Create app.py

app.py

@@ -0,0 +1,42 @@
+import streamlit as st 
+st.markdown(""" This is a Streamlit App """)
+
+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+import chardet
+from pathlib import Path
+from detect_delimiter import detect
+
+label_dict = {
+    0: "Brandsøgning",
+    1: "Informational",
+    2: "Inspiration",
+    3: "Navigational",
+    4: "Transactional"
+}
+upload_file = st.file_uploader("Choose a file",type="csv" )
+model = pickle.load(open("finalized_model.sav","rb"))
+
+if upload_file is not None:
+    result = chardet.detect(upload_file.getvalue())
+    encoding_value = result["encoding"]
+    if encoding_value == "UTF-16":
+      white_space = True
+    else:
+      white_space = False
+    df = pd.read_csv((upload_file), on_bad_lines='skip', encoding=encoding_value, delim_whitespace=white_space)
+    print(df)
+    result = {}
+    result['Keyword'] = df['Keyword'][:5000]
+    result['volume'] =df['Volume'][:5000]
+    classes =  [label_dict[model.predict(item)[0][0]] for item in df['Keyword'].values[:5000]]
+    result['Classes'] = classes
+    df = pd.DataFrame(result)
+    st.download_button(
+        label="Download CSV file",
+        data=df.to_csv().encode('utf-8'),
+        file_name='labbeled_data.csv',
+        mime='text/csv'
+    )