wehub-resource-sync
233 lines
8.8 KiB
Python
233 lines
8.8 KiB
Python
import streamlit as st
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import os
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from llama_index.core import SimpleDirectoryReader, Settings, VectorStoreIndex
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from llama_index.embeddings.nebius import NebiusEmbedding
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from llama_index.llms.nebius import NebiusLLM
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from dotenv import load_dotenv
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import tempfile
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import shutil
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import base64
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from PyPDF2 import PdfReader
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import io
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# Load environment variables
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load_dotenv()
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def run_rag_completion(
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documents,
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query_text: str,
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job_title: str,
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job_description: str,
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embedding_model: str = "BAAI/bge-en-icl",
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generative_model: str = "Qwen/Qwen3-235B-A22B"
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) -> str:
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"""Run RAG completion using Nebius models for resume optimization."""
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try:
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llm = NebiusLLM(
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model=generative_model,
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api_key=os.getenv("NEBIUS_API_KEY")
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)
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embed_model = NebiusEmbedding(
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model_name=embedding_model,
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api_key=os.getenv("NEBIUS_API_KEY")
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)
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Settings.llm = llm
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Settings.embed_model = embed_model
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# Step 1: Analyze the resume
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analysis_prompt = f"""
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Analyze this resume in detail. Focus on:
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1. Key skills and expertise
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2. Professional experience and achievements
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3. Education and certifications
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4. Notable projects or accomplishments
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5. Career progression and gaps
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Provide a concise analysis in bullet points.
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"""
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index = VectorStoreIndex.from_documents(documents)
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resume_analysis = index.as_query_engine(similarity_top_k=5).query(analysis_prompt)
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# Step 2: Generate optimization suggestions
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optimization_prompt = f"""
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Based on the resume analysis and job requirements, provide specific, actionable improvements.
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Resume Analysis:
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{resume_analysis}
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Job Title: {job_title}
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Job Description: {job_description}
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Optimization Request: {query_text}
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Provide a direct, structured response in this exact format:
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## Key Findings
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• [2-3 bullet points highlighting main alignment and gaps]
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## Specific Improvements
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• [3-5 bullet points with concrete suggestions]
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• Each bullet should start with a strong action verb
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• Include specific examples where possible
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## Action Items
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• [2-3 specific, immediate steps to take]
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• Each item should be clear and implementable
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Keep all points concise and actionable. Do not include any thinking process or analysis.
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"""
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optimization_suggestions = index.as_query_engine(similarity_top_k=5).query(optimization_prompt)
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return str(optimization_suggestions)
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except Exception as e:
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raise
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def display_pdf_preview(pdf_file):
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"""Display PDF preview in the sidebar."""
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try:
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st.sidebar.subheader("Resume Preview")
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base64_pdf = base64.b64encode(pdf_file.getvalue()).decode('utf-8')
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pdf_display = f'<iframe src="data:application/pdf;base64,{base64_pdf}" width="100%" height="500" type="application/pdf"></iframe>'
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st.sidebar.markdown(pdf_display, unsafe_allow_html=True)
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return True
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except Exception as e:
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st.sidebar.error(f"Error previewing PDF: {str(e)}")
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return False
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def main():
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st.set_page_config(page_title="Resume Optimizer", layout="wide")
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# Initialize session states
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if "messages" not in st.session_state:
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st.session_state.messages = []
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if "docs_loaded" not in st.session_state:
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st.session_state.docs_loaded = False
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if "temp_dir" not in st.session_state:
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st.session_state.temp_dir = None
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if "current_pdf" not in st.session_state:
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st.session_state.current_pdf = None
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# Header
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st.title("📝 Resume Optimizer")
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st.caption("Powered by Nebius AI")
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# Sidebar for configuration
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with st.sidebar:
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st.image("./Nebius.png", width=150)
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# Model selection
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generative_model = st.selectbox(
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"Generative Model",
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["Qwen/Qwen3-235B-A22B", "deepseek-ai/DeepSeek-V3"],
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index=0
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)
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st.divider()
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# Resume upload
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st.subheader("Upload Resume")
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uploaded_file = st.file_uploader(
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"Choose your resume (PDF)",
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type="pdf",
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accept_multiple_files=False
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)
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# Handle PDF upload and processing
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if uploaded_file is not None:
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if uploaded_file != st.session_state.current_pdf:
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st.session_state.current_pdf = uploaded_file
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try:
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if not os.getenv("NEBIUS_API_KEY"):
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st.error("Missing Nebius API key")
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st.stop()
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# Create temporary directory for the PDF
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if st.session_state.temp_dir:
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shutil.rmtree(st.session_state.temp_dir)
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st.session_state.temp_dir = tempfile.mkdtemp()
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# Save uploaded PDF to temp directory
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file_path = os.path.join(st.session_state.temp_dir, uploaded_file.name)
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with open(file_path, "wb") as f:
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f.write(uploaded_file.getbuffer())
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with st.spinner("Loading Resume..."):
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documents = SimpleDirectoryReader(st.session_state.temp_dir).load_data()
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st.session_state.docs_loaded = True
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st.session_state.documents = documents
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st.success("✓ Resume loaded successfully")
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display_pdf_preview(uploaded_file)
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except Exception as e:
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st.error(f"Error: {str(e)}")
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# Main content area
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col1, col2 = st.columns([1, 1])
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with col1:
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st.subheader("Job Information")
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job_title = st.text_input("Job Title")
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job_description = st.text_area("Job Description", height=200)
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st.subheader("Optimization Options")
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optimization_type = st.selectbox(
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"Select Optimization Type",
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[
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"ATS Keyword Optimizer",
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"Experience Section Enhancer",
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"Skills Hierarchy Creator",
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"Professional Summary Crafter",
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"Education Optimizer",
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"Technical Skills Showcase",
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"Career Gap Framing"
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]
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)
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if st.button("Optimize Resume"):
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if not st.session_state.docs_loaded:
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st.error("Please upload your resume first")
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st.stop()
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if not job_title or not job_description:
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st.error("Please provide both job title and description")
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st.stop()
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# Generate optimization prompt based on selection
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prompts = {
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"ATS Keyword Optimizer": "Identify and optimize ATS keywords. Focus on exact matches and semantic variations from the job description.",
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"Experience Section Enhancer": "Enhance experience section to align with job requirements. Focus on quantifiable achievements.",
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"Skills Hierarchy Creator": "Organize skills based on job requirements. Identify gaps and development opportunities.",
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"Professional Summary Crafter": "Create a targeted professional summary highlighting relevant experience and skills.",
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"Education Optimizer": "Optimize education section to emphasize relevant qualifications for this position.",
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"Technical Skills Showcase": "Organize technical skills based on job requirements. Highlight key competencies.",
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"Career Gap Framing": "Address career gaps professionally. Focus on growth and relevant experience."
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}
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with st.spinner("Analyzing resume and generating suggestions..."):
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try:
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response = run_rag_completion(
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st.session_state.documents,
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prompts[optimization_type],
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job_title,
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job_description,
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"BAAI/bge-en-icl",
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generative_model
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)
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# Remove think tags from response
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response = response.replace("<think>", "").replace("</think>", "")
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st.session_state.messages.append({"role": "assistant", "content": response})
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except Exception as e:
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st.error(f"Error: {str(e)}")
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st.divider()
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with col2:
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st.subheader("Optimization Results")
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for message in st.session_state.messages:
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st.markdown(message["content"])
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if __name__ == "__main__":
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main()
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