Create app.py

app.py

@@ -0,0 +1,43 @@
+import os
+import openai
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+import numpy as np
+import gradio as gr
+
+openai.api_key = "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"
+
+def classify_cause(incident_description):
+    response = openai.Completion.create(
+    engine="text-davinci-003",
+    prompt= f"Identify the root cause from the below list:\nincident_description:{incident_description}\n",
+    temperature= 0,
+    max_tokens= 50,
+    n=1,
+    stop=None
+    #timeout=15,
+    )
+    classification = response.choices[0].text.strip()
+    return classification
+def classify_class(incident_description):
+    response = openai.Completion.create(
+    engine="text-davinci-003",
+    prompt= f"Classify the following incident description into one of the given classes:Aircraft Autopilot Problem, Auxiliary Power Problem,Cabin Pressure Problem, Engine Problem,Fuel System Problem,Avionics Problem,Communications Problem,Electrical System Problem,Engine Problem,Fire/Smoke Problem,Fuel System Problem,Ground Service Problem,Hydraulic System Problem,Ice/Frost Problem,Landing Gear Problem,Maintenance Problem,Oxygen System Problem,other problem\nincident_description:{incident_description}\n", 
+    temperature= 0,
+    max_tokens= 50,
+    n=1,
+    stop=None
+    #timeout=15,
+    )
+    classification = response.choices[0].text.strip()
+    return classification
+
+inputs =  gr.inputs.Textbox(label="Flight Incident Description")
+outputs = [gr.outputs.Textbox(label="Main Issue of the flight incident"),
+           gr.outputs.Textbox(label="category of the flight incident")]
+
+demo = gr.Interface(fn=main,inputs=inputs,outputs=outputs, title="Flight predictive maintanance root cause")
+demo.launch()
+
+
+