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| import streamlit as st | |
| import xgboost as xgb | |
| import pandas as pd | |
| from huggingface_hub import hf_hub_download | |
| import itertools | |
| from langchain_huggingface import HuggingFaceEndpoint | |
| import os | |
| from transformers import pipeline | |
| from langchain_core.prompts import PromptTemplate | |
| from langchain_core.output_parsers import StrOutputParser | |
| xgboostmodel_id = "Sannidhi/stress_prediction_xgboost_model" | |
| xgboost_model = None | |
| model_id = "meta-llama/Llama-3.2-1B-Instruct" | |
| generator = pipeline("text-generation", model=model_id) | |
| def get_llm_response(prompt_text, model_id="meta-llama/Llama-3.2-3B-Instruct", max_new_tokens=256, temperature=0.5): | |
| """Generates a response from the Hugging Face model for a given prompt text.""" | |
| try: | |
| llm = HuggingFaceEndpoint( | |
| repo_id=model_id, | |
| max_new_tokens=max_new_tokens, | |
| temperature=temperature, | |
| token=os.getenv("HF_TOKEN") | |
| ) | |
| system_message = "Rephrase the following text without adding any comments, feedback, or suggestions. Return only the rephrased text exactly as requested." | |
| prompt = PromptTemplate.from_template("{system_message}\n\n{user_text}") | |
| chat = prompt | llm.bind(skip_prompt=True) | StrOutputParser(output_key='content') | |
| response = chat.invoke(input=dict(system_message=system_message, user_text=prompt_text)) | |
| return response | |
| except Exception as e: | |
| return f"Error generating response: {e}" | |
| def load_xgboost_model(): | |
| global xgboost_model | |
| try: | |
| model_path = hf_hub_download(repo_id="Sannidhi/stress_prediction_xgboost_model", filename="xgboost_model.json") | |
| xgboost_model = xgb.Booster() | |
| xgboost_model.load_model(model_path) | |
| 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("Analyse Current Stress") | |
| st.markdown("Answer the questions below to predict your stress level.") | |
| with st.sidebar: | |
| go_home = st.button("Back to Home") | |
| if go_home: | |
| st.session_state.page = "home" | |
| load_xgboost_model() | |
| with st.form(key="stress_form"): | |
| 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 every week 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"] | |
| } | |
| 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 every week 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" | |
| } | |
| response_map = {str(i): i for i in range(11)} | |
| response_map.update({"1": 1, "2": 2}) | |
| responses = {} | |
| for question, options in stress_questions.items(): | |
| responses[question] = st.selectbox(question, options) | |
| submit_button = st.form_submit_button("Submit") | |
| if submit_button: | |
| feature_dict = {question_to_feature_map[q]: response_map[responses[q]] for q in stress_questions.keys()} | |
| feature_df = pd.DataFrame([feature_dict]) | |
| try: | |
| dmatrix = xgb.DMatrix(feature_df) | |
| prediction = xgboost_model.predict(dmatrix) | |
| st.markdown(f"### Predicted Stress Level: {prediction[0]:.2f}") | |
| if prediction[0] <= 1: | |
| st.markdown("Your stress level is within a healthy range. Keep up the good work, and aim to maintain it for continued good health!") | |
| else: | |
| weekly_meditation_input = feature_dict["WEEKLY_MEDITATION"] | |
| sleep_hours_input = feature_dict["SLEEP_HOURS"] | |
| time_for_passion_input = feature_dict["TIME_FOR_PASSION"] | |
| places_visited_input = feature_dict["PLACES_VISITED"] | |
| daily_steps_input = feature_dict["DAILY_STEPS"] | |
| weekly_meditation_upper_bound = min(10, weekly_meditation_input + 3) | |
| sleep_hours_upper_bound = min(10, sleep_hours_input + 3) | |
| time_for_passion_upper_bound = min(10, time_for_passion_input + 3) | |
| places_visited_upper_bound = min(10, places_visited_input + 3) | |
| daily_steps_upper_bound = min(10, daily_steps_input + 3) | |
| weekly_meditation_range = range(weekly_meditation_input, weekly_meditation_upper_bound + 1) | |
| sleep_hours_range = range(sleep_hours_input, sleep_hours_upper_bound + 1) | |
| time_for_passion_range = range(time_for_passion_input, time_for_passion_upper_bound + 1) | |
| places_visited_range = range(places_visited_input, places_visited_upper_bound + 1) | |
| daily_steps_range = range(daily_steps_input, daily_steps_upper_bound + 1) | |
| all_combinations = itertools.product(weekly_meditation_range, sleep_hours_range, time_for_passion_range, places_visited_range, daily_steps_range) | |
| best_combination = None | |
| min_diff = float('inf') | |
| for combination in all_combinations: | |
| adjusted_feature_dict = feature_dict.copy() | |
| adjusted_feature_dict["WEEKLY_MEDITATION"] = combination[0] | |
| adjusted_feature_dict["SLEEP_HOURS"] = combination[1] | |
| adjusted_feature_dict["TIME_FOR_PASSION"] = combination[2] | |
| adjusted_feature_dict["PLACES_VISITED"] = combination[3] | |
| adjusted_feature_dict["DAILY_STEPS"] = combination[4] | |
| adjusted_feature_df = pd.DataFrame([adjusted_feature_dict]) | |
| dmatrix = xgb.DMatrix(adjusted_feature_df) | |
| adjusted_prediction = xgboost_model.predict(dmatrix) | |
| if adjusted_prediction[0] <= 1: | |
| diff = sum(abs(adjusted_feature_dict[feature] - feature_dict[feature]) for feature in adjusted_feature_dict) | |
| if diff < min_diff: | |
| min_diff = diff | |
| best_combination = adjusted_feature_dict | |
| if best_combination: | |
| best_sleep = best_combination["SLEEP_HOURS"] | |
| best_meditation = best_combination["WEEKLY_MEDITATION"] | |
| best_passion = best_combination["TIME_FOR_PASSION"] | |
| best_places = best_combination["PLACES_VISITED"] | |
| best_steps = best_combination["DAILY_STEPS"] | |
| best_stress_level = xgboost_model.predict(xgb.DMatrix(pd.DataFrame([best_combination])))[0] | |
| prompt = f"Your stress level appears a bit elevated. To help bring it to a healthier range, try getting {best_sleep} hours of sleep each night, spend around {best_passion} hours each week doing something you’re passionate about, set aside {best_meditation} hours weekly for meditation, aim for {best_steps} thousand steps a day, and plan to explore {best_places} new places this year. These small changes can make a meaningful difference and help you reach a stress level of {best_stress_level}." | |
| model_response = get_llm_response(prompt) | |
| if model_response: | |
| st.markdown(model_response) | |
| else: | |
| st.markdown("Your stress seems a bit high.") | |
| else: | |
| prompt = f"Your stress level seems a bit high. To help bring it down, aim for up to {sleep_hours_upper_bound} hours of sleep each night, spend around {time_for_passion_upper_bound} hours each week on activities you enjoy, set aside {weekly_meditation_upper_bound} hours for meditation each week, try to reach {daily_steps_upper_bound} thousand steps daily, and plan to explore {places_visited_upper_bound} new places this year. These small adjustments can have a positive impact on your stress levels and overall well-being." | |
| model_response = get_llm_response(prompt) | |
| if model_response: | |
| st.markdown(model_response) | |
| else: | |
| st.markdown("Your stress seems a bit high.") | |
| except Exception as e: | |
| st.error(f"Error making prediction: {e}") |