app.py

import os
import gradio as gr
import nltk
import numpy as np
import tflearn
import random
import json
import pickle
from nltk.tokenize import word_tokenize
from nltk.stem.lancaster import LancasterStemmer
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import googlemaps
import folium
import torch

# Disable GPU usage for TensorFlow
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'

# Download NLTK resources
nltk.download("punkt")

# Initialize Lancaster Stemmer
stemmer = LancasterStemmer()

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

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

# Build the chatbot's neural network 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)
chatbot_model = tflearn.DNN(net)
chatbot_model.load("MentalHealthChatBotmodel.tflearn")

# Model for sentiment detection
tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")

# Model for emotion detection
tokenizer_emotion = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
model_emotion = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")

# Google Maps API client
gmaps = googlemaps.Client(key=os.getenv('GOOGLE_API_KEY'))

# Chatbot logic
def bag_of_words(s, words):
    bag = [0] * len(words)
    s_words = word_tokenize(s)
    s_words = [stemmer.stem(word.lower()) for word in s_words if word.isalnum()]
    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):
    """Generate chatbot response and append to history (as tuples)."""
    history = history or []
    try:
        results = chatbot_model.predict([bag_of_words(message, words)])
        tag = labels[np.argmax(results)]
        response = "I'm not sure how to respond to that. 🤔"
        for intent in intents_data["intents"]:
            if intent["tag"] == tag:
                response = random.choice(intent["responses"])
                break
    except Exception as e:
        response = f"Error: {str(e)} 💥"

    # Append the message and response as a tuple
    history.append((message, response))
    return history, response

# Sentiment analysis
def analyze_sentiment(user_input):
    inputs = tokenizer_sentiment(user_input, return_tensors="pt")
    with torch.no_grad():
        outputs = model_sentiment(**inputs)
    sentiment_class = torch.argmax(outputs.logits, dim=1).item()
    sentiment_map = ["Negative 😔", "Neutral 😐", "Positive 😊"]
    return sentiment_map[sentiment_class]

# Emotion detection
def detect_emotion(user_input):
    pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion)
    result = pipe(user_input)
    emotion = result[0]["label"]
    return emotion

# Generate suggestions based on detected emotion
def generate_suggestions(emotion):
    suggestions = {
        "joy": [
            ["Relaxation Techniques", '<a href="https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation" target="_blank">Visit</a>'],
            ["Dealing with Stress", '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Visit</a>'],
            ["Emotional Wellness Toolkit", '<a href="https://www.nih.gov/health-information/emotional-wellness-toolkit" target="_blank">Visit</a>'],
            ["Relaxation Video", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>'],
        ],
        "anger": [
            ["Emotional Wellness Toolkit", '<a href="https://www.nih.gov/health-information/emotional-wellness-toolkit" target="_blank">Visit</a>'],
            ["Stress Management Tips", '<a href="https://www.health.harvard.edu/health-a-to-z" target="_blank">Visit</a>'],
            ["Dealing with Anger", '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Visit</a>'],
            ["Relaxation Video", '<a href="https://youtu.be/MIc299Flibs" target="_blank">Watch</a>'],
        ],
    }
    return suggestions.get(emotion, [["No suggestions available", ""]])

# Search professionals and generate map
def get_health_professionals_and_map(location, query):
    try:
        geo_location = gmaps.geocode(location)
        if geo_location:
            lat, lng = geo_location[0]["geometry"]["location"].values()
            places_result = gmaps.places_nearby(location=(lat, lng), radius=10000, keyword=query)["results"]

            map_ = folium.Map(location=(lat, lng), zoom_start=13)
            professionals = []
            for place in places_result:
                professionals.append(f"{place['name']} - {place.get('vicinity', '')}")
                folium.Marker([place["geometry"]["location"]["lat"], place["geometry"]["location"]["lng"]],
                              popup=place["name"]).add_to(map_)
            return professionals, map_._repr_html_()
        return ["No professionals found"], ""
    except Exception as e:
        return [f"Error: {e}"], ""

# Main app function
def app_function(message, location, query, history):
    chatbot_history, _ = chatbot(message, history)  # Generate chatbot response
    sentiment = analyze_sentiment(message)          # Detect sentiment
    emotion = detect_emotion(message.lower())       # Detect emotion
    suggestions = generate_suggestions(emotion)     # Generate suggestions
    professionals, map_html = get_health_professionals_and_map(location, query)  # Find professionals & map
    return chatbot_history, sentiment, emotion, suggestions, professionals, map_html

# Custom CSS for Visual Styling
custom_css = """
body {
    background: linear-gradient(135deg, #000000, #ff5722);
    color: white;
    font-family: 'Arial', sans-serif;
}
button {
    background-color: #ff5722 !important;
    border: none !important;
    color: white !important;
    padding: 12px 20px;
    font-size: 16px;
    border-radius: 8px;
    cursor: pointer;
    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.3);
}
button:hover {
    background-color: #e64a19 !important;
}
textarea, input[type="text"] {
    background: rgba(255, 255, 255, 0.1) !important;
    color: white !important;
    border: 2px solid #ff5722 !important;
    padding: 12px !important;
    border-radius: 8px !important;
    font-size: 14px;
}
#components-container {
    margin-top: 20px;
}
.gradio-container {
    padding: 16px !important;
    box-shadow: 0px 12px 24px rgba(0, 0, 0, 0.6);
}
"""

# Gradio Interface
with gr.Blocks(css=custom_css) as app:
    gr.Markdown("# 🌟 Well-Being Companion")
    gr.Markdown("Empowering Your Mental Health Journey 💚")

    with gr.Row():
        user_message = gr.Textbox(label="Your Message", placeholder="Enter your message...")
        user_location = gr.Textbox(label="Your Location", placeholder="Enter your location...")
        search_query = gr.Textbox(label="Query", placeholder="Search for professionals...")
        submit_btn = gr.Button("Submit")

    chatbot_box = gr.Chatbot(label="Chat History")  # Corrected history format (list of tuples)
    emotion_output = gr.Textbox(label="Detected Emotion")
    sentiment_output = gr.Textbox(label="Detected Sentiment")
    suggestions_output = gr.DataFrame(headers=["Title", "Links"], label="Suggestions")
    map_output = gr.HTML(label="Nearby Professionals Map")
    professional_display = gr.Textbox(label="Nearby Professionals", lines=5)

    submit_btn.click(
        app_function,
        inputs=[user_message, user_location, search_query, chatbot_box],
        outputs=[
            chatbot_box, sentiment_output, emotion_output,
            suggestions_output, professional_display, map_output,
        ],
    )

app.launch()