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import streamlit as st
import os
import tempfile
from pathlib import Path
import time
from typing import List, Dict
import pandas as pd
# Initialize Streamlit app
st.set_page_config(
page_title="IntraTalent Resume Processor",
page_icon="📄",
layout="wide"
)
def save_uploaded_file(uploaded_file) -> str:
"""Save uploaded file to temporary directory and return path."""
try:
with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as tmp_file:
tmp_file.write(uploaded_file.getvalue())
return tmp_file.name
except Exception as e:
st.error(f"Error saving file: {e}")
return None
def process_resumes(uploaded_files: List[st.UploadedFile]) -> Dict:
"""Process multiple resumes and return results."""
results = {}
progress_bar = st.progress(0)
for idx, file in enumerate(uploaded_files):
if file.type != "application/pdf":
st.warning(f"Skipping {file.name}: Not a PDF file")
continue
temp_path = save_uploaded_file(file)
if temp_path:
try:
name, projects = parse_resume(temp_path)
results[file.name] = {
"name": name,
"projects": projects
}
# Update progress
progress_bar.progress((idx + 1) / len(uploaded_files))
except Exception as e:
st.error(f"Error processing {file.name}: {e}")
finally:
# Clean up temporary file
os.unlink(temp_path)
return results
def display_results(results: Dict):
"""Display processed resume results in an organized manner."""
if not results:
return
st.subheader("Processed Resumes")
for filename, data in results.items():
with st.expander(f"📄 {data['name']} ({filename})"):
if data['projects']:
df = pd.DataFrame(data['projects'])
st.dataframe(
df,
column_config={
"name": "Project Name",
"description": "Description"
},
hide_index=True
)
else:
st.info("No projects found in this resume")
def main():
st.title("IntraTalent Resume Processor")
# File uploader section
st.header("Upload Resumes")
uploaded_files = st.file_uploader(
"Upload up to 10 resumes (PDF only)",
type=['pdf'],
accept_multiple_files=True,
key="resume_uploader"
)
# Validate number of files
if len(uploaded_files) > 10:
st.error("Maximum 10 files allowed. Please remove some files.")
return
# Process button
if uploaded_files and st.button("Process Resumes"):
with st.spinner("Processing resumes..."):
results = process_resumes(uploaded_files)
st.session_state['processed_results'] = results
display_results(results)
# Query section
st.header("Search Projects")
query = st.text_area(
"Enter your project requirements",
placeholder="Example: Looking for team members with experience in machine learning and computer vision...",
height=100
)
if query and st.button("Search"):
if 'processed_results' not in st.session_state:
st.warning("Please process some resumes first!")
return
with st.spinner("Searching for matches..."):
# Here you would implement the embedding and similarity search
# Using the code from your original script
st.success("Search completed!")
# Display results in a nice format
st.subheader("Top Matches")
# Placeholder for search results
st.info("Feature coming soon: Will display matching projects and candidates based on similarity search")
if __name__ == "__main__":
main() |