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import streamlit as st
import xgboost as xgb
import pandas as pd
from huggingface_hub import hf_hub_download
xgboostmodel_id = "Sannidhi/stress_prediction_xgboost_model"
xgboost_model = None
def load_xgboost_model():
global xgboost_model
try:
# Download the model from Hugging Face using huggingface_hub
model_path = hf_hub_download(repo_id="Sannidhi/stress_prediction_xgboost_model", filename="xgboost_model.json")
# Load the model into XGBoost
xgboost_model = xgb.Booster()
xgboost_model.load_model(model_path) # Load the model into the Booster object
return True
except Exception as e:
st.error(f"Error loading XGBoost model from Hugging Face: {e}")
return False
def display_predict_stress():
st.title("Predict Stress Level")
st.markdown("Answer the questions below to predict your stress level.")
# Sidebar for navigation
with st.sidebar:
go_home = st.button("Back to Home")
if go_home:
st.session_state.page = "home"
st.experimental_rerun() # Go back to homepage
load_xgboost_model()
# Define the form with dropdowns for user input
with st.form(key="stress_form"):
# Define the questions and their options
stress_questions = {
"How many fruits or vegetables do you eat every day?": ["0", "1", "2", "3", "4", "5"],
"How many new places do you visit in an year?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
"How many people are very close to you?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
"How many people do you help achieve a better life?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
"With how many people do you interact with during a typical day?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
"How many remarkable achievements are you proud of?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
"How many times do you donate your time or money to good causes?": ["0", "1", "2", "3", "4", "5"],
"How well do you complete your weekly to-do lists?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
"In a typical day, how many hours do you experience 'FLOW'? (Flow is defined as the mental state, in which you are fully immersed in performing an activity. You then experience a feeling of energized focus, full involvement, and enjoyment in the process of this activity)": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
"How many steps (in thousands) do you typically walk everyday?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
"For how many years ahead is your life vision very clear for?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
"About how long do you typically sleep?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
"How many days of vacation do you typically lose every year?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
"How often do you shout or sulk at somebody?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
"How sufficient is your income to cover basic life expenses (1 for insufficient, 2 for sufficient)?": ["1", "2"],
"How many recognitions have you received in your life?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
"How many hours do you spend everyday doing what you are passionate about?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
"In a typical week, how many times do you have the opportunity to think about yourself?": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
"Age (1 = 'Less than 20' 2 = '21 to 35' 3 = '36 to 50' 4 = '51 or more')": ["1", "2", "3", "4"],
"Gender (1 = 'Female', 0 = 'Male')": ["0", "1"]
}
# Map the question strings to model feature names
question_to_feature_map = {
"How many fruits or vegetables do you eat every day?": "FRUITS_VEGGIES",
"How many new places do you visit in an year?": "PLACES_VISITED",
"How many people are very close to you?": "CORE_CIRCLE",
"How many people do you help achieve a better life?": "SUPPORTING_OTHERS",
"With how many people do you interact with during a typical day?": "SOCIAL_NETWORK",
"How many remarkable achievements are you proud of?": "ACHIEVEMENT",
"How many times do you donate your time or money to good causes?": "DONATION",
"How well do you complete your weekly to-do lists?": "TODO_COMPLETED",
"In a typical day, how many hours do you experience 'FLOW'? (Flow is defined as the mental state, in which you are fully immersed in performing an activity. You then experience a feeling of energized focus, full involvement, and enjoyment in the process of this activity)": "FLOW",
"How many steps (in thousands) do you typically walk everyday?": "DAILY_STEPS",
"For how many years ahead is your life vision very clear for?": "LIVE_VISION",
"About how long do you typically sleep?": "SLEEP_HOURS",
"How many days of vacation do you typically lose every year?": "LOST_VACATION",
"How often do you shout or sulk at somebody?": "DAILY_SHOUTING",
"How sufficient is your income to cover basic life expenses (1 for insufficient, 2 for sufficient)?": "SUFFICIENT_INCOME",
"How many recognitions have you received in your life?": "PERSONAL_AWARDS",
"How many hours do you spend everyday doing what you are passionate about?": "TIME_FOR_PASSION",
"In a typical week, how many times do you have the opportunity to think about yourself?": "WEEKLY_MEDITATION",
"Age (1 = 'Less than 20' 2 = '21 to 35' 3 = '36 to 50' 4 = '51 or more')": "AGE",
"Gender (1 = 'Female', 0 = 'Male')": "GENDER"
}
# Map the responses to numerical values
response_map = {str(i): i for i in range(11)} # Mapping 0-10 to 0-10
response_map.update({"1": 1, "2": 2}) # Mapping "1" and "2" for certain questions
# Store user responses
responses = {}
for question, options in stress_questions.items():
responses[question] = st.selectbox(question, options)
# Submit button
submit_button = st.form_submit_button("Submit")
# When submit is clicked, process the responses and make a prediction
if submit_button:
# Convert responses to feature dictionary based on the feature names
feature_dict = {question_to_feature_map[q]: response_map[responses[q]] for q in stress_questions.keys()}
# Convert to pandas DataFrame
feature_df = pd.DataFrame([feature_dict])
# Make prediction
try:
dmatrix = xgb.DMatrix(feature_df)
prediction = xgboost_model.predict(dmatrix)
st.markdown(f"### Predicted Stress Level: {prediction[0]:.2f}")
st.markdown("Higher values indicate higher stress levels.")
except Exception as e:
st.error(f"Error making prediction: {e}") |