Update app.py
Browse files
app.py
CHANGED
@@ -1,5 +1,5 @@
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import os
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import
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import PyPDF2
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import gradio as gr
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import docx
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@@ -8,7 +8,10 @@ import re
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class Resume_Overall:
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def __init__(self):
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def extract_text_from_file(self,file_path):
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# Get the file extension
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@@ -48,17 +51,22 @@ class Resume_Overall:
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resume = self.extract_text_from_file(resume_path)
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# Define the prompt or input for the model
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result format should be:
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course:[course].
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website link:[website link]
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```{resume}```
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"""
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# Generate a response from the GPT-3 model
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prompt=prompt,
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max_tokens=200,
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temperature=0,
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@@ -67,7 +75,7 @@ class Resume_Overall:
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)
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# Extract the generated text from the API response
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generated_text =
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return generated_text
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def summary_response(self,resume_path):
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@@ -76,13 +84,16 @@ class Resume_Overall:
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# Define the prompt or input for the model
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```{resume}```
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"""
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# Generate a response from the GPT-3 model
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prompt=prompt,
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max_tokens=200,
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temperature=0,
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@@ -91,7 +102,7 @@ class Resume_Overall:
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)
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# Extract the generated text from the API response
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generated_text =
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return generated_text
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@@ -102,13 +113,16 @@ class Resume_Overall:
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# Define the prompt or input for the model
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```{resume}```
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"""
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# Generate a response from the GPT-3 model
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prompt=prompt,
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max_tokens=100, # Set the maximum number of tokens in the generated response
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temperature=0, # Controls the randomness of the output. Higher values = more random, lower values = more focused
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@@ -117,21 +131,25 @@ class Resume_Overall:
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)
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# Extract the generated text from the API response
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generated_text =
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return generated_text
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def _generate_job_list(self, resume: str) -> str:
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prompt=prompt,
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max_tokens=100,
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temperature=0,
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n=1,
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stop=None,
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)
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generated_text =
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return generated_text
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@@ -143,8 +161,9 @@ class Resume_Overall:
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def generate_job_description(self, role, experience):
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# Generate a response from the GPT-3 model
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Job Description Must have
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1. Job Title
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2. Job Summary : [200 words]
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4. Required Skills : Six Skills
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5. Qualifications
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These topics must have in that Generated Job Description.
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"""
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prompt=prompt,
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max_tokens=500, # Set the maximum number of tokens in the generated response
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temperature=0.5, # Controls the randomness of the output. Higher values = more random, lower values = more focused
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)
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# Extract the generated text from the API response
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generated_text =
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return generated_text
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@@ -171,13 +192,17 @@ class Resume_Overall:
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# Define the prompt or input for the model
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```{job_description}```
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"""
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# Generate a response from the GPT-3 model
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prompt=prompt,
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max_tokens=200, # Set the maximum number of tokens in the generated response
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temperature=0, # Controls the randomness of the output. Higher values = more random, lower values = more focused
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@@ -186,7 +211,7 @@ class Resume_Overall:
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)
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# Extract the generated text from the API response
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generated_text =
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return generated_text
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import os
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from openai import AzureOpenAI
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import PyPDF2
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import gradio as gr
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import docx
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class Resume_Overall:
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def __init__(self):
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self.client = AzureOpenAI(api_key=os.getenv("AZURE_OPENAI_KEY"),
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api_version="2023-07-01-preview",
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azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
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)
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def extract_text_from_file(self,file_path):
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# Get the file extension
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resume = self.extract_text_from_file(resume_path)
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# Define the prompt or input for the model
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conversation = [
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{"role": "system", "content": "You are a Resume Assistant."},
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{"role": "user", "content": f"""Analyze the resume to generate online courses with website links to improve skills following resume delimitted by triple backticks. Generate atmost five courses.
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result format should be:
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course:[course].
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website link:[website link]
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```{resume}```
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"""}
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]
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# Generate a response from the GPT-3 model
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chat_completion = self.client.chat.completions.create(
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model = "ChatGPT",
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prompt=prompt,
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max_tokens=200,
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temperature=0,
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)
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# Extract the generated text from the API response
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generated_text = chat_completion.choices[0].message.content
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return generated_text
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def summary_response(self,resume_path):
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# Define the prompt or input for the model
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conversation = [
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{"role": "system", "content": "You are a Resume Summarizer."},
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{"role": "user", "content": f"""Analyze the resume to write the summary for following resume delimitted by triple backticks.
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```{resume}```
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"""}
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]
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# Generate a response from the GPT-3 model
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chat_completion = self.client.chat.completions.create(
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model = "ChatGPT",
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prompt=prompt,
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max_tokens=200,
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temperature=0,
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)
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# Extract the generated text from the API response
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generated_text = chat_completion.choices[0].message.content
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return generated_text
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# Define the prompt or input for the model
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conversation = [
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{"role": "system", "content": "You are a Resume Assistant."},
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{"role": "user", "content": f"""Find Education Gaps in given resume. Find Skills in resume.
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```{resume}```
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"""}
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]
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# Generate a response from the GPT-3 model
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chat_completion = self.client.chat.completions.create(
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model = "ChatGPT", # Choose the GPT-3 engine you want to use
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prompt=prompt,
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max_tokens=100, # Set the maximum number of tokens in the generated response
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temperature=0, # Controls the randomness of the output. Higher values = more random, lower values = more focused
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)
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# Extract the generated text from the API response
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generated_text = chat_completion.choices[0].message.content
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return generated_text
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def _generate_job_list(self, resume: str) -> str:
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conversation = [
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{"role": "system", "content": "You are a Resume Assistant."},
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{"role": "user", "content": f"List out perfect job roles for based on resume informations:{resume}"}
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]
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chat_completion = self.client.chat.completions.create(
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model = "ChatGPT",
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prompt=prompt,
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max_tokens=100,
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temperature=0,
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n=1,
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stop=None,
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)
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generated_text = chat_completion.choices[0].message.content
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return generated_text
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def generate_job_description(self, role, experience):
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# Generate a response from the GPT-3 model
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conversation = [
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{"role": "system", "content": "You are a Resume Assistant."},
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{"role": "user", "content": f"""Your task is generate Job description for this {role} with {experience} years of experience.
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Job Description Must have
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1. Job Title
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2. Job Summary : [200 words]
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4. Required Skills : Six Skills
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5. Qualifications
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These topics must have in that Generated Job Description.
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"""}
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]
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chat_completion = self.client.chat.completions.create(
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model = "ChatGPT", # Choose the GPT-3 engine you want to use
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prompt=prompt,
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max_tokens=500, # Set the maximum number of tokens in the generated response
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temperature=0.5, # Controls the randomness of the output. Higher values = more random, lower values = more focused
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)
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# Extract the generated text from the API response
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generated_text = chat_completion.choices[0].message.content
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return generated_text
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# Define the prompt or input for the model
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conversation = [
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{"role": "system", "content": "You are a Resume Assistant."},
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{"role": "user", "content": f"""Generate interview questions for screening following job_description delimitted by triple backticks. Generate atmost ten questions.
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```{job_description}```
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"""}
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]
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# Generate a response from the GPT-3 model
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chat_completion = self.client.chat.completions.create(
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model = "ChatGPT", # Choose the GPT-3 engine you want to use
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prompt=prompt,
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max_tokens=200, # Set the maximum number of tokens in the generated response
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temperature=0, # Controls the randomness of the output. Higher values = more random, lower values = more focused
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)
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# Extract the generated text from the API response
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generated_text = chat_completion.choices[0].message.content
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return generated_text
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