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README.md

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-# 🧬 CANLoc — Protein Subcellular Localization Predictor
-
-CANLoc is a production-ready machine learning web application for predicting the **subcellular localization of proteins** directly from amino acid sequences.  
-It provides accurate, fast, and interpretable predictions through a modern deep-learning–assisted pipeline and an interactive web interface.
-
----
-
-## 🔬 Model Overview
-
-CANLoc combines:
-
-- **ESM2 (Transformer-based protein language model)**  
-  Used for extracting rich sequence embeddings without alignment.
-
-- **Mean pooling of residue embeddings**  
-  Produces fixed-length feature vectors.
-
-- **XGBoost classifier**  
-  Trained on curated protein datasets for robust multiclass prediction.
-
-### Predicted Classes
-- Cytoplasm  
-- Nucleus  
-- Membrane  
-- Mitochondria  
-
-Each prediction includes **class probabilities** and **confidence visualization.**
-
----
-
-## 📊 Features
-
-- Single sequence prediction
-- Batch prediction via FASTA file upload
-- Probability bar chart and radar plot
-- Confidence-based interpretation
-- Clean, responsive bioinformatics-style UI
-- Dockerized for reproducible deployment
-- FastAPI backend + modern frontend
-
----
-
-## 🧪 Input Formats
-
-### Single Sequence
-Paste a raw amino acid sequence: MVKFKKYGIP...
-
-
-### FASTA File
-Upload a standard FASTA file with one or multiple sequences:
-sp|P25296|CANB_YEAST
-MSLIHPDTAKYPFKFEPF...
-
-
----
-
-## 📈 Output Interpretation
-
-- **Predicted Location**  
-  The most probable subcellular class.
-
-- **Class Probabilities**  
-  Displayed as percentages for all four classes.
-
-- **Confidence Levels**
-  - High: ≥ 75%
-  - Medium: 60–75%
-  - Low: < 60% (interpret with caution)
-
----
-
-## ⚙️ Evaluation & Validation
-
-The model was evaluated using:
-- Train/test split
-- 10-fold stratified cross-validation
-- Precision, recall, F1-score
-- Sensitivity and specificity analysis
-- ROC curves per class
-
-These evaluations confirm CANLoc’s reliability for academic/research workflows..
-
-
----
-
-## 🚀 Deployment
-
-CANLoc is containerized and deployed using **Docker** and **Railway**.
-
-## 📄 License
-This project is licensed under the Apache License 2.0.
-
->Free for academic and commercial use
->Includes patent protection
->No restrictions on deployment or modification
-
-See the LICENSE file for details.
-
-
-## 📬 Contact
-
-For questions, bug report or feedback:
-majidkhan>jssmsc@gmail.com
-
-## 📌 Citation
-
-If you use CANLoc in academic work, please cite appropriately.
-