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Dockerfile

@@ -1,21 +1,14 @@
+# Use a minimal base image with Python 3.9 installed
 FROM python:3.9-slim
 
+# Set the working directory inside the container to /app
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    software-properties-common \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
 
+# Install Python dependencies listed in requirements.txt
 RUN pip3 install -r requirements.txt
 
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
-
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# Define the command to run the Streamlit app on port 8501 and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

app.py

@@ -0,0 +1,37 @@
+import streamlit as st
+import pandas as pd
+import joblib as jb
+
+def load_model():
+  return jb.load('deploy_fm/model.joblib')
+mm2=load_model()
+
+st.title('custo_churn')
+st.write('enter the details')
+cred=st.number_input('credit score',min_value=300,max_value=900,value=650)
+geo=st.selectbox('geography',['france','germany','spain'])
+Age = st.number_input("Age (customer's age in years)", min_value=18, max_value=100, value=30)
+Tenure = st.number_input("Tenure (number of years the customer has been with the bank)", value=12)
+Balance = st.number_input("Account Balance (customer’s account balance)", min_value=0.0, value=10000.0)
+NumOfProducts = st.number_input("Number of Products (number of products the customer has with the bank)", min_value=1, value=1)
+HasCrCard = st.selectbox("Has Credit Card?", ["Yes", "No"])
+IsActiveMember = st.selectbox("Is Active Member?", ["Yes", "No"])
+EstimatedSalary = st.number_input("Estimated Salary (customer’s estimated salary)", min_value=0.0, value=50000.0)
+input_data = pd.DataFrame([{
+    'CreditScore': cred,
+    'Geography': geo,
+    'Age': Age,
+    'Tenure': Tenure,
+    'Balance': Balance,
+    'NumOfProducts': NumOfProducts,
+    'HasCrCard': 1 if HasCrCard == "Yes" else 0,
+    'IsActiveMember': 1 if IsActiveMember == "Yes" else 0,
+    'EstimatedSalary': EstimatedSalary
+}])
+
+ct=0.45
+if st.button('predict'):
+  prediction_proba=mm2.predict_proba(input_data)[0,1]
+  prediction=(prediction_proba>=ct).astype(int)
+  result='churn' if prediction==1 else 'not churn'
+  st.write(f'based on the information provided the customer is likely to {result}')

model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4797c9b09d9ff6e5c624824395fd70c80e615aea1497ac972b744b81fe724799
+size 49311

requirement.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.5.1
+streamlit==1.46.0