Update README.md

README.md

@@ -1,69 +1,96 @@
-Project: PRECISE-GBM - Model training & retraining helpers
-
-Overview
-
-This repository contains code to train models (Gaussian Mixture labelling + SVM and ensemble classifiers) and to persist all artifacts required to reproduce or retrain models on new data. It includes:
-
-- `Scenario_heldout_final_PRECISE.py` — training pipeline producing `.joblib` models and metadata JSONs (selected features, best params, CV results).
-- `retrain_helper.py` — CLI utility to rebuild pipelines, set best params and retrain using saved selected-features and params JSONs. Supports JSON/YAML config files and auto-detection of model type.
-- `README_RETRAIN.md` — detailed retrain examples and a notebook cell.
-
-This repo also includes helper files to make it ready for GitHub:
-- `requirements.txt` — Python dependencies
-- `.gitignore` — recommended ignores (models, caches, logs)
-- `LICENSE` — MIT license
-- GitHub Actions workflow for CI (pytest smoke test)
-
-Getting started (Windows PowerShell)
-
-1) Create and activate a virtual environment
-
-```powershell
-python -m venv .venv
-.\.venv\Scripts\Activate.ps1
-```
-
-2) Install dependencies
-
-```powershell
-pip install --upgrade pip
-pip install -r requirements.txt
-```
-
-3) Run training (note: the training script reads data from absolute paths configured in the script — adjust them or run from an environment where those files are present)
-
-```powershell
-python Scenario_heldout_final_PRECISE.py
-```
-
-The training script will create model files under `models_LM22/` and `models_GBM/` and write metadata JSONs next to each joblib model (selected features, params, cv results) as well as group-level JSON summaries.
-
-Retraining
-
-See `README_RETRAIN.md` for detailed CLI and notebook examples. Short example:
-
-```powershell
-python retrain_helper.py \
-  --model-prefix "models_GBM/scenario_1/GBM_scen1_Tcell" \
-  --train-csv "data\new_train.csv" \
-  --label-col "label"
-```
-
-Notes
-
-- The training script contains hard-coded absolute paths to data files. Before running on another machine, update the `scenarios_*` file paths or place the datasets in the same paths.
-- Retrain helper auto-detects model type when `--model-type` is omitted by looking for `{prefix}_svm_params.json` or `{prefix}_ens_params.json`.
-- YAML config support for retrain requires PyYAML (`pip install pyyaml`).
-
-CI
-
-A basic GitHub Actions workflow runs a smoke pytest to ensure the retrain helper imports and basic pipeline construction works. It does not run heavy training.
-
-Contributing
-
-See `CONTRIBUTING.md` for guidance on opening issues and PRs.
-
-License
-
-This project is released under the MIT License — see `LICENSE`.
-
+---
+license: apache-2.0
+language:
+- en
+tags:
+- api
+- brain
+- cancer
+- trails
+- finder
+- app
+---
+<p align="center"> <b>Brain Cancer Trials Finder</b> </p>
+
+<p align="center">
+  <img src="logo_precise.png" alt="PRECISE-GBM Logo">
+</p>
+
+
+Desktop Application, Streamlit app and code
+
+# Brain Cancer Trails Finder (Desktop Application)
+
+The link for download is here: (Coming soon)
+
+This application uses API with Clinical Trials Registry at NIH (ClinicalTrails.gov, USA) and provides scores according to the type of brain cancer input on the GUI. 
+It also provides links directly to UK cancer registers and search is performed automatically on the type of cancer selected (CRUK, NIHR, ISRCTN(UK) ) 
+
+
+# Brain Cancer Trials Finder (Streamlit)
+
+A minimal Streamlit app that discovers actively recruiting neuro-oncology clinical trials (e.g., glioblastoma, brain tumors) from the ClinicalTrials.gov v2 API and ranks them with simple, explainable heuristics.
+
+Demo features:
+- Filters by country and basic patient factors (age, KPS, setting).
+- Pulls live data from ClinicalTrials.gov v2.
+- Scores trials with transparent reasons for the score.
+
+## Local run
+
+Prereqs: Python 3.9+.
+
+```bash
+python -m venv .venv
+.venv\Scripts\activate
+pip install -r requirements.txt
+streamlit run streamlit_app.py
+```
+
+## Deploy to Streamlit Community Cloud
+
+1) Push this folder to a new GitHub repository (see steps below).
+2) Go to https://share.streamlit.io/ and connect your GitHub account.
+3) Create a new app, select your repo, branch (e.g., `main`), and set the main file path to `streamlit_app.py`.
+4) Click Deploy. Streamlit will build from `requirements.txt` automatically.
+
+## GitHub repository setup (Windows, cmd.exe)
+
+1) Create an empty repo on GitHub (e.g., `brain-trials-finder`). Do not add any files there.
+2) In this project folder, run:
+
+```bat
+git init
+git add .
+git commit -m "Initial commit: Streamlit Brain Cancer Trials Finder"
+git branch -M main
+REM Replace <YOUR-USERNAME> and repo name below
+git remote add origin https://github.com/<YOUR-USERNAME>/brain-trials-finder.git
+git push -u origin main
+```
+
+If you prefer SSH:
+
+```bat
+git remote remove origin 2> NUL
+git remote add origin git@github.com:<YOUR-USERNAME>/brain-trials-finder.git
+git push -u origin main
+```
+
+## Configuration
+
+- No secrets are required. All data is pulled from the public ClinicalTrials.gov API.
+- If you want to pin Python version on Streamlit Cloud, add a `runtime.txt` (e.g., `python-3.10`), though it’s optional.
+
+## Files
+
+- `streamlit_app.py` — Streamlit entrypoint used by Streamlit Cloud.
+- `GUI_CLinicalTrial.py` — Original app file; kept for development, but `streamlit_app.py` mirrors it for deployment.
+- `requirements.txt` — Python dependencies.
+- `.gitignore` — Ignores typical Python/venv artifacts.
+
+## Disclaimer
+
+This tool provides assistive information only and is not a substitute for professional medical advice. Always discuss clinical trials with your clinician.
+
+Copyright: Prajwal Ghimire