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.preprocessing import LabelEncoder 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")) # Load the disease dataset df_train = pd.read_csv("Training.csv") # Change the file path as necessary df_test = pd.read_csv("Testing.csv") # Change the file path as necessary # Encode diseases 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 } # Function to prepare data def prepare_data(df): """Prepares data for training/testing.""" X = df.iloc[:, :-1] # Features y = df.iloc[:, -1] # Target label_encoder = LabelEncoder() y_encoded = label_encoder.fit_transform(y) return X, y_encoded, label_encoder # Preparing training and testing data X_train, y_train, label_encoder_train = prepare_data(df_train) X_test, y_test, label_encoder_test = prepare_data(df_test) # Define the models models = { "Decision Tree": DecisionTreeClassifier(), "Random Forest": RandomForestClassifier(), "Naive Bayes": GaussianNB() } # Train and evaluate models trained_models = {} for model_name, model_obj in models.items(): model_obj.fit(X_train, y_train) # Fit the model y_pred = model_obj.predict(X_test) # Make predictions acc = accuracy_score(y_test, y_pred) # Calculate accuracy trained_models[model_name] = {'model': model_obj, 'accuracy': acc} # 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. πŸ€”" 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 πŸ˜”", "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 πŸ€”"), emotion def generate_suggestions(emotion): """Return relevant suggestions based on detected emotions.""" emotion_key = emotion.lower() suggestions = { "joy": [ ("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"), ], "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"), ], } # Create a markdown string for clickable suggestions in a table format formatted_suggestions = ["### Suggestions"] formatted_suggestions.append(f"Since you’re feeling {emotion}, you might find these links particularly helpful. Don’t hesitate to explore:") formatted_suggestions.append("| Title | Link |") formatted_suggestions.append("|-------|------|") # Table headers formatted_suggestions += [ f"| {title} | [{link}]({link}) |" for title, link in suggestions.get(emotion_key, [("No specific suggestions available.", "#")]) ] return "\n".join(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 # Disease Prediction Logic def predict_disease(symptoms): """Predict disease based on input symptoms.""" valid_symptoms = [s for s in symptoms if s is not None] # Filter out None values if len(valid_symptoms) < 3: return "Please select at least 3 symptoms for a better prediction." input_test = np.zeros(len(X_train.columns)) # Create an array for feature input for symptom in valid_symptoms: if symptom in X_train.columns: input_test[X_train.columns.get_loc(symptom)] = 1 predictions = {} for model_name, info in trained_models.items(): prediction = info['model'].predict([input_test])[0] predicted_disease = label_encoder_train.inverse_transform([prediction])[0] predictions[model_name] = predicted_disease # Create a Markdown table for displaying predictions markdown_output = ["### Predicted Diseases"] markdown_output.append("| Model | Predicted Disease |") markdown_output.append("|-------|------------------|") # Table headers for model_name, disease in predictions.items(): markdown_output.append(f"| {model_name} | {disease} |") return "\n".join(markdown_output) # CSS for the animated welcome message and improved styles welcome_message = """
Welcome to the Well-Being Companion!
""" # Gradio Application Interface with gr.Blocks(theme="shivi/calm_seafoam") as app: gr.HTML(welcome_message) # Animated welcome message with gr.Tab("Well-Being Chatbot"): gr.HTML("""

🌼 Well-Being Companion 🌼

Your Trustworthy Guide to Emotional Wellness and Health

🌈 Emotional Support | πŸ§˜πŸ»β€β™€οΈ Mindfulness | πŸ₯— Nutrition | πŸ‹οΈ Physical Health | πŸ’€ Sleep Hygiene

""") # Infographics with images gr.HTML("""
Wellness Image 1 Wellness Image 2 Wellness Image 3
""") with gr.Row(): user_input = gr.Textbox(label="Please Enter Your Message Here", placeholder="Type your message here...", max_lines=3) location = gr.Textbox(label="Please Enter Your Current Location", placeholder="E.g., Honolulu", max_lines=1) query = gr.Textbox(label="Search Health Professionals Nearby", placeholder="E.g., Health Professionals", max_lines=1) with gr.Row(): # Align Submit and Clear buttons side by side submit_chatbot = gr.Button(value="Submit Your Message", variant="primary") clear_chatbot = gr.Button(value="Clear", variant="secondary") # Clear button chatbot = gr.Chatbot(label="Chat History", show_label=True) sentiment = gr.Textbox(label="Detected Sentiment", show_label=True) emotion = gr.Textbox(label="Detected Emotion", show_label=True) # Apply styles and create the DataFrame professionals = gr.DataFrame( label="Nearby Health Professionals", # Use label parameter to set the title headers=["Name", "Address"], value=[] # Initialize with empty data ) suggestions_markdown = gr.Markdown(label="Suggestions") map_html = gr.HTML(label="Interactive Map") # Functionality to clear the chat input def clear_input(): return "", [] # Clear both the user input and chat history submit_chatbot.click( app_function_chatbot, inputs=[user_input, location, query, chatbot], outputs=[chatbot, sentiment, emotion, suggestions_markdown, professionals, map_html], ) clear_chatbot.click( clear_input, inputs=None, outputs=[user_input, chatbot] # Reset user input and chat history ) with gr.Tab("Disease Prediction"): gr.HTML("""

Disease Prediction

Help us understand your symptoms!

""") symptom1 = gr.Dropdown(choices=[None] + list(X_train.columns), label="Select Symptom 1", value=None) symptom2 = gr.Dropdown(choices=[None] + list(X_train.columns), label="Select Symptom 2", value=None) symptom3 = gr.Dropdown(choices=[None] + list(X_train.columns), label="Select Symptom 3", value=None) symptom4 = gr.Dropdown(choices=[None] + list(X_train.columns), label="Select Symptom 4", value=None) symptom5 = gr.Dropdown(choices=[None] + list(X_train.columns), label="Select Symptom 5", value=None) submit_disease = gr.Button(value="Predict Disease", variant="primary") disease_prediction_result = gr.Markdown(label="Predicted Diseases") submit_disease.click( lambda symptom1, symptom2, symptom3, symptom4, symptom5: predict_disease( [symptom1, symptom2, symptom3, symptom4, symptom5]), inputs=[symptom1, symptom2, symptom3, symptom4, symptom5], outputs=disease_prediction_result ) # Launch the Gradio application app.launch(share=True)