Upload app.py file of backend

Growth_Monitoring-BACKEND/app.py

+# Import the necessary libraries:
+# - Flask: for creating the Flask web server
+# - request: to access incoming request data like form data, file uploads, etc.
+# - jsonify: to convert Python dictionaries or lists to a JSON response which can be returned from Flask routes
+# - CORS: to handle Cross-Origin Resource Sharing, allowing  to make
+# requests from one website to another website in the browser
+import traceback
+
+from flask import Flask, request, jsonify
+from math import ceil
+from flask_cors import CORS
+import json
+import pickle
+import numpy as np
+from sklearn.preprocessing import LabelEncoder
+from datetime import datetime
+import os
+# Initialize the Flask application
+app = Flask(__name__)
+# # Allow cross-origin requests
+# CORS(app)
+# CORS(app, resources={r"/*": {"origins": "*"}})
+
+
+# Load the XGBoost model and the label encoder upon application start
+with open('Models/xg_boost_classifier.pkl', 'rb') as XG_BOOST_MODEL:
+    xg_boost_classifier = pickle.load(XG_BOOST_MODEL)
+
+with open('Models/label_encoder.pkl', 'rb') as LABEL_ENCODER:
+    label_encoder = pickle.load(LABEL_ENCODER)
+
+# Define a route for the root URL ("/") of the API
+# @app.route('/')
+# def home():
+#     return "Growth Monitoring API is up and running!"
+@app.after_request
+def after_request(response):
+    response.headers.add('Access-Control-Allow-Origin', '*')
+    response.headers.add('Access-Control-Allow-Headers', 'Referer,Accept,Origin,User-Agent,Content-Type')
+    response.headers.add('Access-Control-Allow-Methods', 'PUT, GET, POST, DELETE, OPTIONS')
+    return response
+# Define a route for making predictions
+@app.route('/predict_growth_monitoring', methods=['POST'])
+def predict():
+    try:
+        # Extract data from the form of the incoming request
+        data = request.get_json()
+        print(data)
+
+        # Extract individual feature values from the form data
+        character_leaves = int(data['character_leaves'])
+        average_current_height = float(data['average_current_height'])
+        no_of_branches = int(data['no_of_branches'])
+        planted_date = data['planted_date']
+        checked_date = data['checked_date']
+        container_size = data['container_size']
+        color_data = data['current_leaf_colour']
+        dominant_branch = data['dominant_branch']
+
+        # Process the current_leaf_colour to match the model's expected format
+        if color_data == 'bright_yellow':
+            current_leaf_colour_bright_yellow = 1
+            current_leaf_colour_bright_yellow_to_lime_green = 0
+            current_leaf_colour_lime_green_with_yellow_undertones = 0
+            current_leaf_colour_variegated_patterns = 0
+            current_leaf_colour_yellowish_green = 0
+        elif color_data == 'bright_yellow_to_lime_green':
+            current_leaf_colour_bright_yellow = 0
+            current_leaf_colour_bright_yellow_to_lime_green = 1
+            current_leaf_colour_lime_green_with_yellow_undertones = 0
+            current_leaf_colour_variegated_patterns = 0
+            current_leaf_colour_yellowish_green = 0
+        elif color_data == 'lime_green_with_yellow_undertones':
+            current_leaf_colour_bright_yellow = 0
+            current_leaf_colour_bright_yellow_to_lime_green = 0
+            current_leaf_colour_lime_green_with_yellow_undertones = 1
+            current_leaf_colour_variegated_patterns = 0
+            current_leaf_colour_yellowish_green = 0
+        elif color_data == 'variegated_patterns':
+            current_leaf_colour_bright_yellow = 0
+            current_leaf_colour_bright_yellow_to_lime_green = 0
+            current_leaf_colour_lime_green_with_yellow_undertones = 0
+            current_leaf_colour_variegated_patterns = 1
+            current_leaf_colour_yellowish_green = 0
+        else:
+            current_leaf_colour_bright_yellow = 0
+            current_leaf_colour_bright_yellow_to_lime_green = 0
+            current_leaf_colour_lime_green_with_yellow_undertones = 0
+            current_leaf_colour_variegated_patterns = 0
+            current_leaf_colour_yellowish_green = 1
+
+        # Convert strings to datetime objects and calculate the growth duration in days
+        planted_date = datetime.strptime(planted_date, '%Y-%m-%d')
+        checked_date = datetime.strptime(checked_date, '%Y-%m-%d')
+        growth_duration = (checked_date - planted_date).days
+
+        # Process container size information
+        if container_size == 'full_not_crowded':
+            container_size_full_not_crowded = 1
