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5f8763a 889beb5 5f8763a 543b0c2 5f8763a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 | import streamlit as st
import pandas as pd
import plotly.express as px
from datetime import datetime
import json
import os
from pathlib import Path
import logging
from typing import List, Dict, Any
from openai import OpenAI
from dotenv import load_dotenv
#from llm_job_assistant import LLMJobAssistant # Our previous class
class JobAssistantUI:
def __init__(self):
self.setup_streamlit()
self.load_dotenv()
self.assistant = LLMJobAssistant()
def setup_streamlit(self):
"""Configure Streamlit page settings"""
st.set_page_config(
page_title="AI Job Search Assistant",
page_icon="🔍",
layout="wide",
initial_sidebar_state="expanded"
)
def load_dotenv(self):
"""Load environment variables"""
os.environ['PATH'] += f':{os.path.expanduser("~/.cargo/bin")}'
load_dotenv()
if not os.getenv('OPENAI_API_KEY'):
st.sidebar.error("OpenAI API key not found. Please set it in .env file")
def render_sidebar(self):
"""Render sidebar controls"""
with st.sidebar:
st.title("Search Settings")
# Job Search Settings
st.subheader("Job Search Criteria")
keywords = st.text_area(
"Search Keywords (one per line)",
value="\n".join(self.assistant.config['keywords'])
)
self.assistant.config['keywords'] = [k.strip() for k in keywords.split("\n") if k.strip()]
# Location Settings
location_type = st.radio(
"Location Type",
["Remote Only", "Hybrid", "All Locations"]
)
# Experience Level
experience_level = st.multiselect(
"Experience Level",
["Entry Level", "Mid Level", "Senior", "Lead"],
default=["Entry Level", "Mid Level"]
)
# Salary Range
min_salary = st.slider(
"Minimum Salary (USD)",
0, 200000, self.assistant.config['minimum_salary'],
step=5000
)
# Save Settings
if st.button("Save Settings"):
self.assistant.config['minimum_salary'] = min_salary
self.assistant.save_config()
st.success("Settings saved!")
def render_job_search_tab(self):
"""Render job search tab"""
st.header("Job Search")
col1, col2 = st.columns([2, 1])
with col1:
if st.button("Start New Job Search", type="primary"):
with st.spinner("Searching for jobs..."):
jobs_df = self.assistant.run_enhanced_job_search()
st.session_state['jobs_df'] = jobs_df
st.success(f"Found {len(jobs_df)} matching jobs!")
with col2:
if st.button("Load Previous Results"):
try:
jobs_df = pd.read_pickle('enhanced_jobs.pkl')
st.session_state['jobs_df'] = jobs_df
st.success("Previous results loaded!")
except FileNotFoundError:
st.error("No previous results found")
if 'jobs_df' in st.session_state:
self.display_job_results(st.session_state['jobs_df'])
def display_job_results(self, df: pd.DataFrame):
"""Display job search results"""
st.subheader("Search Results")
# Filters
col1, col2, col3 = st.columns(3)
with col1:
companies = st.multiselect(
"Filter by Company",
options=sorted(df['company'].unique())
)
with col2:
min_match = st.slider(
"Minimum Match Score",
0, 100, 50
)
with col3:
sort_by = st.selectbox(
"Sort by",
["Match Score", "Company", "Date Posted"]
)
# Filter DataFrame
filtered_df = df.copy()
if companies:
filtered_df = filtered_df[filtered_df['company'].isin(companies)]
filtered_df = filtered_df[filtered_df['analysis.match_score'] >= min_match]
# Sort DataFrame
if sort_by == "Match Score":
filtered_df = filtered_df.sort_values('analysis.match_score', ascending=False)
elif sort_by == "Company":
filtered_df = filtered_df.sort_values('company')
else:
filtered_df = filtered_df.sort_values('date_scraped', ascending=False)
# Display results
for _, job in filtered_df.iterrows():
with st.expander(f"{job['title']} at {job['company']} - Match: {job['analysis']['match_score']}%"):
col1, col2 = st.columns([2, 1])
with col1:
st.write("**Job Description:**")
st.write(job['full_description'])
st.write("**Required Skills:**")
for skill in job['analysis']['required_skills']:
st.markdown(f"- {skill}")
with col2:
st.write("**Salary Range:**")
st.write(job['analysis']['estimated_salary_range'])
st.write("**Experience Required:**")
st.write(job['analysis']['required_experience'])
if st.button("Generate Application Materials", key=job['url']):
with st.spinner("Generating materials..."):
cover_letter = self.assistant.generate_custom_cover_letter(
job['analysis'],
job['company']
)
resume_suggestions = self.assistant.tailor_resume(job['analysis'])
st.download_button(
"Download Cover Letter",
cover_letter,
file_name=f"cover_letter_{job['company']}.txt"
)
st.download_button(
"Download Resume Suggestions",
resume_suggestions,
file_name=f"resume_suggestions_{job['company']}.txt"
)
def render_analytics_tab(self):
"""Render analytics tab"""
st.header("Job Search Analytics")
if 'jobs_df' in st.session_state:
df = st.session_state['jobs_df']
col1, col2 = st.columns(2)
with col1:
# Match Score Distribution
fig = px.histogram(
df,
x='analysis.match_score',
title='Distribution of Match Scores',
labels={'analysis.match_score': 'Match Score'}
)
st.plotly_chart(fig)
with col2:
# Company Distribution
company_counts = df['company'].value_counts().head(10)
fig = px.bar(
company_counts,
title='Top Companies',
labels={'value': 'Number of Jobs', 'index': 'Company'}
)
st.plotly_chart(fig)
# Salary Distribution
fig = px.box(
df,
y='analysis.estimated_salary_range',
title='Salary Distribution'
)
st.plotly_chart(fig)
def render_settings_tab(self):
"""Render settings tab"""
st.header("Application Settings")
# Resume Upload
st.subheader("Resume")
resume_file = st.file_uploader("Upload your resume (TXT format)", type=['txt'])
if resume_file:
resume_text = resume_file.read().decode()
with open('templates/base_resume.txt', 'w') as f:
f.write(resume_text)
st.success("Resume uploaded successfully!")
# API Settings
st.subheader("API Settings")
api_key = st.text_input(
"OpenAI API Key",
value=os.getenv('OPENAI_API_KEY', ''),
type="password"
)
if st.button("Save API Key"):
with open('.env', 'w') as f:
f.write(f"OPENAI_API_KEY={api_key}")
st.success("API key saved!")
def run(self):
"""Run the Streamlit application"""
st.title("AI Job Search Assistant")
# Render sidebar
self.render_sidebar()
# Main content tabs
tab1, tab2, tab3 = st.tabs(["Job Search", "Analytics", "Settings"])
with tab1:
self.render_job_search_tab()
with tab2:
self.render_analytics_tab()
with tab3:
self.render_settings_tab()
def main():
app = JobAssistantUI()
app.run()
if __name__ == "__main__":
main()
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