4 Files

app.py

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+import gradio as gradio
+import joblib as joblib
+import pip
+# pip install gradio
+# pip install joblib
+# pip install xgboost
+# pip install scikit-learn
+
+import joblib
+import numpy as np
+import gradio as gr
+
+# Load the XGBoost model
+xgboost_model = joblib.load('/Users/rak/PycharmProject/Credit_Card_Fraud_Model/xgboost_model_new.pkl')
+
+# Load the StandardScaler
+scaler = joblib.load('/Users/rak/PycharmProject/Credit_Card_Fraud_Model/scaler.pkl')
+
+month_to_number = {
+    "January": 1,
+    "February": 2,
+    "March": 3,
+    "April": 4,
+    "May": 5,
+    "June": 6,
+    "July": 7,
+    "August": 8,
+    "September": 9,
+    "October": 10,
+    "November": 11,
+    "December": 12,
+}
+
+def time_of_dayy(hour):
+    if 6 <= hour < 12:
+        return 'Morning'
+    elif 12 <= hour < 18:
+        return 'Afternoon'
+    elif 18 <= hour < 24:
+        return 'Evening'
+    else:
+        return 'Night'
+
+# Define category options
+category_options = [
+    'Food/Dining',
+    'Gas/Transport',
+    'Online Grocery',
+    'In-Person Grocery',
+    'Health/Fitness',
+    'Home',
+    'Kids/Pets',
+    'Miscellaneous Online',
+    'Miscellaneous In-Person',
+    'Personal Care',
+    'Shopping Online',
+    'Shopping In-Person',
+    'Travel'
+]
+
+def predict_credit_card_fraud(amount, city_pop, month, hour, age, gender, category):
+    # Map the input month name to its corresponding number
+    month = month_to_number[month]
+
+    time_of_day = time_of_dayy(hour)
+
+    # Prepare input data with dummy variables for category
+    input_data = np.array([[amount, city_pop, month, hour, age, int(gender == 'M'),
+                            int(time_of_day == 'Night'), int(time_of_day == 'Evening'), int(time_of_day == 'Morning')] +
+                           [int(category == cat) for cat in category_options]])
+
+    # Scale the input data using the loaded StandardScaler
+    input_data[:, 0:2] = scaler.transform(input_data[:, 0:2])
+
+    # Use predict_proba to get probability scores for class 1
+    probability = xgboost_model.predict_proba(input_data)[:, 1]
+
+    # Return the probability score
+    return round(probability[0], 2)
+
+gender_options = ["M", "F"]
+months = list(month_to_number.keys())
+
+iface = gr.Interface(fn=predict_credit_card_fraud,
+                    inputs=[
+                        gr.Number(label="Amount", info="Enter the Amount of the Transaction in Dollars"),
+                        gr.Number(label="City Population", info="Enter the City Population"),
+                        gr.Dropdown(
+                            months,
+                            label="Month",
+                            info="Select the month of the transaction"
+                        ),
+                        gr.Slider(label="Hour", info="Enter the Hour in which the Transaction Occurred", minimum=0, maximum=23, step=1),
+                        gr.Slider(label="Age", minimum=10, maximum=100, step=1),
+                        gr.Radio(label="Gender", choices=gender_options),
+                        gr.Dropdown(
+                            category_options,
+                            label="Category",
+                            info="Select the Category of Purchase"
+                        )
+                    ],
+                    outputs="text")
+
+if __name__ == "__main__":
+    iface.launch(share=True)

requirements.txt

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+aiofiles==23.2.1
+altair==5.1.2
+annotated-types==0.6.0
+anyio==3.7.1
+attrs==23.1.0
+certifi==2023.7.22
+charset-normalizer==3.3.2
+click==8.1.7
+colorama==0.4.6
+contourpy==1.2.0
+cycler==0.12.1
+exceptiongroup==1.1.3
+fastapi==0.104.1
+ffmpy==0.3.1
+filelock==3.13.1
+fonttools==4.44.0
+fsspec==2023.10.0
+gradio==4.1.2
+gradio_client==0.7.0
+h11==0.14.0
+httpcore==1.0.1
+httpx==0.25.1
+huggingface-hub==0.18.0
+idna==3.4
+importlib-resources==6.1.1
+Jinja2==3.1.2
+joblib==1.3.2
+jsonschema==4.19.2
+jsonschema-specifications==2023.7.1
+kiwisolver==1.4.5
+learn==1.0.0
+markdown-it-py==3.0.0
+MarkupSafe==2.1.3
+matplotlib==3.8.1
+mdurl==0.1.2
+numpy==1.26.1
+orjson==3.9.10
+packaging==23.2
+pandas==2.1.2
+Pillow==10.1.0
+pydantic==2.4.2
+pydantic_core==2.10.1
+pydub==0.25.1
+Pygments==2.16.1
+pyparsing==3.1.1
+python-dateutil==2.8.2
+python-multipart==0.0.6
+pytz==2023.3.post1
+PyYAML==6.0.1
+referencing==0.30.2
+requests==2.31.0
+rich==13.6.0
+rpds-py==0.12.0
+scikit-learn==1.3.2
+scipy==1.11.3
+semantic-version==2.10.0
+shellingham==1.5.4
+six==1.16.0
+sniffio==1.3.0
+starlette==0.27.0
+threadpoolctl==3.2.0
+tomlkit==0.12.0
+toolz==0.12.0
+tqdm==4.66.1
+typer==0.9.0
+typing_extensions==4.8.0
+tzdata==2023.3
+urllib3==2.0.7
+uvicorn==0.24.0.post1
+websockets==11.0.3
+xgboost==2.0.1

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b913deb7ff1b02212fd4d3512996b74a9840e058ed0d58a9dc316fa6edbdde77
+size 983

xgboost_model_new.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:25c781be94764459752dfe626c2c475a8718793089657e80076046f238f82fc2
+size 258523