+        else:
+            container_size_full_not_crowded = 0
+
+        # Process dominant branch information
+        if dominant_branch == 'yes':
+            dominant_branch_yes = 1
+        else:
+            dominant_branch_yes = 0
+
+        # Construct the input array from the extracted values
+        input_array = np.array([
+            character_leaves,
+            average_current_height,
+            no_of_branches,
+            growth_duration,
+            current_leaf_colour_bright_yellow,
+            current_leaf_colour_bright_yellow_to_lime_green,
+            current_leaf_colour_lime_green_with_yellow_undertones,
+            current_leaf_colour_variegated_patterns,
+            current_leaf_colour_yellowish_green,
+            container_size_full_not_crowded,
+            dominant_branch_yes
+        ])
+
+        # Reshape the array to the expected input shape for the model
+        input_array = input_array.reshape(1, -1)
+
+        # Make a prediction using the XGBoost model
+        predicted_label = xg_boost_classifier.predict(input_array)
+
+        # Convert the encoded label back to its string representation
+        predicted_class = label_encoder.inverse_transform(predicted_label)
+
+        # Return the prediction as a JSON response
+        return jsonify({'Predicted Growth Category': predicted_class[0]})
+
+    except Exception as e:
+        # Log the error for debugging purposes (print for this example)
+        print(str(e))
+
+        # Return an error response
+        return jsonify({'error': 'An error occurred during prediction.', 'details': str(e)}), 500
+
+
+# Load the XGBoost model for order prediction on application start
+with open('Models/xg_boost_REGRESSOR.pkl', 'rb') as XG_BOOST_MODEL:
+    xgb_order_model = pickle.load(XG_BOOST_MODEL)
+
+# Route to predict order date based on given inputs
+@app.route('/predict_order_date', methods=['POST'])
+def predict_order_date():
+    try:
+        # Extract data from the incoming request
+        data = request.get_json()
+
+        # Extract values from the data
+        fertilizer_ratio = int(data['FERTILZER_RATIO'])
+        growth_duration = int(data['GROWTH_DURATION'])
+        media_grown = data['MEDIA_GROWN']
+        current_growth_category = data['CURRENT_GROWTH_CATEGORY']
+        current_pot_type = data['CURRENT_POT_TYPE']
+        shade_type = data['SHADE_TYPE']
+
+        # Process the media_grown information
+        if media_grown == 'coir_cowdung':
+            media_grown_coir_cowdung = 1
+            media_grown_coir_cowdung_coco_chips = 0
+        else:
+            media_grown_coir_cowdung = 0
+            media_grown_coir_cowdung_coco_chips = 1
+
+        # Process the current_growth_category information
+        if current_growth_category == 'mature_well_grown':
+
+            current_growth_category_mature_well_grown = 1
+            current_growth_category_medium_level_poorly_growing = 0
+            current_growth_category_medium_level_well_growing = 0
+            current_growth_category_recently_planted_poorly_growing = 0
+            current_growth_category_recently_planted_well_growing = 0
+            current_growth_category_small_level_poorly_growing = 0
+            current_growth_category_small_level_well_growing = 0
+
+        elif current_growth_category == 'medium_level_poorly_growing':
+
+            current_growth_category_mature_well_grown = 0
+            current_growth_category_medium_level_poorly_growing = 1
+            current_growth_category_medium_level_well_growing = 0
+            current_growth_category_recently_planted_poorly_growing = 0
+            current_growth_category_recently_planted_well_growing = 0
+            current_growth_category_small_level_poorly_growing = 0
+            current_growth_category_small_level_well_growing = 0
+        elif current_growth_category == 'medium_level_well_growing':
+
+            current_growth_category_mature_well_grown = 0
+            current_growth_category_medium_level_poorly_growing = 0
+            current_growth_category_medium_level_well_growing = 1
+            current_growth_category_recently_planted_poorly_growing = 0
+            current_growth_category_recently_planted_well_growing = 0
+            current_growth_category_small_level_poorly_growing = 0
+            current_growth_category_small_level_well_growing = 0
+        elif current_growth_category == 'recently_planted_poorly_growing':
+
+            current_growth_category_mature_well_grown = 0
+            current_growth_category_medium_level_poorly_growing = 0
