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 and logs
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
nltk.download("punkt")

# Initialize Stemmer
stemmer = LancasterStemmer()

# Load 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 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 for Nearby Professionals
gmaps = googlemaps.Client(key=os.getenv('GOOGLE_API_KEY'))

# Chatbot Helper
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 a chatbot response and update 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: {str(e)}"
    history.append((message, response))
    return history, response

def analyze_sentiment(user_input):
    """Detect sentiment and return sentiment emoji."""
    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 based on input."""
    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 clickable suggestions for each emotion."""
    suggestions = {
        "joy": [
            ["Relaxation Techniques", '<a href="https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation" target="_blank">Visit</a>'],
            ["Emotional Wellness Toolkit", '<a href="https://www.nih.gov" target="_blank">Visit</a>'],
            ["Relaxation Video", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>'],
        ],
        "anger": [
            ["Stress Management", '<a href="https://www.health.harvard.edu" target="_blank">Visit</a>'],
            ["Dealing with Anger", '<a href="https://www.helpguide.org" target="_blank">Visit</a>']
        ],
        "fear": [
            ["Coping with Anxiety", '<a href="https://www.helpguide.org" target="_blank">Visit</a>'],
            ["Mindfulness Video", '<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": [
            ["Stress Tips", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>']
        ]
    }
    return suggestions.get(emotion.lower(), [["No suggestions available", ""]])

def get_health_professionals_and_map(location, query):
    """Show nearby professionals and interactive map."""
    geo_location = gmaps.geocode(location)
    if geo_location:
        lat, lng = geo_location[0]["geometry"]["location"].values()
        map_ = folium.Map(location=(lat, lng), zoom_start=13)
        professionals = []
        places_result = gmaps.places_nearby(location=(lat, lng), radius=10000, keyword=query)["results"]
        for place in places_result:
            professionals.append(f"{place['name']} - {place.get('vicinity', '')}")
            folium.Marker(
                location=[place["geometry"]["location"]["lat"], place["geometry"]["location"]["lng"]],
                popup=place["name"]
            ).add_to(map_)
        return professionals, map_._repr_html_()
    return ["No professionals found nearby."], ""

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

# CSS for Orange Themed Submit Button
custom_css = """
button { background: linear-gradient(45deg, #ff5722, #ff9800); color: white; }
.gr-dataframe, .gr-html, .gr-chatbot { background: black; color: white; border: 1px solid #ff5722; }
"""

# Gradio Application
with gr.Blocks(css=custom_css) as app:
    gr.Markdown("### 🌟 Well-Being Companion")
    user_input = gr.Textbox(label="Enter Your Message")
    location_input = gr.Textbox(label="Your Location")
    query_input = gr.Textbox(label="Search Query (e.g., therapist)")
    chatbot_history = gr.Chatbot(label="Chatbot History")
    sentiment_box = gr.Textbox(label="Sentiment Detected")
    emotion_box = gr.Textbox(label="Emotion Detected")
    suggestions_table = gr.DataFrame(headers=["Title", "Link"], label="Suggestion Based On Emotion")
    map_output_box = gr.HTML(label="Interactive Map of Professionals")
    professional_list_box = gr.Textbox(label="Professionals Nearby", lines=5)
    submit_button = gr.Button("Submit")

    submit_button.click(
        app_function, 
        inputs=[user_input, location_input, query_input, chatbot_history], 
        outputs=[chatbot_history, sentiment_box, emotion_box, suggestions_table, professional_list_box, map_output_box]
    )
app.launch()