Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""app.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1PufyNXKkKJpa1fHdbC5xMAHNAkAcB3UC
+"""
+
+!pip install gradio
+
+import os
+import uuid
+import joblib
+import json
+
+import gradio as gr
+import pandas as pd
+
+from huggingface_hub import CommitScheduler
+from pathlib import Path
+
+log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
+log_folder = log_file.parent
+
+scheduler = CommitScheduler(
+    repo_id="dsla_predictor",
+    repo_type="dataset",
+    folder_path=log_folder,
+    path_in_repo="data",
+    every=2
+)
+
+dsla_predictor = joblib.load('model.joblib')
+
+snow_input = gr.Number(label='SNOW Instance')
+priority_input = gr.Number(label='Priority 1-5')
+sla_breached_input = gr.Number(label='SLA Breached 1=Yes, 0=No')
+ci_input = gr.Dropdown(
+    ['App1','App10','App11','App12','App13','App14','App15','App16','App17','App18','App19','App2','App20','App21','App22','App3','App4','App5','App6','App7','App8','App9','OTHER'],
+    label='CI'
+)
+
+error_input = gr.Dropdown(
+    'Access','Availability','Connectivity','Data','Error','Failure','File Transfer','Functionality','Info Security','Latency','Performance','Question','Request','Test',
+    label='Error'
+)
+
+error_symptom_input = gr.Dropdown(
+    ['Account Issue','Application Functionality','Authentication Service Issue','Batch Job','BC Testing','Business Process/Event','Business Process/Rule','Client Side Error','Data Fix','External Mailing','General','Hardware','How To','Human Error','Impacting Normal Operations','Inaccurate Data','Inaccurate Data - Back Office','Inaccurate Data - Client Facing','Inbound Feed Delay','Inbound Feed Failure','Intermittent','Intermittent (Client)','Intermittent (PRU)','Investigation'],
+    label='Error Symptom'
+)
+
+networkdays_input = gr.Number(label='Net Work Days')
+
+model_output = gr.Label(label="dSLA Prediction")
+
+def predict_dsla(snow, priority,sla_breached,ci,error,error_symptom,networkdays):
+    sample = {
+        'SNOW': snow,
+        'Priority': priority,
+        'SLA Breached': sla_breached,
+        'CI': ci,
+        'Error': error,
+        'Error Symptom': error_symptom,
+        'Network Days': networkdays
+    }
+    data_point = pd.DataFrame([sample])
+    prediction = dsla_predictor.predict(data_point).tolist()
+
+    with scheduler.lock:
+        with log_file.open("a") as f:
+            f.write(json.dumps(
+                {
+                    'SNOW': snow,
+                    'Priority': priority,
+                    'SLA Breached': sla_breached,
+                    'CI': ci,
+                    'Error': error,
+                    'Error Symptom': error_symptom,
+                    'Network Days': networkdays,
+                    'prediction': prediction[0]
+                }
+            ))
+            f.write("\n")
+
+    return prediction[0]
+
+demo = gr.Interface(
+    fn=predict_dsla,
+    inputs=[snow_input, priority_input, sla_breached_input, ci_input, error_input, error_symptom_input, networkdays_input],
+    outputs=model_output,
+    title="Dynamic SLA Predictor",
+    description="This API allows you to predict the Dynamic SLA for an incident",
+    allow_flagging="auto",
+    concurrency_limit=8
+)
+
+demo.queue()
+demo.launch(share=False)