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app.py

@@ -6,7 +6,7 @@ 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("ABM Nozzle Selection") #define the name of the app
+nozzleselect_api = Flask("ABMs-Nozzle-Selection") #define the name of the app
 
 # Load the trained prediction model
 model = joblib.load("nozzle_selection_prediction_model.joblib") #define the location of the serialized model
@@ -21,7 +21,7 @@ def home():
     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_api.post('/v1/predict')
+@nozzleselect_api.post('/v1/predict')
 def predict_nozzle():
     """
     This function handles POST requests to the '/v1/predict' endpoint.
@@ -54,11 +54,11 @@ def predict_nozzle():
 # Define an endpoint for batch prediction (POST request)
 @nozzleselect_api.post('/v1/predictbatch')
 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
     and returns the predicted rental prices as a dictionary in the JSON response.
-    """
+    
     # Get the uploaded CSV file from the request
     file = request.files['file']
 
@@ -70,7 +70,6 @@ def predict_nozzle_batch():
 
     # Return the prediction as a JSON response
     return jsonify({'nozzle': predicted_nozzle})
-
 """
 
 # Run the Flask app in debug mode