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Dockerfile

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+# Use a minimal base image with Python 3.11 installed
+FROM python:3.11
+#Set working directory inside container
+WORKDIR /app
+#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 pip install -r requirements.txt
+#Define the command to run the Flask app
+CMD ["python","-w","4", "-b", "0.0.0.0:7860", "app:Sales_forecasting_api"]

app.py

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+import pandas as pd
+from flask import Flask, request, jsonify
+import joblib
+
+#Initialize Flask app with a name
+Sales_forecasting_api = Flask("Sales Forecasting API")
+
+#Load of the Saved Mpdel Pipeline
+pipeline = joblib.load("superkart_pipeline.pkl")
+
+#Home Route
+@Sales_forecasting_api.route("/")
+def home():
+    return"Welcome to the SuperKart Sales Prediction API!"
+# Define an endpoint to predict Sales
+@Sales_forecasting_api.route("/predict", methods=["POST"])
+def predict():
+    try:
+        # Get JSON data from the request
+        data = request.get_json()
+        # Convert JSON to DataFrame
+        input_df = pd.DataFrame([data])
+          # Make predictions
+        predictions = pipeline.predict(input_df)
+        # Return predictions as JSON
+        return jsonify({"predictions": predictions.tolist()})
+    except Exception as e:
+        return jsonify({"error": str(e)})

requirements.txt

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+flask==3.0.3
+pandas==2.2.2
+numpy==1.26.4
+scikit-learn==1.5.1
+joblib==1.4.2
+gunicorn==22.0.0
+flask-cors==4.0.1
+python-dotenv==1.0.1