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-# 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.
-
----