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
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score

# Suppress TensorFlow 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")

# Hugging Face sentiment and emotion 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"))

# Disease dictionary to map disease names to numerical values
disease_dict = {
    'Fungal infection': 0, 'Allergy': 1, 'GERD': 2, 'Chronic cholestasis': 3, 'Drug Reaction': 4,
    'Peptic ulcer disease': 5, 'AIDS': 6, 'Diabetes ': 7, 'Gastroenteritis': 8, 'Bronchial Asthma': 9,
    'Hypertension ': 10, 'Migraine': 11, 'Cervical spondylosis': 12, 'Paralysis (brain hemorrhage)': 13,
    'Jaundice': 14, 'Malaria': 15, 'Chicken pox': 16, 'Dengue': 17, 'Typhoid': 18, 'hepatitis A': 19,
    'Hepatitis B': 20, 'Hepatitis C': 21, 'Hepatitis D': 22, 'Hepatitis E': 23, 'Alcoholic hepatitis': 24,
    'Tuberculosis': 25, 'Common Cold': 26, 'Pneumonia': 27, 'Dimorphic hemorrhoids(piles)': 28,
    'Heart attack': 29, 'Varicose veins': 30, 'Hypothyroidism': 31, 'Hyperthyroidism': 32,
    'Hypoglycemia': 33, 'Osteoarthritis': 34, 'Arthritis': 35,
    '(vertigo) Paroxysmal Positional Vertigo': 36, 'Acne': 37, 'Urinary tract infection': 38,
    'Psoriasis': 39, 'Impetigo': 40
}

# Helper Functions for Chatbot
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 generate_chatbot_response(message, history):
    """Generate chatbot response and maintain conversation history."""
    history = history or []
    try:
        result = chatbot_model.predict([bag_of_words(message, words)])
        tag = labels[np.argmax(result)]
        response = "I'm sorry, I didn't understand 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 and map to emojis."""
    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 f"Sentiment: {sentiment_map[sentiment_class]}"

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

def generate_suggestions(emotion):
    """Return relevant suggestions based on detected emotions."""
    emotion_key = emotion.lower()
    suggestions = {
        "joy": [
            ["Relaxation Techniques", "https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation"],
            ["Dealing with Stress", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"],
            ["Emotional Wellness Toolkit", "https://www.nih.gov/health-information/emotional-wellness-toolkit"],
            ["Relaxation Video", "https://youtu.be/m1vaUGtyo-A"],
        ],
        "anger": [
            ["Emotional Wellness Toolkit", "https://www.nih.gov/health-information/emotional-wellness-toolkit"],
            ["Stress Management Tips", "https://www.health.harvard.edu/health-a-to-z"],
            ["Dealing with Anger", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"],
            ["Relaxation Video", "https://youtu.be/MIc299Flibs"],
        ],
        "fear": [
            ["Mindfulness Practices", "https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation"],
            ["Coping with Anxiety", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"],
            ["Emotional Wellness Toolkit", "https://www.nih.gov/health-information/emotional-wellness-toolkit"],
            ["Relaxation Video", "https://youtu.be/yGKKz185M5o"],
        ],
        "sadness": [
            ["Emotional Wellness Toolkit", "https://www.nih.gov/health-information/emotional-wellness-toolkit"],
            ["Dealing with Anxiety", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"],
            ["Relaxation Video", "https://youtu.be/-e-4Kx5px_I"],
        ],
        "surprise": [
            ["Managing Stress", "https://www.health.harvard.edu/health-a-to-z"],
            ["Coping Strategies", "https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety"],
            ["Relaxation Video", "https://youtu.be/m1vaUGtyo-A"],
        ],
    }
    
    # Format the output to include HTML anchor tags
    formatted_suggestions = [
        [title, f'<a href="{link}" target="_blank">{link}</a>'] for title, link in suggestions.get(emotion_key, [["No specific suggestions available.", "#"]])
    ]

    return formatted_suggestions

def get_health_professionals_and_map(location, query):
    """Search nearby healthcare professionals using Google Maps API."""
    try:
        if not location or not query:
            return [], ""  # Return empty list if inputs are missing

        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([place['name'], place.get('vicinity', 'No address provided')])
                folium.Marker(
                    location=[place["geometry"]["location"]["lat"], place["geometry"]["location"]["lng"]],
                    popup=f"{place['name']}"
                ).add_to(map_)
            return professionals, map_._repr_html_()

        return [], ""  # Return empty list if no professionals found
    except Exception as e:
        return [], ""  # Return empty list on exception

# Main Application Logic for Chatbot
def app_function_chatbot(user_input, location, query, history):
    chatbot_history, _ = generate_chatbot_response(user_input, history)
    sentiment_result = analyze_sentiment(user_input)
    emotion_result, cleaned_emotion = detect_emotion(user_input)
    suggestions = generate_suggestions(cleaned_emotion)
    professionals, map_html = get_health_professionals_and_map(location, query)
    return chatbot_history, sentiment_result, emotion_result, suggestions, professionals, map_html

# Load datasets for Disease Prediction
def load_data():
    df = pd.read_csv("Training.csv")
    tr = pd.read_csv("Testing.csv")
    
    # Encode diseases
    df.replace({'prognosis': disease_dict}, inplace=True)
    df = df.infer_objects(copy=False)
    tr.replace({'prognosis': disease_dict}, inplace=True)
    tr = tr.infer_objects(copy=False)
    
    return df, tr

df, tr = load_data()
l1 = list(df.columns[:-1])  
X = df[l1]
y = df['prognosis']
X_test = tr[l1]
y_test = tr['prognosis']

