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Dockerfile

@@ -11,6 +11,6 @@ RUN pip install --no-cache-dir --upgrade -r requirements.txt
 
 # Define the command to start the application using Gunicorn with 4 worker processes
 # - `-w 4`: Uses 4 worker processes for handling requests
-# - `-b 0.0.0.0:8501`: Binds the server to port 8501 on all network interfaces
+# - `-b 0.0.0.0:8501`: Binds the server to port 7860 on all network interfaces
 # - `app:app`: Runs the Flask app (assuming `app.py` contains the Flask instance named `app`)
-CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:8501", "app:extraalearn_api"]
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:extraalearn_api"]

app.py

@@ -6,7 +6,7 @@ import pandas as pd  # For data manipulation
 from flask import Flask, request, jsonify  # For creating the Flask API
 
 # Initialize Flask app with a name
-extraalearn_api = Flask("ExtraaLearnt")
+extraalearn_api = Flask("ExtraaLearn")
 
 # Load the trained churn prediction model
 model = joblib.load("extraalearn_model.joblib")
@@ -43,7 +43,7 @@ def predict_sales():
     input_data = pd.DataFrame([sample])
 
     # Make a churn prediction using the trained model
-    prediction = model.predict(input_data).tolist()[0]
+    prediction = model.predict(input_data)[0]
 
     # Return the prediction as a JSON response
     return jsonify({'Sales': prediction})

extraalearn_model.joblib

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:09e0725cf08dcf67ca7d20f0690f4c7c3e6012349a69ca0250ec74e531ed46a6
-size 317033
+oid sha256:f620d3af597296b0c8e1dad99342b3ea4b02c85bab53b96ebaf7379481405c83
+size 316539