app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline
import requests
import csv
import time
import re
from bs4 import BeautifulSoup
import pandas as pd
import chromedriver_autoinstaller
import os
import nltk
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import tflearn
import tensorflow as tf
import json
import pickle

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

# Import LancasterStemmer from nltk.stem
from nltk.stem import LancasterStemmer

# 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.")

# Define a PyTorch model with the same architecture as your tflearn model
class PyTorchModel(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, num_classes):
        super(PyTorchModel, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.fc1 = nn.Linear(embedding_dim, hidden_dim)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_dim, num_classes)

    def forward(self, x):
        out = self.embedding(x)
        out = torch.mean(out, dim=1)
        out = self.fc1(out)
        out = self.relu(out)
        out = self.fc2(out)
        return out

# Convert the tflearn model to a PyTorch model
vocab_size = len(words)
embedding_dim = 128
hidden_dim = 64
num_classes = len(labels)

# Load the TensorFlow model
model_tf = tflearn.DNN(tflearn.input_data(shape=[None, len(training[0])]))
model_tf.load("MentalHealthChatBotmodel.tflearn")

# Convert the TensorFlow model to a PyTorch model
pytorch_model = PyTorchModel(vocab_size, embedding_dim, hidden_dim, num_classes)

# Load weights from the TensorFlow model
layer_names = ['fc1/kernel', 'fc1/bias', 'fc2/kernel', 'fc2/bias']

for layer_name in layer_names:
    weight_tensor = getattr(model_tf, layer_name)
    pytorch_layer_name = layer_name.replace('/', '_')
    pytorch_model.state_dict()[pytorch_layer_name].copy_(torch.tensor(weight_tensor.eval(session=model_tf.trainer.session)))

# Move the model to the CPU
pytorch_model.cpu()

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

# 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"

# Google Geocoding API endpoint
geocoding_url = "https://maps.googleapis.com/maps/api/geocode/json"

# Your actual Google API Key (replace with your key)
api_key = "AIzaSyCcfJzMFfuv_1LN7JPTJJYw_aS0A_SLeW0"  # Replace with your own Google API key

# Search query for wellness professionals in Hawaii
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 OR integrative medicine OR chiropractor OR naturopath in Hawaii"

# 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 {}

# 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_reviews_total = place.get("user_reviews_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_div_for_contact_info(website)
                else:
                    email = "Not available"

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

                all_results.append([name, address, phone_number, rating, business_status,
                                    user_reviews_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 Reviews Total", "Website", "Types", "Latitude", "Longitude",
            "Opening Hours", "Reviews", "Email"
        ])
        writer.writerows(data)
    print(f"Data saved to {filename}")

# Geocoding function to convert location text to coordinates
def geocode_location(address):
    params = {
        "address": address,
        "key": api_key
    }
    response = requests.get(geocoding_url, params=params)

    if response.status_code == 200:
        data = response.json()
        if data['status'] == 'OK':
            location = data['results'][0]['geometry']['location']
            return location['lat'], location['lng']
        else:
            raise ValueError("Geocoding failed.")
    else:
        raise ValueError("Failed to retrieve geocoding data.")

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

    # User input for text (emotion detection)
    user_input_emotion = gr.Textbox(lines=1, label="How are you feeling today?")
    submit_emotion = gr.Button("Submit")

    # Model prediction for emotion detection
    def predict_emotion(text):
        inputs = tokenizer_sentiment(text, return_tensors="pt").to('cpu')
        with torch.no_grad():
            outputs = pytorch_model(inputs['input_ids'])
        _, predicted_class = torch.max(outputs, dim=1)
        emotion = labels[predicted_class.item()]
        return emotion

