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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
def main(incident_description):
defect_class = classify_class(incident_description)
main_issue = classify_cause(incident_description)
return defect_class, main_issue
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()
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