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Runtime error
| 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%}") | |