Agasa

Anxiety_ANN_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:877aaee1a85db2a238287a434fb5242e11d32cb0d0c41943c7d7d88e5aa43217
+size 73880

anxiety-test-c3553-firebase-adminsdk-5urg9-2928954445.json

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+{
+  "type": "service_account",
+  "project_id": "anxiety-test-c3553",
+  "private_key_id": "29289544456d5fa39048fdd4bb242c9adb39c164",
+  "private_key": "-----BEGIN PRIVATE KEY-----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\nFvE2J61J1KaoriBX7xc7g2Q=\n-----END PRIVATE KEY-----\n",
+  "client_email": "firebase-adminsdk-5urg9@anxiety-test-c3553.iam.gserviceaccount.com",
+  "client_id": "114282250147198274627",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/firebase-adminsdk-5urg9%40anxiety-test-c3553.iam.gserviceaccount.com",
+  "universe_domain": "googleapis.com"
+}

app.py

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+import streamlit as st
+import pandas as pd
+import os
+import random
+import pickle
+import tensorflow as tf
+import firebase_admin
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from sklearn.preprocessing import StandardScaler
+from tensorflow.keras.models import load_model
+from firebase_admin import credentials, firestore
+
+
+current_directory = os.path.dirname(__file__)
+firebase_config = os.path.join(current_directory,"anxiety-test-c3553-firebase-adminsdk-5urg9-2928954445.json")
+cred = credentials.Certificate(firebase_config)
+if not firebase_admin._apps:
+    firebase_admin.initialize_app(cred)
+
+fs = firestore.client()
+
+def main():
+    # Create a sidebar with navigation links
+    st.sidebar.title("Navigation")
+    page = st.sidebar.selectbox("Select a page", ["Home", "Wellness Test"])
+
+    if page == "Home":
+        show_home_page()
+    elif page == "Wellness Test":
+        show_wellness_test_page()
+
+def show_home_page():
+    st.title("Student Wellness Test")
+    st.subheader("A Website to test your wellness")
+    st.write("A Project By Sugih Ahmad Fauzan and Marco William")
+    st.write('To Start Test, Click on the left side bar and choose Wellness Test')
+
+def show_wellness_test_page():
+    st.title("Wellness Test App")
+
+    st.write("This app allows you to take the GAD (Generalized Anxiety Disorder), SWL (Satisfaction with Life), SPIN (Social Phobia Inventory), and answer some Personal Questions.")
+
+    # GAD Test
+    st.header("GAD Test")
+    gad_questions = ["Feeling nervous, anxious or on edge?",
+                     "Not being able to stop or control worrying?",
+                     "Worrying too much about different things?",
+                     "Trouble relaxing?",
+                     "Being so restless that it's hard to sit still?",
+                     "Becoming easily annoyed or irritable?",
+                     "Feeling afraid as if something awful might happen"]
+    gad_responses = collect_gad_responses(gad_questions)
+    gad_total_score = sum(gad_responses.values())
+
+    st.divider()
+    # SWL Test
+    st.header("SWL Test")
+    swl_questions = ["In most ways, my life is close to my ideal.",
+                     "The conditions of my life are excellent.",
+                     "I am satisfied with my life.",
+                     "So far, I have gotten the important things I want in life.",
+                     "If I could live my life over, I would change almost nothing"]
+    swl_responses = collect_swl_responses(swl_questions)
+    swl_total_score = sum(swl_responses.values())
+
+    st.divider()
+    # SPIN Test
+    st.header("SPIN Test")
+    spin_questions = ["I avoid talking to people I don’t know.",
+                      "I am afraid to speak in public.",
+                      "I avoid activities in which I am the center of attention.",
+                      "Being criticized scares me.",
+                      "I avoid making phone calls.",
+                      "I avoid parties and social events.",
+                      "I avoid participating in class or at meetings.",
+                      "I avoid participating in small groups.",
+                      "I avoid eating with others.",
+                      "I am uncomfortable writing in front of others.",
+                      "I avoid talking to authority figures.",
+                      "I avoid using public restrooms.",
+                      "I avoid expressing disagreement with others.",
+                      "I avoid talking to strangers.",
+                      "I avoid eye contact with others.",
+                      "I am uncomfortable talking to people in authority.",
+                      "I am afraid to date or ask someone out on a date"]
+    spin_responses = collect_spin_responses(spin_questions)
+    spin_total_score = sum(spin_responses.values())
+
+    st.divider()
+    # Personal Questions
+    st.header("Personal Questions")
+    income = st.number_input("1. How much do you earn in a month?", value=0, step=1)
+    age = st.number_input("2. How old are you?", value=18, step=1)
+    work_options = {"Not Working" :0, "Part Time":1, "Full Time":2}
+    work = st.selectbox("3. What is your employment status?", options=list(work_options.keys()))
+    degree_options = {"Still in School" : 0, "Bachelor":1, "Master":2, "Doctor":3, "Professor":4}
+    degree = st.selectbox("4. What is your highest degree?", options=list(degree_options.keys()))
+    confidence_rating = st.slider("5. Rate your confidence when talking to somebody (1 lowest, 5 highest)", min_value=1, max_value=5, value=3)
+    gender_options = {"Male": 0, "Female": 1}
+    gender = st.selectbox("6. What is your gender?", options=list(gender_options.keys()))
+
+    # Collect all answers in lists
+    GAD_T = gad_total_score/21
+    SWL_T = swl_total_score/25
+    SPIN_T = spin_total_score/51
+    GAD_T = round(GAD_T,6)
+    SWL_T = round(SWL_T,6)
