Upload untitled2.py

untitled2.py

@@ -0,0 +1,46 @@
+# -*- coding: utf-8 -*-
+"""Untitled2.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1EU-VIsBUmCvPXWODe7G3MMx3nOyrhLE3
+"""
+
+!pip install pygal
+
+!pip install gradio
+
+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 (1).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()
+