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app.py

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+import streamlit as st
+import numpy as np
+import pickle
+import random
+import os
+
+# Function to load the model
+def load_model():
+    try:
+        # Get the absolute path to the .pkl file
+        model_path = os.path.join(os.getcwd(), "clfmodel.pkl")
+        model = pickle.load(open(model_path, 'rb'))
+        st.write("Model loaded successfully.")
+        return model
+    except Exception as e:
+        st.write("Something went wrong while loading the model.")
+        st.write(e)
+        return None
+
+# Function to make prediction
+@st.cache_data
+def make_prediction(_model, features):
+    try:
+        predicted_label = _model.predict(features)
+        return predicted_label
+    except Exception as e:
+        st.write("Something went wrong while making prediction.")
+        st.write(e)
+        return None
+
+# Main function to run the app
+def main():
+    st.title("Custom Grocery App Predictor in Hungary")
+    st.write("This app predicts the preferable grocery app based on user characteristics.")
+
+    # Load the model
+    clf_model = load_model()
+
+    if clf_model:
+        # Sidebar inputs
+        st.sidebar.header('User Input Features')
+        gender = st.sidebar.radio('Gender', ('Male', 'Female'))
+        education = st.sidebar.selectbox('Education Level', ('Under Diploma and Diploma', 'Associate', 'Bachelor', 'Master', 'PhD'))
+        age = st.sidebar.slider('Age', 18, 100, 30)
+        exp_online = st.sidebar.slider('Years of Online Experience', 0, 50, 5)
+        exp_app = st.sidebar.slider('Years of App Experience', 0, 20, 1)
+
+        gender_code = 1 if gender == 'Male' else 2
+        education_code = ['Under Diploma and Diploma', 'Associate', 'Bachelor', 'Master', 'PhD'].index(education) + 1
+
+        # Make prediction
+        predicted_label = make_prediction(clf_model, np.array([[gender_code, education_code, age, exp_online, exp_app]]))
+
+        if predicted_label is not None:
+            class_App_names = ['FoodPanda', 'Wolt', 'Spar', 'Tesco online', 'myLidl']
+            predicted_App_class = class_App_names[predicted_label[0] - 1]
+
+            # Generate a random color
+            random_color = "#{:06x}".format(random.randint(0, 0xFFFFFF))
+
+            # Render the output with only the predicted_App_class colored and bigger font size
+            st.write(f"In Hungary, for a {gender}, with education level of {education}, age of {age}, with online experience of {exp_online} years, and shopping experience from online Apps {exp_app} years, It seems that the preferable Grocery App is: ", end="")
+            st.markdown(f"<span style='color: {random_color}; font-weight: bold; font-size: 50px'>{predicted_App_class}</span>.", unsafe_allow_html=True)
+    else:
+        st.write("Failed to load the model.")
+
+if __name__ == '__main__':
+    main()

clfmodel.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ee53eb29cf25b5f58c5c296806f569a003e4f716a7cc6c5a1640551a8a252a10
+size 1520416

requirements.txt

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+altair==5.2.0
+attrs==23.2.0
+blinker==1.7.0
+cachetools==5.3.3
+certifi==2024.2.2
+charset-normalizer==3.3.2
+click==8.1.7
+colorama==0.4.6
+gitdb==4.0.11
+GitPython==3.1.42
+idna==3.6
+Jinja2==3.1.3
+joblib==1.3.2
+jsonschema==4.21.1
+jsonschema-specifications==2023.12.1
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+mdurl==0.1.2
+numpy==1.26.4
+packaging==23.2
+pandas==2.2.1
+pillow==10.2.0
+protobuf==4.25.3
+pyarrow==15.0.2
+pydeck==0.8.1b0
+Pygments==2.17.2
+python-dateutil==2.9.0.post0
+pytz==2024.1
+referencing==0.34.0
+requests==2.31.0
+rich==13.7.1
+rpds-py==0.18.0
+scikit-learn==1.4.1.post1
+scipy==1.12.0
+six==1.16.0
+smmap==5.0.1
+streamlit==1.32.2
+tenacity==8.2.3
+threadpoolctl==3.4.0
+toml==0.10.2
+toolz==0.12.1
+tornado==6.4
+typing_extensions==4.10.0
+tzdata==2024.1
+urllib3==2.2.1
+watchdog==4.0.0