Upload 6 files

Dockerfile

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+# Use an official Python runtime as a parent image
+LABEL authors="LazyBoss"
+FROM python:3.11-slim
+# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]

frauddetection.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:87b0d6c6d39201ca68ce8d21257bbbe60c869667d81071b09e41257e38abab5e
+size 2161056

main.py

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+import joblib
+from pydantic import BaseModel
+from fastapi import FastAPI
+import uvicorn
+import logging
+
+logging.basicConfig(level = logging.INFO)
+# 1. Load the trained model
+model = joblib.load('frauddetection.pkl')
+
+# 2. Define the input data schema using Pydantic BaseModel
+class InputData(BaseModel):
+    Year:int
+    Month:int
+    UseChip:int
+    Amount:int
+    MerchantName:int
+    MerchantCity:int
+    MerchantState:int
+    mcc:int
+    # Add the rest of the input features (feature4, feature5, ..., feature12)
+
+# 3. Create a FastAPI app
+app = FastAPI()
+@app.get('/')
+def welcome():
+    return {"Welcome": "This is the home page of the API"}
+
+
+# 4. Define the prediction route
+@app.post('/predict/')
+async def predict(data: InputData):
+    # Convert the input data to a dictionary
+    input_data = data.dict()
+
+    # Extract the input features from the dictionary
+    feature1 = input_data['Year']
+    feature2=input_data['Month']
+    feature3=input_data['UseChip']
+    feature4=input_data['Amount']
+    feature5=input_data['MerchantName']
+    feature6=input_data['MerchantCity']
+    feature7=input_data['MerchantState']
+    feature8=input_data['mcc']
+    # Extract the rest of the input features (feature4, feature5, ..., feature12)
+
+    # Perform the prediction using the loaded model
+    prediction = model.predict([[feature1, feature2, feature3,feature4,feature5,feature6,feature7,feature8]])  # Replace ... with the rest of the features
+
+    # Convert the prediction to a string (or any other format you prefer)
+    result = "Fraud" if prediction[0] == 1 else "Not a Fraud"
+
+    return {"prediction": result}
+# 4. Run the API with uvicorn
+#    Will run on http://127.0.0.1:8000
+if __name__ == '__main__':
+    uvicorn.run(app, port=8080)

requirements.txt

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+fastapi
+uvicorna