Create app.py

app.py

@@ -0,0 +1,85 @@
+import os
+import openai
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+import numpy as np
+import gradio as gr
+
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+def classify_defect(defect_description):
+    response = openai.Completion.create(
+    engine="text-davinci-003",
+    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:",
+    temperature= 0,
+    max_tokens= 50,
+    n=1,
+    stop=None
+    #timeout=15,
+    )
+    classification = response.choices[0].text.strip()
+    return classification
+def access(defect_description):
+    response = openai.Completion.create(
+    engine="text-davinci-003",
+    prompt=f"Classify the following defect description into one of the given classes:Login, Network \nDefect Description:{defect_description}\nDefect Class:",
+    max_tokens= 225,
+    n=1,
+    stop=None
+    #timeout=15,
+    )
+    classification = response.choices[0].text.strip()
+    return classification
+def software(defect_description):
+    response = openai.Completion.create(
+    model="text-davinci-003",
+    prompt=f"identify the software from each item in below list:\n[{defect_description}]\nsoftware:",
+    temperature=0.71,
+    max_tokens=73,
+    top_p=1,
+    frequency_penalty=0,
+    presence_penalty=0
+    )
+    classification = response.choices[0].text.strip()
+    return classification
+def hardware(defect_description):
+    response = openai.Completion.create(
+    engine="text-davinci-003",
+    prompt=f"identify the object from each item in below list:\n[{defect_description}]\nobject:",
+    temperature=0.71,
+    max_tokens=73,
+    top_p=1,
+    frequency_penalty=0,
+    presence_penalty=0
+    )
+    classification = response.choices[0].text.strip()
+    return classification
+def mainissue(defect_description):
+    response = openai.Completion.create(
+    engine="text-davinci-003",
+    prompt=f"identify the main issue from defect description given below:\n{defect_description}\nmain issue:",
+    temperature=0.71,
+    max_tokens=73,
+    top_p=1,
+    frequency_penalty=0,
+    presence_penalty=0
+    )
+    classification = response.choices[0].text.strip()
+    return classification
+def main(defect_description):
+    defect_class = classify_defect(defect_description)
+    main_issue = mainissue(defect_description)
+    if defect_class == "Software Issue":
+        sub_class = software(defect_description)
+    elif defect_class == "Hardware Issue":
+        sub_class = hardware(defect_description)
+    elif defect_class =="Access Issue":
+        sub_class = access(defect_description)
+    else:
+        sub_class = "Error"
+    return defect_class, sub_class, main_issue
+inputs = gr.inputs.Textbox(label="Ticket Description")
+outputs = [gr.outputs.Textbox(label="Ticket Category"), gr.outputs.Textbox(label="Ticket Sub Category"),gr.outputs.Textbox(label="Main Issue of The Ticket")]
+
+demo = gr.Interface(fn=main,inputs=inputs,outputs=outputs, title="AI Based Ticket Classification")
+demo.launch()