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app.py

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+import streamlit as st
+import joblib
+import pandas as pd
+
+# Modeli yükle
+model_filename = 'random_forest_model.joblib'
+model = joblib.load(model_filename)
+
+# Kategorik verileri manuel olarak sayılara dönüştüren bir fonksiyon
+def encode_family(family):
+    family_mapping = {'family1': 0, 'family2': 1, 'family3': 2}
+    return family_mapping.get(family, -1)  # -1, geçersiz kategori
+
+def encode_holiday_or_weekday(holiday_or_weekday):
+    holiday_mapping = {'weekday': 0, 'holiday': 1}
+    return holiday_mapping.get(holiday_or_weekday, -1)  # -1, geçersiz kategori
+
+# Tahmin fonksiyonu
+def predict(input_data):
+    input_df = pd.DataFrame(input_data, index=[0])
+    
+    # Kategorik verileri manuel olarak dönüştür
+    input_df['family'] = input_df['family'].apply(encode_family)
+    input_df['holiday_or_weekday'] = input_df['holiday_or_weekday'].apply(encode_holiday_or_weekday)
+    
+    # Modelle tahmin yap
+    prediction = model.predict(input_df)
+    return prediction[0]
+
+# Streamlit UI
+st.title("Sales Prediction App")
+st.write("Enter the input features:")
+
+# Girdi alanları
+store_nbr = st.number_input('Store Number:', min_value=1) 
+family = st.selectbox('Family:', ['family1', 'family2', 'family3']) 
+date_conv = st.number_input('Date (YYYYMMDD):')  
+dcoilwtico = st.number_input('Oil Price (dcoilwtico):')  
+day_week = st.number_input('Day of Week (0=Monday, 6=Sunday):', min_value=0, max_value=6)  
+holiday_or_weekday = st.selectbox('Holiday or Weekday:', ['weekday', 'holiday'])  
+
+# Girdi verilerini hazırla
+input_data = {
+    'id': 0,  
+    'store_nbr': store_nbr,
+    'family': family,
+    'date_conv': date_conv,
+    'dcoilwtico': dcoilwtico,
+    'day_week': day_week,
+    'holiday_or_weekday': holiday_or_weekday,
+}
+
+if st.button('Predict'):
+    prediction = predict(input_data)
+    st.write(f'Predicted sales: {prediction:.2f}')

random_forest_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:83ec2b28535f065c788670ece08006bef51e809fa820be76c6901aa7ff6bd15e
+size 4922600817

requirements.txt

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+streamlit
+tensorflow
+opencv-python
+scikit-learn
+torch
+torchvision
+matplotlib
+transformers
+sentencepiece
+plotly