Upload 3 files

Dockerfile (1)

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+# Use Python base image
+FROM python:3.11-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy files
+COPY requirements.txt .
+COPY app.py .
+COPY random_forest_pipeline.pkl .
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose port
+EXPOSE 7860
+
+# Run the Flask app
+CMD ["python", "app.py"]

app (1).py

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+from flask import Flask, request, jsonify
+import pandas as pd
+import joblib
+
+# Load the trained SuperKart model
+sales_model = joblib.load("random_forest_pipeline.pkl")
+
+# Initialize Flask application
+app = Flask(__name__)
+
+# Root endpoint
+@app.route('/')
+def index():
+    return "SuperKart Sales Prediction Project"
+
+# Prediction endpoint
+@app.route('/predict', methods=['POST'])
+def make_prediction():
+    try:
+        input_data = request.get_json()
+        input_df = pd.DataFrame([input_data])
+        forecast = sales_model.predict(input_df)[0]
+        return jsonify({'Predicted_Sales_Product': round(forecast, 2)})
+    except Exception as err:
+        return jsonify({'error': str(err)})
+
+# Launch the Flask server
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860)

requirements (1).txt

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+flask
+pandas
+numpy
+fastapi
+uvicorn
+joblib
+scikit-learn==1.6.1