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Dockerfile

@@ -1,6 +1,13 @@
+# FROM python:3.9-slim
+# WORKDIR /app
+# COPY . .
+# COPY requirements.txt .
+# COPY app.py .   
+# RUN pip install --no-cache-dir --upgrade -r requirements.txt
+# CMD ["streamlit", "run", "app.py", "server.address=0.0.0.0", "server.port=8501"]
 FROM python:3.9-slim
 WORKDIR /app
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 COPY app.py .
-CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+CMD ["streamlit", "run", "app.py", "--server.port=7862", "--server.address=0.0.0.0"]

app.py

@@ -19,8 +19,8 @@ tenure = st.number_input("Tenure (Months with the company)", min_value=0, value=
 MonthlyCharges = st.number_input("Monthly Charges", min_value=0.0, value=50.0)
 TotalCharges = st.number_input("Total Charges", min_value=0.0, value=600.0)
 
-# Convert categorical inputs to match model training
-customer_data = {
+# Convert user input into a DataFrame
+customer_data = pd.DataFrame([{
     'SeniorCitizen': 1 if SeniorCitizen == "Yes" else 0,
     'Partner':Partner,
     'Dependents': Dependents,
@@ -31,7 +31,8 @@ customer_data = {
     'PaymentMethod': PaymentMethod,
     'MonthlyCharges': MonthlyCharges,
     'TotalCharges': TotalCharges
-}
+}])
+
 
 # Single prediction section
 # if st.button("Predict", type='primary'):
@@ -50,7 +51,7 @@ if st.button("Predict"):
         print(customer_data)
         response = requests.post(
             "https://MainiSandeep1987-BackEndFlaskAPITelecomChurnPrediction.hf.space/v1/customer",
-            json=input_data.to_dict(orient='records')[0]
+            json=customer_data.to_dict(orient='records')[0]
         )  # Send data to Flask API
         response.raise_for_status()  # Raise an error if the request fails
         result = response.json()

requirements.txt

@@ -1,3 +1,3 @@
 pandas==2.2.2
 requests==2.28.1
-streamlit==1.43.2
+streamlit==1.43.2