Upload 5 files

app.py

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+import gradio as gr
+from preprocessing import vectorizer
+
+
+def predict_category(Issue, SubIssue):
+    model, vectorize, category_mapping = vectorizer()
+    example_text = Issue + " " + SubIssue
+    example_text_vecorized = vectorize.transform([example_text])
+    predicted_category = model.predict(example_text_vecorized)
+    predicted_category_name = [category for category,index in category_mapping.items() if index == predicted_category[0]][0]
+    return predicted_category_name
+
+gr.Interface(title="Predict the category",
+                    description="Here we take issue and subissue of the user and predict different product categories",
+        fn=predict_category,
+        inputs = [gr.Textbox(lines=2,placeholder="Enter issue"),
+        gr.Textbox(lines=2,placeholder="Enter Subissue")],
+        allow_flagging="never",
+        outputs="text",
+        examples=[["i need money for my masters education","till now i haven't got it"],
+                  ["My money is debited from account and still now not transferred to respective bank account","It's been 3 hours"]]).launch()

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:02313298f3d993cb4baa551d41cb699b2370e1c1e3c0a282cfcfc77e1f6c9d90
+size 28146

preprocessing.py

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+import pandas as pd
+import numpy as np
+import pickle
+import warnings
+warnings.filterwarnings("ignore")
+
+def vectorizer():
+    category_mapping = {'Debt collection': 0, 
+                        'Mortgage': 1, 
+                        'Consumer Loan': 2, 
+                        'Bank account or service': 3, 
+                        'Credit reporting': 4, 
+                        'Payday loan': 5, 
+                        'Other financial service': 6, 
+                        'Student loan': 7, 
+                        'Money transfers': 8, 
+                        'Prepaid card': 9, 
+                        'Credit card': 10
+                        }
+    
+    print("Created Mapping")
+    model = pickle.load(open("model.pkl",'rb'))
+    vectorizer = pickle.load(open("vectorizer.pkl",'rb'))
+    print("Loaded the model")
+    return model, vectorizer, category_mapping

vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9d35ad42bf1caaca6c282f237f7d683d35bedab2c41516083a90623de8cc9c73
+size 9528