Delete untitled2.py

untitled2.py

@@ -1,43 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Untitled2.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1EU-VIsBUmCvPXWODe7G3MMx3nOyrhLE3
-"""
-
-
-import pandas as pd
-import pygal
-from sklearn.cluster import DBSCAN
-import numpy as np
-
-import gradio as gr
-def Predict(inputName):
-    dataFrame = pd.read_json(r"MOCK_DATA.json")
-    safe_distance = 0.0020288 # a radial distance of 6 feet in kilometers
-    model = DBSCAN(eps=safe_distance, min_samples=2, metric='haversine').fit(dataFrame[['Latitude', 'Longitude']])
-    core_samples_mask = np.zeros_like(model.labels_, dtype=bool)
-    core_samples_mask[model.core_sample_indices_] = True
-    labels = model.labels_
-    dataFrame['Cluster'] = model.labels_.tolist()
-    inputNameClusters = set()
-    for i in range(len(dataFrame)):
-        if dataFrame['User'][i] == inputName:
-            inputNameClusters.add(dataFrame['Cluster'][i])
-    infected = set()
-    for cluster in inputNameClusters:
-        if cluster != -1:
-            namesInCluster = dataFrame.loc[dataFrame['Cluster'] == cluster, 'User']
-            for i in range(len(namesInCluster)):
-                name = namesInCluster.iloc[i]
-                if name != inputName:
-                    infected.add(name) 
-    return infected
-
-gui = gr.Interface(fn=Predict, 
-                   inputs="text", 
-                   outputs="text") 
-gui.launch()
-