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import streamlit as st
from pdf_loader import load_btyes_io
from core import pipeline
# Developer Details
developer_details = {
"Sudhir Sharma": {
"Education": "B.Tech in Computer Science and Engineering, IIT Bhilai, 2024",
"Email": "[email protected]",
"GitHub": "[GitHub Profile](https://github.com/Sudhir878786)",
"LinkedIn": "[LinkedIn Profile](https://www.linkedin.com/in/sudhirsharma87/)"
}
}
def inference(query, files, embedding_type):
# pdfReader = PyPDF2.PdfReader(files[0])
# text = ''
# for page in pdfReader.pages:
# text += page.extract_text()
# st.write(text)
results, _ = pipeline(query, load_btyes_io(files), embedding_type=embedding_type)
prob_per_documents = {result['name']: result['similarity'] for result in results}
return prob_per_documents
st.sidebar.header("Developer Details")
selected_developer = st.sidebar.selectbox("Select a developer", list(developer_details.keys()))
st.sidebar.markdown(developer_details[selected_developer]["Education"])
st.sidebar.markdown(developer_details[selected_developer]["Email"])
st.sidebar.markdown(developer_details[selected_developer]["GitHub"])
st.sidebar.markdown(developer_details[selected_developer]["LinkedIn"])
sample_files = [
"documents/business.pdf",
"documents/data_science.pdf",
]
sample_job_descriptions = {
"Software Engineer": """We are looking for a software engineer with experience in Python and web development. The ideal candidate should have a strong background in building scalable and robust applications. Knowledge of frameworks such as Flask and Django is a plus. Experience with front-end technologies like HTML, CSS, and JavaScript is desirable. The candidate should also have a good understanding of databases and SQL. Strong problem-solving and communication skills are required for this role.
""",
"Data Scientist": """We are seeking a data scientist with expertise in machine learning and statistical analysis. The candidate should have a solid understanding of data manipulation, feature engineering, and model development. Proficiency in Python and popular data science libraries such as NumPy, Pandas, and Scikit-learn is required. Experience with deep learning frameworks like TensorFlow or PyTorch is a plus. Strong analytical and problem-solving skills are essential for this position.
"""
}
st.sidebar.header("Sample Files")
for sample_file in sample_files:
st.sidebar.markdown(f"[{sample_file}](./sample_files/{sample_file})")
st.sidebar.header("Sample Job Descriptions")
selected_job = st.sidebar.selectbox("Select a job description", list(sample_job_descriptions.keys()))
st.sidebar.markdown("```")
st.sidebar.code(sample_job_descriptions[selected_job])
st.title("👨🏼🎓Resume Ranker ")
query = st.text_area("Job Description", height=200, value=sample_job_descriptions[selected_job])
uploaded_files = st.file_uploader("Upload Resume", accept_multiple_files=True, type=["txt", "pdf"])
embedding_type = st.selectbox("Embedding Type", ["bert", "minilm", "tfidf"])
if st.button("Submit"):
if not query:
st.warning("Please enter a job description.")
elif not uploaded_files:
st.warning("Please upload one or more resumes.")
else:
with st.spinner("Processing..."):
results = inference(query, uploaded_files,embedding_type)
st.subheader("Results")
for document, similarity in results.items():
# make similiarty round to 2 decimal place
if similarity >= 1:
similarity = round(similarity, 2)
st.write(f"- {document}:")
st.progress(similarity, text=f"{similarity:.2%}")
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