# Trained models
def train_models():
    models = {
        "Decision Tree": DecisionTreeClassifier(),
        "Random Forest": RandomForestClassifier(),
        "Naive Bayes": GaussianNB()
    }
    trained_models = {}
    for model_name, model_obj in models.items():
        model_obj.fit(X, y)
        acc = accuracy_score(y_test, model_obj.predict(X_test))
        trained_models[model_name] = (model_obj, acc)
    return trained_models

trained_models = train_models()

def predict_disease(model, symptoms):
    input_test = np.zeros(len(l1))
    for symptom in symptoms:
        if symptom in l1:
            input_test[l1.index(symptom)] = 1
    prediction = model.predict([input_test])[0]
    return list(disease_dict.keys())[list(disease_dict.values()).index(prediction)]

# Disease Prediction Application Logic
def app_function_disease(name, symptom1, symptom2, symptom3, symptom4, symptom5):
    if not name.strip():
        return "Please enter the patient's name."
    
    symptoms_selected = [s for s in [symptom1, symptom2, symptom3, symptom4, symptom5] if s != "None"]
    
    if len(symptoms_selected) < 3:
        return "Please select at least 3 symptoms for accurate prediction."
    
    results = []
    for model_name, (model, acc) in trained_models.items():
        prediction = predict_disease(model, symptoms_selected)
        result = f"{model_name} Prediction: Predicted Disease: **{prediction}**"
        result += f" (Accuracy: {acc * 100:.2f}%)"
        results.append(result)
    
    return "\n\n".join(results)

# CSS Styling for the Gradio Interface
custom_css = """
body {
    font-family: 'Roboto', sans-serif;
    background-color: #3c6487; /* Set the background color */
    color: white;
}

h1 {
    background: #ffffff;
    color: #000000;
    border-radius: 8px;
    padding: 10px;
    font-weight: bold;
    text-align: center;
    font-size: 2.5rem;
}

textarea, input {
    background: transparent;
    color: black;
    border: 2px solid orange;
    padding: 8px;
    font-size: 1rem;
    caret-color: black;
    outline: none;
    border-radius: 8px;
}

textarea:focus, input:focus {
    background: transparent;
    color: black;
    border: 2px solid orange;
    outline: none;
}

textarea:hover, input:hover {
    background: transparent;
    color: black;
    border: 2px solid orange;
}

.df-container {
    background: white;
    color: black;
    border: 2px solid orange;
    border-radius: 10px;
    padding: 10px;
    font-size: 14px;
    max-height: 400px;
    height: auto;
    overflow-y: auto;
}

#suggestions-title {
    text-align: center !important; /* Ensure the centering is applied */
    font-weight: bold !important; /* Ensure bold is applied */
    color: white !important; /* Ensure color is applied */
    font-size: 4.2rem !important; /* Ensure font size is applied */
    margin-bottom: 20px !important; /* Ensure margin is applied */
}

/* Style for the submit button */
.gr-button {
    background-color: #ae1c93;  /* Set the background color to #ae1c93 */
    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1), 0 2px 4px rgba(0, 0, 0, 0.06);
    transition: background-color 0.3s ease;
}

.gr-button:hover {
    background-color: #8f167b;
}

.gr-button:active {
    background-color: #7f156b;
}
"""

# Gradio Application
with gr.Blocks(css=custom_css) as app:
    gr.HTML("<h1>🌟 Well-Being Companion</h1>")
    
    with gr.Tab("Mental Health Chatbot"):
        with gr.Row():
            user_input = gr.Textbox(label="Please Enter Your Message Here")
            location = gr.Textbox(label="Please Enter Your Current Location Here")
            query = gr.Textbox(label="Please Enter Which Health Professional You Want To Search Nearby")
        
        submit_chatbot = gr.Button(value="Submit Chatbot", variant="primary")

        chatbot = gr.Chatbot(label="Chat History")
        sentiment = gr.Textbox(label="Detected Sentiment")
        emotion = gr.Textbox(label="Detected Emotion")
        
        gr.Markdown("Suggestions", elem_id="suggestions-title")
        
        suggestions = gr.DataFrame(headers=["Title", "Link"])
        professionals = gr.DataFrame(label="Nearby Health Professionals", headers=["Name", "Address"])
        map_html = gr.HTML(label="Interactive Map")

        submit_chatbot.click(
            app_function_chatbot,
            inputs=[user_input, location, query, chatbot],
            outputs=[chatbot, sentiment, emotion, suggestions, professionals, map_html],
        )

    with gr.Tab("Disease Prediction"):
        patient_name = gr.Textbox(label="Name of Patient")
        symptom1 = gr.Dropdown(["None"] + l1, label="Symptom 1")
        symptom2 = gr.Dropdown(["None"] + l1, label="Symptom 2")
        symptom3 = gr.Dropdown(["None"] + l1, label="Symptom 3")
        symptom4 = gr.Dropdown(["None"] + l1, label="Symptom 4")
        symptom5 = gr.Dropdown(["None"] + l1, label="Symptom 5")

        submit_disease = gr.Button(value="Submit Disease Prediction", variant="primary")
        disease_prediction_result = gr.Textbox(label="Prediction")

        submit_disease.click(
            app_function_disease,
            inputs=[patient_name, symptom1, symptom2, symptom3, symptom4, symptom5],
            outputs=disease_prediction_result,
        )

# Launch the Gradio application
app.launch()