    # Show suggestions based on the detected emotion
    def show_suggestions(emotion):
        if emotion == 'joy':
            return "You're feeling happy! Keep up the great mood!\nUseful Resources:\n[Relaxation Techniques](https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation)\n[Dealing with Stress](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)\n[Emotional Wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)\n\nRelaxation Videos:\n[Watch on YouTube](https://youtu.be/m1vaUGtyo-A)"
        elif emotion == 'anger':
            return "You're feeling angry. It's okay to feel this way. Let's try to calm down.\nUseful Resources:\n[Emotional Wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)\n[Stress Management Tips](https://www.health.harvard.edu/health-a-to-z)\n[Dealing with Anger](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)\n\nRelaxation Videos:\n[Watch on YouTube](https://youtu.be/MIc299Flibs)"
        elif emotion == 'fear':
            return "You're feeling fearful. Take a moment to breathe and relax.\nUseful Resources:\n[Mindfulness Practices](https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation)\n[Coping with Anxiety](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)\n[Emotional Wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)\n\nRelaxation Videos:\n[Watch on YouTube](https://youtu.be/yGKKz185M5o)"
        elif emotion == 'sadness':
            return "You're feeling sad. It's okay to take a break.\nUseful Resources:\n[Emotional Wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)\n[Dealing with Anxiety](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)\n\nRelaxation Videos:\n[Watch on YouTube](https://youtu.be/-e-4Kx5px_I)"
        elif emotion == 'surprise':
            return "You're feeling surprised. It's okay to feel neutral!\nUseful Resources:\n[Managing Stress](https://www.health.harvard.edu/health-a-to-z)\n[Coping Strategies](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)\n\nRelaxation Videos:\n[Watch on YouTube](https://youtu.be/m1vaUGtyo-A)"

    emotion_output = gr.Textbox(label="Emotion Detected")
    submit_emotion.click(predict_emotion, inputs=user_input_emotion, outputs=emotion_output)

    # Button for summary
    def show_summary(emotion):
        return f"Emotion Detected: {emotion}\nUseful Resources based on your mood:\n{show_suggestions(emotion)}"

    summary_button = gr.Button("Show Summary")
    summary_output = gr.Textbox(label="Summary")
    summary_button.click(show_summary, inputs=emotion_output, outputs=summary_output)

    # Chatbot functionality
    chatbot = gr.Chatbot(label="Chat")
    message_input = gr.Textbox(lines=1, label="Message")
    submit_chat = gr.Button("Send")

    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

    submit_chat.click(chat, inputs=[message_input, gr.State()], outputs=[chatbot, gr.State()])

    # Location input for fetching nearby health professionals
    location_input = gr.Textbox(lines=1, label="Enter your location (plain English):")
    submit_location = gr.Button("Find Nearby Health Professionals")

    # Fetch and display nearby health professionals
    def fetch_nearby_health_professionals(location):
        try:
            lat, lon = geocode_location(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 OR integrative medicine OR chiropractor OR naturopath"
            radius = 50000  # 50 km radius

            all_results = get_all_places(query, f"{lat},{lon}", radius, api_key)
            if all_results:
                df = pd.DataFrame(all_results, columns=["Name", "Address", "Phone", "Rating", "Business Status", "User Reviews Total", "Website", "Types", "Latitude", "Longitude", "Opening Hours", "Reviews", "Email"])
                return df
            else:
                return "No data found."
        except Exception as e:
            return str(e)

    nearby_health_professionals_table = gr.Dataframe(headers=["Name", "Address", "Phone", "Rating", "Business Status", "User Reviews Total", "Website", "Types", "Latitude", "Longitude", "Opening Hours", "Reviews", "Email"])
    submit_location.click(fetch_nearby_health_professionals, inputs=location_input, outputs=nearby_health_professionals_table)

    # User input for text (sentiment analysis)
    user_input_sentiment = gr.Textbox(lines=1, label="Enter text to analyze sentiment:")
    submit_sentiment = gr.Button("Submit")

    # Prediction button for sentiment analysis
    def predict_sentiment(text):
        inputs = tokenizer_sentiment(text, 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 sentiment

    sentiment_output = gr.Textbox(label="Predicted Sentiment")
    submit_sentiment.click(predict_sentiment, inputs=user_input_sentiment, outputs=sentiment_output)

    # Button to fetch wellness professionals data
    fetch_button = gr.Button("Fetch Wellness professionals data")
    data_output = gr.Dataframe(headers=["Name", "Address", "Phone", "Rating", "Business Status", "User Reviews Total", "Website", "Types", "Latitude", "Longitude", "Opening Hours", "Reviews", "Email"])

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

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

# Launch Gradio interface
demo.launch()