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from flask import Flask, request, jsonify, send_from_directory
import pandas as pd
import numpy as np
import joblib
import os
app = Flask(__name__)
# Load the model and preprocessing objects
model = joblib.load('mental_health_model_final.pkl')
scaler = joblib.load('scaler_final.pkl')
le_gender = joblib.load('le_gender_final.pkl')
le_target = joblib.load('le_target_final.pkl')
# Print feature importance
columns = ['Sentiment_Score', 'HRV', 'Sleep_Hours', 'Activity', 'Age', 'Gender', 'Work_Study_Hours']
print("Feature Importance:")
for feature, importance in zip(columns, model.feature_importances_):
print(f"{feature}: {importance:.4f}")
# Serve the HTML file
@app.route('/')
def serve_html():
return send_from_directory('.', 'index.html')
@app.route('/predict', methods=['POST'])
def predict():
try:
# Get the input data from the request
data = request.get_json()
# Convert to DataFrame
columns = ['Sentiment_Score', 'HRV', 'Sleep_Hours', 'Activity', 'Age', 'Gender', 'Work_Study_Hours']
new_data = pd.DataFrame([data], columns=columns)
# Validate input
if new_data['Sleep_Hours'].iloc[0] < 0:
return jsonify({'error': 'Sleep_Hours cannot be negative'}), 400
if new_data['Work_Study_Hours'].iloc[0] < 0:
return jsonify({'error': 'Work_Study_Hours cannot be negative'}), 400
if new_data['Gender'].iloc[0] not in ['Male', 'Female']:
return jsonify({'error': 'Gender must be Male or Female'}), 400
# Preprocess the data
new_data['Gender'] = le_gender.transform(new_data['Gender'])
new_data_scaled = scaler.transform(new_data)
# Predict
prediction = model.predict(new_data_scaled)[0]
probs = model.predict_proba(new_data_scaled)[0]
health_status = {0: "Low", 1: "Moderate", 2: "High"}
result = health_status[prediction]
# Include probabilities in the response
prob_dict = {health_status[i]: float(prob) for i, prob in enumerate(probs)}
# Add a disclaimer for borderline predictions
max_prob = probs.max()
disclaimer = ""
if max_prob < 0.7:
disclaimer = "This prediction is uncertain (confidence below 70%). Please consult a professional for an accurate assessment."
# Add a simple chatbot-like message
if result == "Low":
chatbot_message = "It looks like you might be experiencing low mental health. Consider reaching out to a friend or professional for support."
elif result == "Moderate":
chatbot_message = "Your mental health seems moderate. Keep up with self-care practices, and consider talking to someone if you feel overwhelmed."
else: # High
chatbot_message = "Great news! Your mental health appears to be high. Keep maintaining your healthy habits!"
return jsonify({
'prediction': result,
'probabilities': prob_dict,
'disclaimer': disclaimer,
'model_accuracy': 'This model has a cross-validation accuracy of 76.8%.',
'chatbot_message': chatbot_message
})
except Exception as e:
return jsonify({'error': str(e)}), 400
@app.route('/feedback', methods=['POST'])
def feedback():
try:
feedback_data = request.get_json()
with open('feedback.log', 'a') as f:
f.write(f"Prediction: {feedback_data['prediction']}, Accurate: {feedback_data['accurate']}\n")
return jsonify({'message': 'Feedback received'})
except Exception as e:
return jsonify({'error': str(e)}), 400
if __name__ == '__main__':
app.run(debug=True, host='0.0.0.0', port=5000) |