from transformers import pipeline import numpy as np import matplotlib.pyplot as plt from lime.lime_text import LimeTextExplainer class MentalHealthChatbot: def __init__(self, sentiment_model_path, disorder_model_path): # Load sentiment and classification models self.sentiment_pipeline = pipeline("text-classification", model=sentiment_model_path) self.classify_pipeline = pipeline("text-classification", model=disorder_model_path) # Label mappings self.label_mapping = { "LABEL_0": "ADHD", "LABEL_1": "BPD", "LABEL_2": "OCD", "LABEL_3": "PTSD", "LABEL_4": "Anxiety", "LABEL_5": "Autism", "LABEL_6": "Bipolar", "LABEL_7": "Depression", "LABEL_8": "Eating Disorders", "LABEL_9": "Health", "LABEL_10": "Mental Illness", "LABEL_11": "Schizophrenia", "LABEL_12": "Suicide Watch" } self.sentiment_mapping = { "POS": "Positive", "NEG": "Negative", "NEU": "Neutral" } self.exercise_recommendations = { # Exercise recommendations data as defined in the original code } # Initialize the LIME explainer self.explainer = LimeTextExplainer(class_names=list(self.sentiment_mapping.values()) + list(self.label_mapping.values())) def get_sentiment(self, text): results = self.sentiment_pipeline(text) if results and isinstance(results, list): best_result = results[0] label = self.sentiment_mapping.get(best_result["label"], "Unknown") confidence = best_result["score"] * 100 return label, confidence return "Unknown", 0 def get_disorder(self, text, threshold=50): results = self.classify_pipeline(text) if results and isinstance(results, list): best_result = results[0] disorder_confidence = best_result["score"] * 100 if disorder_confidence > threshold: disorder_label = self.label_mapping.get(best_result["label"], "Unknown") if disorder_confidence < 50: risk_level = "Low Risk" elif 50 <= disorder_confidence <= 75: risk_level = "Moderate Risk" else: risk_level = "High Risk" if risk_level == "High Risk": print("βœ” 🚨 Alert Notification Triggered: High risk detected!\n") return disorder_label, disorder_confidence, risk_level return "No significant disorder detected", 0.0, "No Risk" def predict_fn(self, texts): sentiment_output = self.sentiment_pipeline(texts) sentiment_probs = np.array([[item['score']] for item in sentiment_output]) disorder_output = self.classify_pipeline(texts) disorder_probs = np.vstack([np.array([item['score']]) for item in disorder_output]) result = np.hstack([sentiment_probs, disorder_probs]) return result def explain_text(self, text): explanation = self.explainer.explain_instance(text, self.predict_fn, num_features=5, num_samples=25) explanation.as_pyplot_figure() # Display the plot plt.show() explanation_str = "The model's prediction is influenced by the following factors: " explanation_str += "; ".join([f'"{feature}" contributes with a weight of {weight:.4f}' for feature, weight in explanation.as_list()]) + "." return explanation_str def get_recommendations(self,condition, risk_level): exercise_recommendations = { "Depression": { "High Risk": ["Try 10 minutes of deep breathing.", "Go for a 15-minute walk in nature.", "Practice guided meditation."], "Moderate Risk": ["Write down 3 things you’re grateful for.", "Do light stretching or yoga for 10 minutes.", "Listen to calming music."], "Low Risk": ["Engage in a hobby you enjoy.", "Call a friend and have a short chat.", "Do a short 5-minute mindfulness exercise."] }, "Anxiety": { "High Risk": ["Try progressive muscle relaxation.", "Use the 4-7-8 breathing technique.", "Write down your thoughts to clear your mind."], "Moderate Risk": ["Listen to nature sounds or white noise.", "Take a 15-minute break from screens.", "Try a short visualization exercise."], "Low Risk": ["Practice slow, deep breathing for 5 minutes.", "Drink herbal tea and relax.", "Read a book for 10 minutes."] }, "Bipolar": { "High Risk": ["Engage in grounding techniques like 5-4-3-2-1.", "Try slow-paced walking in a quiet area.", "Listen to calm instrumental music."], "Moderate Risk": ["Do a 10-minute gentle yoga session.", "Keep a mood journal for self-awareness.", "Practice self-affirmations."], "Low Risk": ["Engage in light exercise like jogging.", "Practice mindful eating for a meal.", "Do deep breathing exercises."] }, "OCD": { "High Risk": ["Use exposure-response prevention techniques.", "Try 5 minutes of guided meditation.", "Write down intrusive thoughts and challenge them."], "Moderate Risk": ["Take a short break from triggers.", "Practice progressive relaxation.", "Engage in a calming activity like drawing."], "Low Risk": ["Practice deep breathing with slow exhales.", "Listen to soft music and relax.", "Try focusing on one simple task at a time."] }, "PTSD": { "High Risk": ["Try grounding techniques (hold an object, describe it).", "Do 4-7-8 breathing for relaxation.", "Write in a trauma journal."], "Moderate Risk": ["Practice mindfulness for 5 minutes.", "Engage in slow, rhythmic movement (walking, stretching).", "Listen to soothing music."], "Low Risk": ["Try positive visualization techniques.", "Engage in light exercise or stretching.", "Spend time in a quiet, safe space."] }, "Suicide Watch": { "High Risk": ["Immediately reach out to a mental health professional.", "Call a trusted friend or family member.", "Try a grounding exercise like cold water on hands."], "Moderate Risk": ["Write a letter to your future self.", "Listen to uplifting music.", "Practice self-care (take a bath, make tea, etc.)