+    SPIN_T = round(SPIN_T,6)
+
+    all_gad_answers = list(gad_responses.values())
+    all_swl_answers = list(swl_responses.values())
+    all_spin_answers = list(spin_responses.values())
+    all_personal_answers = [confidence_rating,income, gender_options[gender], age, work_options[work], degree_options[degree], GAD_T, SWL_T, SPIN_T]
+
+    all_answers = all_gad_answers + all_swl_answers + all_spin_answers + all_personal_answers
+    display_df = pd.DataFrame([all_answers], columns=get_feature_names())
+    df = display_df.copy()
+
+    # scaler_path = "scaler.pkl"  # Replace with the actual path to your model file
+    # with open(scaler_path, 'rb') as file:
+    #     data = pickle.load(file)
+    #     scaler = data['scaler']
+    
+    # model = load_model('Anxiety_ANN_model.h5')
+
+    # result = model.predict(df)
+    # result = int(result*100)
+    
+    # st.write("Kemungkinan anda mengalami Anxiety sebesar : ",result,"%")
+    # df = scaler.transform(df)
+
+    st.subheader("DataFrame of Answers")
+    st.dataframe(df)
+
+    pred = st.button('Prediksi')
+
+    if pred:
+        # Pass the values to the prediction page
+        result = predict_result(df)
+        st.write("Kemungkinan anda mengalami Anxiety sebesar : ",result,"%")
+        threshold = 50
+        thresholded_result = 1 if result > threshold else 0
+        df['Label'] = thresholded_result
+        save_dataframe_to_firestore(df)
+
+
+def collect_gad_responses(questions):
+    # Initialize a dictionary to store responses
+    responses = {}
+    # Iterate through GAD questions and collect user responses
+    for i, question in enumerate(questions, start=1):
+        st.subheader(f"GAD{i}")
+        st.write(f"**Question**: {question}")
+        # Answer options for GAD
+        response = st.radio(f"Select your response (GAD{i}):",
+                            options=["Not at all", "Several days", "More than half the days", "Nearly every day"],
+                            key=f"gad_radio_{i}")
+        # Map response to a numerical value for scoring
+        if response == "Not at all":
+            score = 0
+        elif response == "Several days":
+            score = 1
+        elif response == "More than half the days":
+            score = 2
+        else:
+            score = 3
+        # Store the response and score
+        responses[f'gad{i}'] = score
+    return responses
+
+def collect_swl_responses(questions):
+    # Initialize a dictionary to store responses
+    responses = {}
+    # Iterate through SWL questions and collect user responses
+    for i, question in enumerate(questions, start=1):
+        st.subheader(f"SWL{i}")
+        st.write(f"**Question**: {question}")
+        # Answer options for SWL
+        response = st.radio(f"Select your response (SWL{i}):",
+                            options=["Strongly Disagree", "Disagree", "Neither Agree nor Disagree", "Agree", "Strongly Agree"],
+                            key=f"swl_radio_{i}")
+        # Map response to a numerical value for scoring
+        if response == "Strongly Disagree":
+            score = 1
+        elif response == "Disagree":
+            score = 2
+        elif response == "Neither Agree nor Disagree":
+            score = 3
+        elif response == "Agree":
+            score = 4
+        else:
+            score = 5
+        # Store the response and score
+        responses[f'swl{i}'] = score
+    return responses
+
+def collect_spin_responses(questions):
+    # Initialize a dictionary to store responses
+    responses = {}
+    # Iterate through SPIN questions and collect user responses
+    for i, question in enumerate(questions, start=1):
+        st.subheader(f"SPIN{i}")
+        st.write(f"**Question**: {question}")
+        # Answer options for SPIN
+        response = st.radio(f"Select your response (SPIN{i}):",
+                            options=["Not at all", "A little bit", "Somewhat", "Very much"],
+                            key=f"spin_radio_{i}")
+        # Map response to a numerical value for scoring
+        if response == "Not at all":
+            score = 0
+        elif response == "A little bit":
+            score = 1
+        elif response == "Somewhat":
+            score = 2
+        else:
+            score = 3
+        # Store the response and score
+        responses[f'spin{i}'] = score
+    return responses
+
+def get_feature_names():
+    gad_features = [f'GAD{i}' for i in range(1, 8)]
+    swl_features = [f'SWL{i}' for i in range(1, 6)]
+    spin_features = [f'SPIN{i}' for i in range(1, 18)]
+    personal_features = ['Narcissism','earnings','Gender','Age','Work','Degree','GAD_T', 'SWL_T', 'SPIN_T']
+    return gad_features + swl_features + spin_features + personal_features
+
+def predict_result(answers):
+    scaler_path = "scaler.pkl"  # Replace with the actual path to your model file
+    with open(scaler_path, 'rb') as file:
+        data = pickle.load(file)
+        scaler = data['scaler']
+    
+    model = load_model('Anxiety_ANN_model.h5')
+    df = answers
+    # df = scaler.transform(df)
+    result = model.predict(df)
+    result = int(result*100)
+    return result
+
+def save_dataframe_to_firestore(dataframe):
+    # Convert DataFrame to dictionary
+    data_dict = dataframe.to_dict(orient='records')
+
+    # Add your Firestore collection and document name
+    doc_ref = fs.collection("user_data").add({"data": data_dict})
+
+    return "Successfull Write to Database"
+
+if __name__ == "__main__":
+    main()
+    
+

requirements.txt

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+numpy==1.26.2
+pandas==2.1.3
+scikit-learn==1.3.2
+scipy==1.11.4
+tensorflow==2.15.0
+firebase-admin==6.2.0

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bddfe75d9cc5f35fcf49ded354cef9b1afebc8d3ba4a47a14a4fc91cefd307ef
+size 1777