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app.py

@@ -6,10 +6,10 @@ import pandas as pd  # For data manipulation
 from flask import Flask, request, jsonify  # For creating the Flask API
 
 # Initialize Flask app with a name
-nozzleselect_api = Flask("ABMs-Nozzle-Selection") #define the name of the app
+nozzleselect_api = Flask("ABM Nozzle Selection") #define the name of the app
 
 # Load the trained prediction model
-model = joblib.load("nozzle_selection.joblib") #define the location of the serialized model
+model = joblib.load("nozzle_selection_prediction_model.joblib") #define the location of the serialized model
 
 # Define a route for the home page
 @nozzleselect_api.get('/')
@@ -18,10 +18,10 @@ def home():
     This function handles GET requests to the root URL ('/') of the API.
     It returns a simple welcome message.
     """
-    return "Welcome to Babatunde's AFWC Nozzle Selection Predictor API!" #define a welcome message
+    return "Welcome to ABM's Nozzle Select Predictor API!" #define a welcome message
 
 # Define an endpoint to predict churn for a single customer
-@nozzleselect_api.post('/v1/predict')
+@nozzleselect_api_api.post('/v1/predict')
 def predict_nozzle():
     """
     This function handles POST requests to the '/v1/predict' endpoint.
@@ -33,9 +33,11 @@ def predict_nozzle():
 
     # Extract relevant product ans store features from the input data. The order of the column names matters.
     sample = {
+        'nozzle': data['nozzle'],
         'cg': data['cg'],
         'cg_fw': data['cg_fw'],
-        'pressure': data['pressure']
+        'Decide': data['Decide']
+
     }
 
 
@@ -48,11 +50,10 @@ def predict_nozzle():
     # Return the prediction as a JSON response
     return jsonify({'nozzle': prediction})
 
+"""
 # Define an endpoint for batch prediction (POST request)
 @nozzleselect_api.post('/v1/predictbatch')
-
-"""
-def predict_sales_batch():
+def predict_nozzle_batch():
     """
     This function handles POST requests to the '/v1/predictbatch' endpoint.
     It expects a CSV file containing property details for multiple properties
@@ -65,10 +66,10 @@ def predict_sales_batch():
     input_data = pd.read_csv(file)
 
     # Make predictions for all properties in the DataFrame
-    predicted_sales = model.predict(input_data).tolist()
+    predicted_nozzle = model.predict(input_data).tolist()
 
     # Return the prediction as a JSON response
-    return jsonify({'Sales': predicted_sales})
+    return jsonify({'nozzle': predicted_nozzle})
 
 """
 

nozzle_selection.joblib

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8e01b8ef8be7c88fd6393ae6c8ac11ea204cfe445ae48d1bbc006e93606294ca
-size 431004
+oid sha256:025c6acdf8595d9180d84372d7882c5d499461a9ee7039d84a91aa4d098b311c
+size 51899