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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("✔ 🚨 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"📨 You said: \"{text}\"\n")
print("Thank you for sharing. Let's take a closer look at what you're feeling.\n")
print("📝 Analysis Summary:")
print(f" 💬 Sentiment: {sentiment} | Confidence: {sentiment_confidence:.2f}%")
print(f" 🩺 Identified Condition: {disorder_label} | Confidence: {disorder_confidence:.2f}%")
print(f" 🚦 Risk Level: {risk_level}")
print("\n🔍 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("\n💡✨ Just a Note from Your AI Companion:")
print("🤖 I'm here to provide thoughtful insights and emotional support based on your input.")
print("👥 If you're seeking connection or advice, feel free to visit our 🌐 Community Support Page.")
print("🧑⚕️ If things feel overwhelming, consider reaching out to a professional — either directly or with guidance from the community.")
print("You're not alone on this journey. We're here with you. 💙")
print("------------------------------------------------------------")
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