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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+dataset.csv filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,28 @@
+import gradio as gr
+import pandas as pd
+from xgboost import XGBClassifier
+import pickle
+from sklearn.neighbors import KNeighborsClassifier
+
+loaded_model = XGBClassifier()
+loaded_model.load_model('xgb_model.json')
+# with open('knn_model.pkl', 'rb') as file:
+#     loaded_knn = pickle.load(file)
+# with open('random_forest_model.pkl', 'rb') as file:
+#     loaded_rf_model = pickle.load(file)
+df_std=pd.read_csv('dataset.csv')
+df_std.drop('phishing', axis=1)
+std_name=df_std.columns
+
+def process_data(file):
+    df = pd.read_csv(file)
+    df_new=df[std_name]
+    out=loaded_model.predict(df_new)
+    return out
+
+iface = gr.Interface(
+    process_data,
+    inputs=gr.File(),
+    outputs=gr.Textbox()
+)
+iface.launch() 

dataset.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c7a9f5a27b5c3269694942a5b116470c7dcc164e528ce11ab1d207d847ebaac
+size 152621314

knn_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f43ef10b5da070180732f0c12db6ccd90d8534aaed1e8077a52e642dad235ce5
+size 59766726

random_forest_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e9655fa7196fc833aa06bc7d9de7954a4e1ef2aa1241a4f3ec6a80af63ad91ac
+size 71631079

requirements.txt

@@ -0,0 +1,5 @@
+numpy==1.26.4
+pandas==2.2.1
+xgboost
+pickle
+sklearn.neighbors