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datasciencedojo
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bbd5462
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Parent(s):
65ac5d4
Update app.py
Browse files
app.py
CHANGED
@@ -1,6 +1,6 @@
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import streamlit as st
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from utils.
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import pyperclip
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from dotenv import load_dotenv
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import pandas as pd
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@@ -140,7 +140,7 @@ def main():
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st.title("Job Hiring Assistant")
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# Select
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st.sidebar.header("Select Task")
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selection = st.sidebar.radio("Select option", ("Generate Job Adverstisment", "Resume Analysis","Resume Selection"))
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@@ -154,7 +154,7 @@ def main():
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job_requirement = st.text_area("Enter job requirement here")
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if st.button("Generate Job Adverstisment"):
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if job_requirement is not None:
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prompt_template =
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jobadv = generate_adv(job_requirement,job_title_text, prompt_template)
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st.subheader("Job Adverstisment:")
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st.markdown(jobadv)
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@@ -217,7 +217,7 @@ def main():
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if st.session_state.input_hash != current_input_hash:
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# Inputs have changed, generate new analysis
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st.session_state.input_hash = current_input_hash
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prompt_template =
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response = generate_analysis(resume_text, job_pref, job_title_text, must_have, prompt_template)
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# Cache the result
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@@ -253,7 +253,7 @@ def main():
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if st.session_state.input_hash_sel != current_input_hash:
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# Inputs have changed, generate new analysis
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st.session_state.input_hash_sel = current_input_hash
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prompt_template =
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response = generate_sel_analysis(resume_text, job_pref, job_title_text, must_have, prompt_template)
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print('response:',response)
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response_anal=max(response, key=lambda x: x['overall_match_score'])
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import streamlit as st
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from utils.utils import *
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import prompts
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import pyperclip
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from dotenv import load_dotenv
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import pandas as pd
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st.title("Job Hiring Assistant")
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# Select Task
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st.sidebar.header("Select Task")
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selection = st.sidebar.radio("Select option", ("Generate Job Adverstisment", "Resume Analysis","Resume Selection"))
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job_requirement = st.text_area("Enter job requirement here")
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if st.button("Generate Job Adverstisment"):
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if job_requirement is not None:
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prompt_template = prompts.prompt_template_classic
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jobadv = generate_adv(job_requirement,job_title_text, prompt_template)
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st.subheader("Job Adverstisment:")
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st.markdown(jobadv)
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if st.session_state.input_hash != current_input_hash:
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# Inputs have changed, generate new analysis
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st.session_state.input_hash = current_input_hash
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prompt_template = prompts.prompt_template_modern
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response = generate_analysis(resume_text, job_pref, job_title_text, must_have, prompt_template)
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# Cache the result
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if st.session_state.input_hash_sel != current_input_hash:
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# Inputs have changed, generate new analysis
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st.session_state.input_hash_sel = current_input_hash
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prompt_template = prompts.prompt_template_resumes_
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response = generate_sel_analysis(resume_text, job_pref, job_title_text, must_have, prompt_template)
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print('response:',response)
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response_anal=max(response, key=lambda x: x['overall_match_score'])
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