Deploy transaction classifier with Gradio API

app.py

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+import joblib
+import gradio as gr
+import pandas as pd
+
+def clean_text(text:str)->str:
+  if pd.isna(text):
+        return ""
+  text = str(text).lower()
+  text = ''.join(char for char in text if char.isalpha() or char.isspace())
+  return text
+
+LogisticModel = joblib.load(r'.\assets\transaction_classifier_model_Logistic_Regression.joblib')
+Vectorizer = joblib.load(r'.\assets\tfidf_vectorizer.joblib')
+
+def predict_category(description:str)->str:
+    cleaned_text = clean_text(description)
+    X = Vectorizer.transform([cleaned_text]).toarray()
+    predict = LogisticModel.predict(X)[0]
+    return predict 
+
+if __name__ == "__main__":
+    demo = gr.Interface(
+        fn=predict_category,
+        inputs=gr.Textbox(label="Enter Transaction Description"),
+        outputs=gr.Textbox(label="Predicted Category"),
+        title="Transaction Classifier",
+        description="Enter a transaction description to predict its category!"
+    )
+
+    demo.launch()

assets/tfidf_vectorizer.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a935342c5ee467059bcf3ef83251081ee7547dac6f077132d5d3ca098deeb9bf
+size 352362

assets/transaction_classifier_model_Logistic_Regression.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7c08b662c45a28701b9f1dd74f6100ce2aa502060ae7bce63d0784d936b2fe7a
+size 769655

requirements.txt

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+gradio==5.24.0
+joblib==1.4.2
+scikit-learn==1.6.1
+pandas