Spaces:
Sleeping
Sleeping
robertselvam
commited on
Commit
•
f83ca37
1
Parent(s):
8940745
Update app.py
Browse files
app.py
CHANGED
@@ -140,6 +140,56 @@ class Resume_Overall:
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job_list = self._generate_job_list(resume_text)
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return job_list
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def show_file(self,file_path):
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return file_path.name
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@@ -153,20 +203,24 @@ class Resume_Overall:
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upload_button = gr.UploadButton(
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"Browse File",file_types=[".txt", ".pdf", ".doc", ".docx",".json",".csv"],
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elem_classes="uploadbutton")
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btn = gr.Button(value="Submit")
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output_text = gr.Textbox(label="Designation List")
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analyse = gr.Button("Analyze")
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summary_result = gr.Textbox(label="Summarized",lines=8)
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analyse_resume = gr.Button("Analyze Resume")
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result = gr.Textbox(label="Skills and Education Gaps",lines=8)
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course_analyse = gr.Button("Find Courses")
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course_result = gr.Textbox(label="Suggested Cources",lines=8)
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upload_button.upload(self.show_file,upload_button,file_output)
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course_analyse.click(self.course_response, [upload_button], course_result)
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analyse_resume.click(self.skill_response, [upload_button], result)
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@@ -181,11 +235,26 @@ class Resume_Overall:
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upload_button1 = gr.UploadButton(
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"Browse File",file_types=[".txt", ".pdf", ".doc", ".docx",".json",".csv"],
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elem_classes="uploadbutton")
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with gr.
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with gr.
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app.launch(debug=True)
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if __name__ == "__main__":
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job_list = self._generate_job_list(resume_text)
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return job_list
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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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prompt = 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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3. Responsibilities : Five Responsibilities in five lines
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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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response = openai.Completion.create(
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engine='text-davinci-003', # 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 = response.choices[0].text.strip()
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return generated_text
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def response(self,job_description_path):
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job_description_path = job_description_path.name
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job_description = self.extract_text_from_file(job_description_path)
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# Define the prompt or input for the model
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prompt = 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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# Generate a response from the GPT-3 model
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response = openai.Completion.create(
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engine='text-davinci-003', # 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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n=1, # Generate a single response
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stop=None, # Specify an optional stop sequence to limit the length of the response
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)
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# Extract the generated text from the API response
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generated_text = response.choices[0].text.strip()
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return generated_text
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def show_file(self,file_path):
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return file_path.name
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upload_button = gr.UploadButton(
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"Browse File",file_types=[".txt", ".pdf", ".doc", ".docx",".json",".csv"],
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elem_classes="uploadbutton")
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with gr.TabItem("Designation"):
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with gr.Column(elem_id = "col-container",scale=0.80):
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btn = gr.Button(value="Submit")
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output_text = gr.Textbox(label="Designation List")
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with gr.TabItem("Summarized"):
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with gr.Column(elem_id = "col-container",scale=0.80):
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analyse = gr.Button("Analyze")
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summary_result = gr.Textbox(label="Summarized",lines=8)
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with gr.TabItem("Skills and Education Gaps"):
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with gr.Column(elem_id = "col-container",scale=0.80):
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analyse_resume = gr.Button("Analyze Resume")
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result = gr.Textbox(label="Skills and Education Gaps",lines=8)
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with gr.TabItem("Course"):
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with gr.Column(elem_id = "col-container",scale=0.80):
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course_analyse = gr.Button("Find Courses")
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course_result = gr.Textbox(label="Suggested Cources",lines=8)
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upload_button.upload(self.show_file,upload_button,file_output)
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course_analyse.click(self.course_response, [upload_button], course_result)
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analyse_resume.click(self.skill_response, [upload_button], result)
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upload_button1 = gr.UploadButton(
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"Browse File",file_types=[".txt", ".pdf", ".doc", ".docx",".json",".csv"],
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elem_classes="uploadbutton")
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with gr.TabItem("Screening Question"):
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with gr.Row():
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with gr.TabItem("Screening Question"):
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jd_btn = gr.Button(value="Submit")
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output_text1 = gr.Textbox(label="Screening Question")
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with gr.TabItem("Screening Question"):
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with gr.Row():
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with gr.Column():
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rolls = gr.Textbox(label="Role")
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with gr.Column():
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experience = gr.Textbox(label="Experience")
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with gr.Row():
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get_jd_btn = gr.Button("Generate JD")
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with gr.Row():
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result_jd = gr.Textbox(label="Job Description",lines=8)
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get_jd_btn.click(self.generate_job_description, [rolls,experience], result_jd)
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upload_button1.upload(self.show_file,upload_button1,file_output1)
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jd_btn.click(self.response,[upload_button1], output_text1)
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app.launch(debug=True)
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if __name__ == "__main__":
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