+            current_growth_category_medium_level_well_growing = 0
+            current_growth_category_recently_planted_poorly_growing = 1
+            current_growth_category_recently_planted_well_growing = 0
+            current_growth_category_small_level_poorly_growing = 0
+            current_growth_category_small_level_well_growing = 0
+        elif current_growth_category == 'recently_planted_well_growing':
+            current_growth_category_mature_well_grown = 0
+            current_growth_category_mature_well_grown = 0
+            current_growth_category_medium_level_poorly_growing = 0
+            current_growth_category_medium_level_well_growing = 0
+            current_growth_category_recently_planted_poorly_growing = 0
+            current_growth_category_recently_planted_well_growing = 1
+            current_growth_category_small_level_poorly_growing = 0
+            current_growth_category_small_level_well_growing = 0
+        elif current_growth_category == 'small_level_poorly_growing':
+
+            current_growth_category_mature_well_grown = 0
+            current_growth_category_medium_level_poorly_growing = 0
+            current_growth_category_medium_level_well_growing = 0
+            current_growth_category_recently_planted_poorly_growing = 0
+            current_growth_category_recently_planted_well_growing = 0
+            current_growth_category_small_level_poorly_growing = 1
+            current_growth_category_small_level_well_growing = 0
+        else :
+
+
+            current_growth_category_mature_well_grown = 0
+            current_growth_category_medium_level_poorly_growing = 0
+            current_growth_category_medium_level_well_growing = 0
+            current_growth_category_recently_planted_poorly_growing = 0
+            current_growth_category_recently_planted_well_growing = 0
+            current_growth_category_small_level_poorly_growing = 0
+            current_growth_category_small_level_well_growing = 1
+
+
+        # Process current_pot_type
+        if current_pot_type == '8cm_pot':
+            current_pot_type_8cm_pot = 1
+            current_pot_type_hanging_pot = 0
+            current_pot_type_net_pot = 0
+        elif current_pot_type == 'hanging_pot':
+            current_pot_type_8cm_pot = 0
+            current_pot_type_hanging_pot = 1
+            current_pot_type_net_pot = 0
+        else:
+            current_pot_type_8cm_pot = 0
+            current_pot_type_hanging_pot = 0
+            current_pot_type_net_pot = 1
+
+
+            # Process shade_type
+        if shade_type == '70_shade_net':
+            shade_type_70_shade_net = 1
+        else:
+            shade_type_70_shade_net = 0
+            # Construct the input array from the processed values
+        input_array = np.array([
+            fertilizer_ratio,
+            growth_duration,
+            media_grown_coir_cowdung,
+            media_grown_coir_cowdung_coco_chips,
+            current_growth_category_mature_well_grown,
+            current_growth_category_medium_level_poorly_growing,
+            current_growth_category_medium_level_well_growing,
+            current_growth_category_recently_planted_poorly_growing,
+            current_growth_category_recently_planted_well_growing,
+            current_growth_category_small_level_poorly_growing,
+            current_growth_category_small_level_well_growing,
+            current_pot_type_8cm_pot,
+            current_pot_type_hanging_pot,
+            current_pot_type_net_pot,
+            shade_type_70_shade_net
+        ])
+
+        # Reshape the array to the expected input shape for the model
+        input_array = input_array.reshape(1, -1)
+
+        # Make a prediction using the XGBoost model
+        predicted_order_date = xgb_order_model.predict(input_array)
+
+        # Round the prediction to the nearest higher integer
+        predicted_order_date = np.ceil(predicted_order_date[0])
+
+        # Return the prediction as a JSON response
+        # Round up the predicted value to the nearest whole number
+
+        rounded_order_date = ceil(predicted_order_date)
+
+        return jsonify({'Predicted Order Date': str(rounded_order_date) + " days"})
+
+    except Exception as e:
+        # Log the error for debugging purposes
+        return jsonify({
+            "error": "An error occurred during prediction.",
+            "details": str(e),
+            "trace": traceback.format_exc()  # Import traceback at the beginning of your file
+        })
+
+# Run the Flask app
+
+if __name__ == '__main__':
+ app.run(host='0.0.0.0', port=os.environ.get('PORT', 5000))
+