Add application file

Dockerfile

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+# Read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

label_encoder_gender.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:360bd57973ec93035a912994862bffe697c19e212fddcd4f966fb030af35522a
+size 258

main.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel, conint, confloat
+from enum import Enum
+import numpy as np
+import pandas as pd
+import pickle as pkl
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Load the trained model
+model = load_model("model.h5")
+
+# Load the pre-trained scalers and encoders
+with open("scaler.pkl", "rb") as f:
+    scaler = pkl.load(f)
+
+with open("label_encoder_gender.pkl", "rb") as f:
+    gen_encoder = pkl.load(f)
+
+with open("onehot_encoder_geography.pkl", "rb") as f:
+    geo_encoder = pkl.load(f)
+
+# Enums for Gender and Geography
+class GenderEnum(str, Enum):
+    Male = "Male"
+    Female = "Female"
+
+class GeographyEnum(str, Enum):
+    France = "France"
+    Germany = "Germany"
+    Spain = "Spain"
+
+# Pydantic model for request validation
+class CustomerData(BaseModel):
+    CreditScore: conint(ge=350, le=850)
+    Gender: GenderEnum
+    Age: conint(ge=18, le=92)
+    Tenure: conint(ge=0, le=10)
+    Balance: confloat(ge=0)
+    NumOfProducts: conint(ge=1, le=4)
+    HasCrCard: conint(ge=0, le=1)
+    IsActiveMember: conint(ge=0, le=1)
+    EstimatedSalary: confloat(ge=0)
+    Geography: GeographyEnum
+
+# API Endpoint for prediction
+@app.post("/predict/")
+def predict_churn(data: CustomerData):
+    # Encode gender
+    gender_encoded = gen_encoder.transform([data.Gender.value])[0]
+
+    # One-hot encode geography
+    geo_encoded = geo_encoder.transform([[data.Geography.value]])
+    geo_encoded_df = pd.DataFrame(geo_encoded, columns=geo_encoder.categories_[0])
+
+    # Prepare input data as DataFrame
+    input_data = {
+        "CreditScore": data.CreditScore,
+        "Gender": gender_encoded,
+        "Age": data.Age,
+        "Tenure": data.Tenure,
+        "Balance": data.Balance,
+        "NumOfProducts": data.NumOfProducts,
+        "HasCrCard": data.HasCrCard,
+        "IsActiveMember": data.IsActiveMember,
+        "EstimatedSalary": data.EstimatedSalary,
+    }
+
+    input_df = pd.DataFrame([input_data])
+
+    # Append one-hot encoded geography
+    input_df = pd.concat([input_df, geo_encoded_df], axis=1)
+
+    # Rename columns to match training data
+    input_df.rename(
+        columns={"France": "Geography_France", "Germany": "Geography_Germany", "Spain": "Geography_Spain"},
+        inplace=True,
+    )
+
+    # Scale the input data
+    input_scaled = scaler.transform(input_df)
+
+    # Make prediction
+    prediction = model.predict(input_scaled)
+
+    # Return result
+    result = "The customer is likely to churn" if prediction[0][0] > 0.5 else "The customer is not likely to churn"
+    return {"prediction": result, "probability": float(prediction[0][0])}

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:168c067e4ea72ad77a5303bd432fc923ac92fa4c9ec8e88080f5228af7c7c3a2
+size 62832

onehot_encoder_geography.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:af5952b1a655b7212d54b478b896d6154553276da878171d4a7f0559199789d2
+size 597

requirements.txt

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+fastapi
+pydantic
+tensorflow
+numpy
+pandas
+scikit-learn
+uvicorn

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ac0cba24ccc97524d4e79423685898630de704289622df2aaa849e786b1bed44
+size 992