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Dockerfile

@@ -1,16 +1,6 @@
 FROM python:3.9-slim
-
-# Set the working directory inside the container
 WORKDIR /app
-
-# Copy all files from the current directory to the container's working directory
 COPY . .
-
-# Install dependencies from the requirements file without using cache to reduce image size
-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 0.0.0.0:7860`: 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", "0.0.0.0:7860", "app:superkart_api"]
+RUN pip install --no-cache-dir -r requirements.txt
+# Important: app:app refers to app.py and the Flask instance named 'app'
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:app"]

app.py

@@ -1,40 +1,28 @@
-
-# Import necessary libraries
 import numpy as np
-import joblib  # For loading the serialized model
-import pandas as pd  # For data manipulation
-from flask import Flask, request, jsonify  # For creating the Flask API
+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
 
-# Initialize Flask app with a name
-superkart_api = Flask("ExtraaLearn")
+# Create the Flask app - the name 'app' is standard for Gunicorn
+app = Flask(__name__)
 
-# Load the trained churn prediction model
-model = joblib.load("ExtraaLearn.joblib")
+# Load the model from the local directory inside the container
+# Note: backend_files contains Extraalearn.joblib, so in the container it's just 'Extraalearn.joblib'
+model = joblib.load('Extraalearn.joblib')
 
-# Define a route for the home page
-@superkart_api.get('/')
+@app.get('/')
 def home():
-    return "Welcome to the ExtraaLearn System"
+    return 'ExtraaLearn API is Running'
 
-# Define an endpoint to predict churn for a single customer
-@superkart_api.post('/v1/predict')
+@app.post('/v1/predict')
 def predict_sales():
-    # Get JSON data from the request
-    data = request.get_json()
-
-    # Extract relevant customer features from the input data
-
-
-    # Convert the extracted data into a DataFrame
-    input_data = pd.DataFrame([data])
-
-    # Make a churn prediction using the trained model
-    prediction = model.predict(input_data).tolist()[0]
-
-    # Return the prediction as a JSON response
-    return jsonify({'Sales': prediction})
-
+    try:
+        data = request.get_json()
+        input_data = pd.DataFrame([data])
+        prediction = model.predict(input_data).tolist()[0]
+        return jsonify({'Sales': float(prediction)})
+    except Exception as e:
+        return jsonify({'error': str(e)}), 400
 
-# Run the Flask app in debug mode
 if __name__ == '__main__':
-    superkart_api.run(debug=True)
+    app.run(host='0.0.0.0', port=7860)