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import os |
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import openai |
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import pandas as pd |
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from sklearn.preprocessing import LabelEncoder |
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import numpy as np |
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import gradio as gr |
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openai.api_key = os.getenv("OPENAI_API_KEY") |
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def classify_defect(defect_description): |
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response = openai.Completion.create( |
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engine="text-davinci-003", |
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prompt= f"Classify the following defect description into one of the given classes:Software Issue, Hardware Issue, Access Issue \nDefect Description:{defect_description}\nDefect Class:", |
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temperature= 0, |
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max_tokens= 50, |
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n=1, |
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stop=None |
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) |
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classification = response.choices[0].text.strip() |
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return classification |
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def access(defect_description): |
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response = openai.Completion.create( |
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engine="text-davinci-003", |
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prompt=f"Classify the following defect description into one of the given classes:Login, Network \nDefect Description:{defect_description}\nDefect Class:", |
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max_tokens= 225, |
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n=1, |
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stop=None |
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) |
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classification = response.choices[0].text.strip() |
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return classification |
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def software(defect_description): |
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response = openai.Completion.create( |
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model="text-davinci-003", |
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prompt=f"identify the software from each item in below list:\n[{defect_description}]\nsoftware:", |
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temperature=0.71, |
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max_tokens=73, |
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top_p=1, |
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frequency_penalty=0, |
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presence_penalty=0 |
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) |
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classification = response.choices[0].text.strip() |
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return classification |
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def hardware(defect_description): |
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response = openai.Completion.create( |
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engine="text-davinci-003", |
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prompt=f"identify the object from each item in below list:\n[{defect_description}]\nobject:", |
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temperature=0.71, |
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max_tokens=73, |
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top_p=1, |
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frequency_penalty=0, |
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presence_penalty=0 |
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) |
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classification = response.choices[0].text.strip() |
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return classification |
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def mainissue(defect_description): |
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response = openai.Completion.create( |
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engine="text-davinci-003", |
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prompt=f"identify the main issue from defect description given below:\n{defect_description}\nmain issue:", |
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temperature=0.71, |
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max_tokens=73, |
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top_p=1, |
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frequency_penalty=0, |
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presence_penalty=0 |
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) |
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classification = response.choices[0].text.strip() |
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return classification |
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def main(defect_description): |
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defect_class = classify_defect(defect_description) |
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main_issue = mainissue(defect_description) |
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if defect_class == "Software Issue": |
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sub_class = software(defect_description) |
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elif defect_class == "Hardware Issue": |
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sub_class = hardware(defect_description) |
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elif defect_class =="Access Issue": |
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sub_class = access(defect_description) |
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else: |
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sub_class = "Error" |
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return defect_class, sub_class, main_issue |
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inputs = gr.inputs.Textbox(label="Ticket Description") |
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outputs = [gr.outputs.Textbox(label="Ticket Category"), gr.outputs.Textbox(label="Ticket Sub Category"),gr.outputs.Textbox(label="Main Issue of The Ticket")] |
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demo = gr.Interface(fn=main,inputs=inputs,outputs=outputs, title="AI Based Ticket Classification") |
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demo.launch() |