."], "Low Risk": ["Watch a motivational video.", "Write down your emotions in a journal.", "Spend time with loved ones."] }, "ADHD": { "High Risk": ["Try structured routines for the day.", "Use a timer for focus sessions.", "Engage in short bursts of physical activity."], "Moderate Risk": ["Do a quick exercise routine (jumping jacks, stretches).", "Use fidget toys to channel energy.", "Try meditation with background music."], "Low Risk": ["Practice deep breathing.", "Listen to classical or instrumental music.", "Organize your workspace."] }, "BPD": { "High Risk": ["Try dialectical behavior therapy (DBT) techniques.", "Practice mindfulness.", "Use a weighted blanket for comfort."], "Moderate Risk": ["Write down emotions and analyze them.", "Engage in creative activities like painting.", "Listen to calming podcasts."], "Low Risk": ["Watch a lighthearted movie.", "Do breathing exercises.", "Call a friend for a short chat."] }, "Autism": { "High Risk": ["Engage in deep-pressure therapy (weighted blanket).", "Use noise-canceling headphones.", "Try sensory-friendly relaxation techniques."], "Moderate Risk": ["Do repetitive physical activities like rocking.", "Practice structured breathing exercises.", "Engage in puzzles or memory games."], "Low Risk": ["Spend time in a quiet space.", "Listen to soft instrumental music.", "Follow a structured schedule."] }, "Schizophrenia": { "High Risk": ["Seek immediate support from a trusted person.", "Try simple grounding exercises.", "Use distraction techniques like puzzles."], "Moderate Risk": ["Engage in light physical activity.", "Listen to calming sounds or music.", "Write thoughts in a journal."], "Low Risk": ["Read a familiar book.", "Do a 5-minute breathing exercise.", "Try progressive muscle relaxation."] }, "Eating Disorders": { "High Risk": ["Seek professional help immediately.", "Try self-affirmations.", "Practice intuitive eating (listen to body cues)."], "Moderate Risk": ["Engage in mindful eating.", "Write down your emotions before meals.", "Do light stretching after meals."], "Low Risk": ["Try a gentle walk after eating.", "Listen to calming music.", "Write a gratitude journal about your body."] }, "Mental Health": { "High Risk": ["Reach out to a mental health professional.", "Engage in deep relaxation techniques.", "Talk to a support group."], "Moderate Risk": ["Write in a daily journal.", "Practice guided meditation.", "Do light physical activities like walking."], "Low Risk": ["Try deep breathing exercises.", "Watch an uplifting video.", "Call a friend for a chat."] } } if condition in exercise_recommendations: if risk_level in exercise_recommendations[condition]: return exercise_recommendations[condition][risk_level] return ["No specific recommendations available."] def run_chat(self, text): sentiment, sentiment_confidence = self.get_sentiment(text) disorder_label, disorder_confidence, risk_level = self.get_disorder(text) print("\n🧠 Mental Health Chatbot Assessment") print("--------------------------------------------------") print(f"πŸ“¨ You said: \"{text}\"\n") print("Thank you for sharing. Let's take a closer look at what you're feeling.\n") print("πŸ“ Analysis Summary:") print(f" πŸ’¬ Sentiment: {sentiment} | Confidence: {sentiment_confidence:.2f}%") print(f" 🩺 Identified Condition: {disorder_label} | Confidence: {disorder_confidence:.2f}%") print(f" 🚦 Risk Level: {risk_level}") print("\nπŸ” Explanation (LIME Interpretation):") print("--------------------------------------------------") print("Here’s how the system interpreted your message using explainable AI techniques:") explanation_str = self.explain_text(text) print(explanation_str + "\n") print("\n🧭 Personalized Recommendations:") print("--------------------------------------------------") print("These are tailored suggestions to help guide you toward the next steps:") recommendations = self.get_recommendations(disorder_label, risk_level) for idx, action in enumerate(recommendations, 1): print(f" {idx}. {action}") print("We encourage you to try the steps that resonate with your situation.") print("\nπŸ’‘βœ¨ Just a Note from Your AI Companion:") print("πŸ€– I'm here to provide thoughtful insights and emotional support based on your input.") print("πŸ‘₯ If you're seeking connection or advice, feel free to visit our 🌐 Community Support Page.") print("πŸ§‘β€βš•οΈ If things feel overwhelming, consider reaching out to a professional β€” either directly or with guidance from the community.") print("You're not alone on this journey. We're here with you. πŸ’™") print("------------------------------------------------------------")