Food Price Prediction API

This Flask API predicts the price of a food item based on its demand and season.

Dependencies

• Flask
• pandas
• scikit-learn

Usage

1. Create a CSV dataset:

   • The dataset should have columns for "Food Type" (product name), "Demand" (Low, Medium, High), "Season" (Spring, Summer, Autumn, Winter), "Expiration Date", and "Price".
   • Replace "your_dataset.csv" in the code with the actual filename of your dataset.

2. Run the API:

   • Save the code as food_price_prediction.py.
   • Open a terminal and navigate to the directory containing the file.
   • Run the following command:

   python farefinale.py

   • This will start the API on http://localhost:5000/predict.

3. Send a prediction request:

   • Use a tool like Postman or curl to send a POST request to http://localhost:5000/predict with the following JSON data:

   {
       "product_name": "Apple",
       "demand": "Medium",
       "season": "Summer"
   }

   • Replace "Apple" with the desired product name, "Medium" with the demand level (Low, Medium, High), and "Summer" with the season (Spring, Summer, Autumn, Winter).

4. Response:

   • The API will respond with a JSON object containing the predicted price or an error message if the model is not trained for the specified product.

Example Response (Success)

{
    "predicted_price": 12.50
}

Example Response (Error)

{
    "error": "Model not trained for product Pear"
}

Notes

• The API uses a Random Forest Regressor model for prediction.
• The model considers the demand and season when adjusting the predicted price.
• You can modify the demand and season multipliers in the predict_price function to customize the price adjustment logic.

Example request and Response

➜  ~ curl -X POST http://localhost:5000/predict \
     -H "Content-Type: application/json" \
     -d '{
            "items": [
                {"product_name": "Chips", "demand": "Medium", "season": "Summer"},
                {"product_name": "Instant Noodles", "demand": "High", "season": "Winter"},
                {"product_name": "Popcorn", "demand": "Low", "season": "Spring"}
            ]
         }'

  "predictions": [
    {
      "predicted_price": 2.1375,
      "product_name": "Chips"
    },
    {
      "predicted_price": 2.0,
      "product_name": "Instant Noodles"
    },
    {
      "predicted_price": 1.6829999999999972,
      "product_name": "Popcorn"
    }
  ]
}