From 4933378638095319133aab0f812e893180cff89c Mon Sep 17 00:00:00 2001
From: Mukesh A <amukesh.mk@gmail.com>
Date: Mon, 20 Jul 2020 12:05:43 +0100
Subject: [PATCH] fixes #18 : prediction result of an algorithm when no
 model/dataset exists

---
 src/api.py | 23 ++++++++++++++++++++++-
 1 file changed, 22 insertions(+), 1 deletion(-)

diff --git a/src/api.py b/src/api.py
index aeb9f9c..45ae8ac 100644
--- a/src/api.py
+++ b/src/api.py
@@ -1,5 +1,7 @@
-from flask import Blueprint, Flask, request, jsonify
+import math
+import json
 import pandas as pd
+from flask import Blueprint, Flask, request, jsonify
 
 from src.utils.constants import *
 
@@ -65,6 +67,11 @@ def retrieve_algorithm_list():
 
     all_metrics = Metric.query.filter_by(dataset_hash=dataset_hash).all()
     
+    if all_metrics == []:
+        data = {}
+        data['response'] = 'Information about the requested dataset is unavailable in the Server!'
+        return json.dumps(data)
+
     return metrics_schema.jsonify(all_metrics)
 
 
@@ -75,6 +82,11 @@ def retrieve_algorithm_best_min():
 
     metric = Metric.query.filter_by(dataset_hash=dataset_hash).order_by(Metric.metric_value.asc()).first()
 
+    if metric is None:
+        data = {}
+        data['response'] = 'Information about the requested dataset is unavailable in the Server!'
+        return json.dumps(data)
+
     return metric_schema.jsonify(metric)
 
 
@@ -85,6 +97,11 @@ def retrieve_algorithm_best_max():
 
     metric = Metric.query.filter_by(dataset_hash=dataset_hash).order_by(Metric.metric_value.desc()).first()
 
+    if metric is None:
+        data = {}
+        data['response'] = 'Information about the requested dataset is unavailable in the Server!'
+        return json.dumps(data)
+
     return metric_schema.jsonify(metric)
 
 
@@ -98,6 +115,10 @@ def predict_fmlearn():
 
     data = {}
 
+    if fml._new_recs == math.inf:
+        data['response'] = 'Model not trained!'
+        return json.dumps(data)
+
     # fetching the encoder for target type for encoding the input data
     tt_encoder = fml.get_encoders()[utils.TARGET_TYPE]
     # fetching the columns used in the dataframe for the said encoder