app.py

import nltk
import numpy as np
import random
import json
import pickle
import gradio as gr
import requests
import folium
from nltk.tokenize import word_tokenize
from nltk.stem.lancaster import LancasterStemmer
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import tensorflow as tf
import tflearn
import torch
import pandas as pd
import time
from bs4 import BeautifulSoup
import re  # Added for regex operations
import os

# Google Places API endpoint
url = "https://maps.googleapis.com/maps/api/place/textsearch/json"
places_details_url = "https://maps.googleapis.com/maps/api/place/details/json"

# Initialize necessary libraries for chatbot and NLP
nltk.download('punkt')
stemmer = LancasterStemmer()

# Load the chatbot intents file
with open("intents.json") as file:
    data = json.load(file)

# Load preprocessed data from pickle
with open("data.pickle", "rb") as f:
    words, labels, training, output = pickle.load(f)

# Build the chatbot model
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)
model = tflearn.DNN(net)
model.load("MentalHealthChatBotmodel.tflearn")

# Emotion and sentiment analysis model
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, emotion_model = load_model()

# Google Places API query function
def get_places_data(query, location, radius=5000, api_key="GOOGLE_API_KEY"):
    params = {
        "query": query,
        "location": location,
        "radius": radius,
        "key": api_key
    }
    response = requests.get(url, params=params)
    if response.status_code == 200:
        data = response.json()
        return data.get('results', [])
    else:
        return []

# Map generation function
def create_map(locations):
    m = folium.Map(location=[21.3, -157.8], zoom_start=12)
    for loc in locations:
        name = loc.get("name", "No Name")
        lat = loc['geometry']['location']['lat']
        lng = loc['geometry']['location']['lng']
        folium.Marker([lat, lng], popup=name).add_to(m)
    return m._repr_html_()  # Return HTML representation

# Sentiment Analysis function
def analyze_sentiment(user_input):
    tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
    model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
    inputs = tokenizer(user_input, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    predicted_class = torch.argmax(outputs.logits, dim=1).item()
    sentiment = ["Negative", "Neutral", "Positive"][predicted_class]
    return sentiment

# Chatbot function for user interaction
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)

def chatbot(message, history):
    history = history or []
    message = message.lower()
    try:
        results = model.predict([bag_of_words(message, words)])
        results_index = np.argmax(results)
        tag = labels[results_index]
        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

# Emotion Detection function
def detect_emotion(user_input):
    pipe = pipeline("text-classification", model=emotion_model, tokenizer=tokenizer)
    result = pipe(user_input)
    emotion = result[0]['label']
    return emotion

# 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

# Main Gradio interface for emotion detection and chatbot
def emotion_and_chatbot(user_input, history, query, location):
    # Emotion Detection
    emotion = detect_emotion(user_input)
    sentiment = analyze_sentiment(user_input)
    emotion_response = f"Emotion Detected: {emotion}. Sentiment: {sentiment}"
    
    # Search Places (for wellness or other queries)
    places_data = get_places_data(query, location)
    places_map = create_map(places_data) if places_data else "No places found."
    
    # Chatbot response
    history, _ = chatbot(user_input, history)
    
    return emotion_response, places_map, history, history

# Gradio interface setup
iface = gr.Interface(
    fn=emotion_and_chatbot,
    inputs=[
        gr.Textbox(label="Enter your message", placeholder="How are you feeling?"),
        "state",  # Chat history
        gr.Textbox(label="Search Query (e.g. wellness)", placeholder="e.g. therapist"),
        gr.Textbox(label="Location (latitude,longitude)", placeholder="e.g. 21.3,-157.8")
    ],
    outputs=[
        gr.Textbox(label="Emotion and Sentiment"),
        gr.HTML(label="Places Map"),
        gr.Chatbot(label="Chatbot History"),
        "state"
    ],
    title="Wellbeing Chatbot with Emotion Detection & Location Search",
    description="A chatbot that provides mental health support, analyzes emotions, and helps find wellness professionals near you."
)

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