Upload readme.md

readme.md

@@ -0,0 +1,70 @@
+# Candidate Recommendation Engine powered by LLM
+
+## 📌 Overview
+
+This Candidate Recommendation Engine ranks and summarizes resumes against a given job description using Natural Language Processing (NLP) and semantic similarity techniques. It is designed to assist recruiters and hiring managers in quickly identifying top candidates from a pool of resumes.
+
+The application:
+
+- Accepts a **job description** (text input).
+- Accepts **multiple resumes** (PDF, DOCX, or TXT files).
+- Extracts and cleans resume text.
+- Generates semantic embeddings using **sentence-transformers**.
+- Calculates **cosine similarity** between each resume and the job description.
+- Uses a lightweight LLM (**MBZUAI/LaMini-Flan-T5-248M**) for a short, human-readable explanation of why the candidate is a good fit.
+- Displays ranked results with:
+  - **Candidate Name** (from resume or filename)
+  - **File Name**
+  - **Similarity Score**
+  - **Summary**
+
+---
+
+## 🛠 Approach
+
+- **Text Extraction**  
+  - PDF resumes → parsed using PyPDF2  
+  - DOCX resumes → parsed using python-docx  
+  - TXT resumes → directly read as text  
+  - Minimal cleaning (remove extra spaces, special characters where needed)
+
+- **Embedding Generation**  
+  - Used **all-MiniLM-L6-v2** from sentence-transformers for generating 384-dimensional embeddings.  
+  - Generated embeddings for:  
+    - Job description (once)  
+    - Each resume (individually)
+
+- **Similarity Calculation**  
+  - Computed cosine similarity between the job description embedding and each resume embedding.  
+  - Higher score = higher semantic similarity to the job description.
+
+- **Candidate Name Extraction**  
+  - Tried extracting candidate’s name from the first 3 lines of the resume (common format for names at the top).  
+  - If not found, fell back to filename without extension.
+
+- **Summary Generation**  
+  - Used **MBZUAI/LaMini-Flan-T5-248M** LLM for generating short summaries.  
+  - Prompt: *"Why is this candidate a good fit for the given job description?"*  
+  - Helps recruiters understand the matching context, not just the score.
+
+---
+
+## 📋 Assumptions
+
+- Resumes are in English.  
+- Resume files are well-formatted (name near top, text extractable).  
+- Similarity score is a proxy for relevance — not a hiring decision.  
+- LLM summarization works best with cleaned, relevant resume sections.  
+- Job descriptions are detailed enough for meaningful comparison.
+
+---
+
+## ⚠ Limitations
+
+- Cannot perfectly handle image-based (scanned) resumes without OCR.  
+- Candidate name extraction may fail if resumes have unconventional formatting.  
+- LLM summaries depend on model capability — may occasionally be generic.  
+- Cosine similarity does not account for specific skill weights (all terms treated equally).  
+- Large file uploads may impact performance on free hosting tiers.
+
+---