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Dockerfile

@@ -2,13 +2,12 @@ FROM python:3.9-slim
 
 WORKDIR /app
 
-COPY requirements.txt ./
+COPY requirements.txt .
 
 RUN pip install --no-cache-dir --upgrade -r requirements.txt
 
-COPY app.py ./
-COPY SuperKart_Sales_Prediction_Model.joblib ./ 
+COPY . .
 
 EXPOSE 5000
 
-CMD ["gunicorn", "-w", "4", "--bind", "0.0.0.0:5000", "--timeout", "120", "app:app"]
+CMD ["gunicorn", "-w", "4", "--bind", "0.0.0.0:7860", "app:app"]

SuperKart_Sales_Prediction_Model.joblib

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0f612465edf4838d6867ea8d66915d4f18bf9e845e7991124a54e3513dcece86
-size 1401539
+oid sha256:7c672355b08b885f21ebed6ecbcc2b7cb1e8855e24d7f491803c5444d6c56fd7
+size 473398

app.py

@@ -1,19 +1,26 @@
+
 import os
 import joblib
 from flask import Flask, request, jsonify
 import pandas as pd
+import numpy as np
+import warnings
+warnings.filterwarnings("ignore")
 
-# Initialize flask app with a name
-app = Flask("SuperKart Sales Prediction App")
+# Define the path to the serialised model
+MODEL_PATH = "/content/deployment_files/SuperKart_Sales_Prediction_Model.joblib"
 
 # Load the trained model pipeline
-model_path = "SuperKart_Sales_Prediction_Model.joblib"  
 try:
-    model = joblib.load(model_path)
+    model_pipeline = joblib.load(MODEL_PATH)
+    print(f"Model loaded successfully from {MODEL_PATH}")
 except Exception as e:
-    model = None
+    model_pipeline = None
     print(f"Error loading model: {e}")
 
+# Initialize the Flask application
+app = Flask(__name__)
+
 # Define a route for the home page
 @app.route("/")
 def home():
@@ -21,8 +28,8 @@ def home():
 
 # Define an endpoint for making predictions
 @app.route("/predict", methods=["POST"])
-def predict_sales():
-    if model is None:
+def predict():
+    if model_pipeline is None:
         return jsonify({"error": "Model not loaded"}), 500
 
     try:
@@ -34,10 +41,12 @@ def predict_sales():
         # Extract features from the JSON data
         input_df = pd.DataFrame([data])
 
-        return jsonify({"prediction": model.predict(input_df).tolist()})
+        prediction = model_pipeline.predict(input_df)
+        return jsonify({"prediction": prediction.tolist()})
 
     except Exception as e:
-        return jsonify({"error": str(e)}), 500
+        return jsonify({"error": f'Error during prediction: {e}'}), 500
 
 if __name__ == "__main__":  # Correct indentation
+    port = int(os.environ.get("PORT", 5000))
     app.run(host="0.0.0.0", port=5000, debug=True)

requirements.txt

@@ -4,6 +4,6 @@ scikit-learn==1.6.1
 joblib==1.4.2
 requests==2.32.3
 huggingface_hub==0.30.1
-flask==3.0.0
-gunicorn==21.2.0
+flask==3.1.0
+gunicorn==23.0.0
 Werkzeug==3.0.1