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

# Suppress TensorFlow's GPU usage and warnings
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"

# Download necessary NLTK resources
nltk.download("punkt")
stemmer = LancasterStemmer()

# Load intents and chatbot 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 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")

# Sentiment and Emotion Detection Models
tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")

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"))

# Helper Functions
def bag_of_words(s, words):
    """Convert user input to bag-of-words vector."""
    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 chat history."""
    history = history or []
    try:
        result = chatbot_model.predict([bag_of_words(message, words)])
        tag = labels[np.argmax(result)]
        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: {e}"
    history.append((message, response))
    return history, response

def analyze_sentiment(user_input):
    """Analyze sentiment from 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]

def detect_emotion(user_input):
    """Detect user emotion with emoji representation."""
    pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion)
    result = pipe(user_input)
    emotion = result[0]["label"].lower()
    emotion_map = {
        "joy": "😊 Joy",
        "anger": "😠 Anger",
        "sadness": "😢 Sadness",
        "fear": "😨 Fear",
        "surprise": "😲 Surprise",
        "neutral": "😐 Neutral",
    }
    return emotion_map.get(emotion, "Unknown 🤔")

def generate_suggestions(emotion):
    """Provide suggestions based on the detected emotion."""
    suggestions = {
        "joy": [
            ["Relaxation Techniques", '<a href="https://www.helpguide.org/mental-health/meditation" target="_blank">Visit</a>'],
            ["Dealing with Stress", '<a href="https://www.helpguide.org/mental-health/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": [
            ["Stress Management Tips", '<a href="https://www.health.harvard.edu" target="_blank">Visit</a>'],
            ["Relaxation Video", '<a href="https://youtu.be/MIc299Flibs" target="_blank">Watch</a>'],
        ],
        "fear": [
            ["Coping with Anxiety", '<a href="https://www.helpguide.org/mental-health/anxiety" target="_blank">Visit</a>'],
            ["Mindfulness Techniques", '<a href="https://youtu.be/yGKKz185M5o" target="_blank">Watch</a>'],
        ],
        "sadness": [
            ["Overcoming Sadness", '<a href="https://youtu.be/-e-4Kx5px_I" target="_blank">Watch</a>'],
        ],
        "surprise": [
            ["Managing Surprises", '<a href="https://www.health.harvard.edu" target="_blank">Visit</a>'],
            ["Calm Relaxation", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>'],
        ],
    }
    return suggestions.get(emotion.lower(), [["No suggestions are available.", ""]])

def get_health_professionals_and_map(location, query):
    """Search for nearby healthcare professionals and generate a map."""
    try:
        if not location or not query:
            return ["Please provide a valid location and query."], ""

        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"]

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

        return ["No professionals found for the given location."], ""
    except Exception as e:
        return [f"An error occurred: {str(e)}"], ""

# Main Application Logic
def app_function(user_message, location, query, history):
    chatbot_history, _ = chatbot(user_message, history)
    sentiment = analyze_sentiment(user_message)
    emotion = detect_emotion(user_message)
    suggestions = generate_suggestions(emotion)
    professionals, map_html = get_health_professionals_and_map(location, query)
    return chatbot_history, sentiment, emotion, suggestions, professionals, map_html

# Custom CSS for Dark Theme and Gradient Buttons
custom_css = """
body {
    background: linear-gradient(135deg, #000000, #ff5722);
    font-family: 'Roboto', sans-serif;
    color: white;
}
button {
    background: linear-gradient(45deg, #ff5722, #ff9800) !important;
    border: none;
    border-radius: 8px;
    padding: 12px 20px;
    cursor: pointer;
    color: white;
    font-size: 16px;
    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.3);
}
button:hover {
    background: linear-gradient(45deg, #ff9800, #ff5722) !important;
}
textarea, input {
    background: black !important;
    color: white !important;
    padding: 12px;
    border: 1px solid #ff5722 !important;
    border-radius: 8px;
}
.gr-dataframe {
    background-color: black !important;
    color: white !important;
    overflow-y: scroll;
    height: 300px;
    border: 1px solid #ff5722;
}
"""

# Gradio Interface
with gr.Blocks(css=custom_css) as app:
    gr.Markdown("<h1 style='text-align: center;'>🌟 Well-Being Companion</h1>")
    gr.Markdown("<h3 style='text-align: center;'>Empowering Your Mental Health Journey 💚</h3>")

    with gr.Row():
        user_message = gr.Textbox(label="Your Message", placeholder="Enter your message...")
        location = gr.Textbox(label="Your Location", placeholder="Enter your location...")
        query = gr.Textbox(label="Health Query", placeholder="Search for health professionals...")

    chatbot_history = gr.Chatbot(label="Chat History")
    sentiment_output = gr.Textbox(label="Detected Sentiment")
    emotion_output = gr.Textbox(label="Detected Emotion")
    suggestions_table = gr.DataFrame(headers=["Suggestion", "Link"], label="Suggestions")
    professionals_output = gr.Textbox(label="Nearby Health Professionals", lines=5)
    map_output = gr.HTML(label="Map")

    submit_button = gr.Button("Submit")

    submit_button.click(
        app_function,
        inputs=[user_message, location, query, chatbot_history],
        outputs=[chatbot_history, sentiment_output, emotion_output, suggestions_table, professionals_output, map_output]
    )

app.launch()