Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import openpyxl
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.ensemble import RandomForestClassifier
+import pickle
+
+import warnings
+warnings.filterwarnings('ignore')
+
+
+st.set_page_config(page_title='Product Type Predictor')
+st.title('Detect Product Type')
+st.subheader('Upload your CSV file')
+uploaded_file = st.file_uploader('Choose a CSV file', type='csv')
+if uploaded_file is not None:
+    st.markdown('---')
+    # Loading the data
+    @st.cache_data
+    def load_excel(file1):
+            df = pd.read_csv(file1)
+            return df
+        
+    data = load_excel(uploaded_file)
+    st.subheader('Data Preview')
+    st.dataframe(data.head(20))
+    # Feature selection
+    features = ['a_ApplicableMarkets', 'Number of Unique Finished Packs in BOM',
+       'Total Number of Finished Packs in BOM', 'GMN', 'Product_Description',
+       'EA_GTIN', 'CV_GTIN', 'Product_Hierarchy_Code',
+       'Product_Hierarchy_Units_Per_Pack_L8', 'myPSR_Pack_Variant',
+       'Stibo_Pack_variant']
+    df = data[features]
+    df = df.replace(r'^\s*$', np.nan, regex=True)
+    df[['EA_GTIN', 'CV_GTIN']] = df[['EA_GTIN', 'CV_GTIN']].apply(pd.to_numeric)
+    df = df.replace(np.nan, 0, regex=True)
+    text_cols = ['a_ApplicableMarkets', 'Product_Hierarchy_Units_Per_Pack_L8', 'myPSR_Pack_Variant', 'Stibo_Pack_variant']
+    df = pd.get_dummies(data=df, columns=text_cols)
+    v = CountVectorizer()
+    text_vectors = v.fit_transform(df['Product_Description'])
+    text_vectors_df = pd.DataFrame(text_vectors.toarray(), columns=v.get_feature_names_out())
+    df_ext = pd.concat([df, text_vectors_df],axis=1)
+    df = df_ext.drop("Product_Description",axis=1)
+    scaler = MinMaxScaler()
+    df[['GMN','EA_GTIN', 'CV_GTIN']] = scaler.fit_transform(df[['GMN','EA_GTIN', 'CV_GTIN']])
+    loaded_model = pickle.load(open('rfc_model.pkl', 'rb'))
+    result = loaded_model.predict(df)
+    data['Product_Type']=result
+
+    out=data.to_csv().encode('utf-8')
+    st.download_button(label='DOWNLOAD RESULT',data=out, file_name='Product_Output.csv',mime='csv')