Create app.py

app.py

@@ -0,0 +1,27 @@
+import pandas as pd
+import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.model_selection import train_test_split
+from sklearn.svm import SVC
+from sklearn.metrics import classification_report
+from sklearn.pipeline import Pipeline
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import StandardScaler
+import joblib
+
+# Load Dataset
+data = pd.read_csv('sarcasm_dataset.csv')
+data['user_feature'] = data['user_feature'].fillna(0)
+
+# Preprocessing
+text_vectorizer = TfidfVectorizer(max_features=5000, stop_words='english')
+scaler = StandardScaler()
+preprocessor = ColumnTransformer(
+    transformers=[
+        ('text', text_vectorizer, 'text'),
+        ('user_features', scaler, ['user_feature']),
+    ]
+)
+
+# Model
+