, wellness_results

# Gradio interface setup
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"),
        "state"
    ],
    outputs=[
        gr.Chatbot(label="Chat History"),
        gr.Textbox(label="Sentiment Analysis"),
        gr.Textbox(label="Detected Emotion"),
        gr.Textbox(label="Suggestions"),
        gr.File(label="Download Wellness Professionals CSV")
    ],
    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.",
)

# Launch Gradio interface
if __name__ == "__main__":
    iface.launch(debug=True)