Upload 4 files

app.py

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+import joblib
+import pandas as pd
+import streamlit as st 
+
+# Load the models
+model_acc = joblib.load('model_acc.joblib')  # Model for accommodation cost prediction
+model_tp = joblib.load('model_tp.joblib')    # Model for transportation cost prediction
+
+def main():
+    st.title("Angie Travel Assistant")
+
+    with st.form("questionnaire"):
+        day = st.slider("Duration of Traveling", min_value=1, max_value=60)
+        acc_type = st.selectbox("Preferred Accommodation Type", unique_value_acc)
+        tp_type = st.selectbox("Preferred Transportation Type", unique_value_tp)
+
+        clicked = st.form_submit_button("Predict total expense")
+        if clicked:
+            # Prepare input data for both models
+            data_acc = pd.DataFrame({
+                'duration_(days)': [day],
+                'accommodation_cost': [0],  # You can set this to 0 since it's not used for prediction
+                f'accommodation_type_{acc_type}': [1]
+            })
+
+            data_tp = pd.DataFrame({
+                'duration_(days)': [day],
+                'transportation_cost': [0],  # You can set this to 0 since it's not used for prediction
+                f'transportation_type_{tp_type}': [1]
+            })
+
+            # Predict expenses using both models
+            result1 = model_acc.predict(data_acc)
+            result2 = model_tp.predict(data_tp)
+
+            # Calculate total expense
+            total_expense = result1[0] + result2[0]
+
+            st.success(f'The predicted total expense is {total_expense}')
+
+if __name__ == '__main__':
+    main()

requirements.txt

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+joblib
+pandas
+scikit-learn==1.2.2
+xgboost==1.7.6
+altair<5

unique_values_acc.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5aeac4e2dd7ca83ae6a70fa17cd84085914252b58d9084cbca89e27e81c47066
+size 676

unique_values_tp.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2312aae4c2aa70db3d3bef93240e0caa38bc92e29e86d314b428634ca33f67cd
+size 580