Update predict_stress.py

predict_stress.py

@@ -1,193 +1,193 @@
-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:
+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 = "unsloth/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}")