From aef7ce058b0d93e488f6ee9c1908cfc3c7bf3928 Mon Sep 17 00:00:00 2001
From: Gabriel Caldas <87482328+gabrielgcbs@users.noreply.github.com>
Date: Tue, 13 Jun 2023 03:25:41 -0300
Subject: [PATCH 1/2] Update README.md

---
 README.md | 83 ++++++++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 82 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index bfb8bbe..f16db00 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,83 @@
-# predict-me
+# 🤖 Predict4me
 A web Artificial Intelligence model for everyone - just upload your dataset and let it predict for you
+
+---
+
+ℹ️ **ATTENTION**: The fork on [@gcbsdev](https://github.com/gcbsdev) is meant to be read-only and its purpose is for streaming the app to [Streamlit Community Cloud](https://streamlit.io/cloud). When submitting a Pull Request or opening and Issue, please always refer to the repo in [@gabrielgcbs](https://github.com/gabrielgcbs/predict-for-me)
+
+## ❓ What is Predict4me?
+Predict4me is a web Machine Learning app built with Python and [Streamlit](https://streamlit.io)  
+  
+It implements a simple user-friendly interface that let's you upload your data and click one button to train a model and predict the data.
+
+> Note that the purporse of Predict4me is to help those who are not experienced with Machine Learning predict their data without needing to code a model, thus it is not suited for more complex predictions or model engineering.
+
+Currently, the app covers only the two Supervised Learning problems, which are:
+- Classification
+- Regression
+
+The models implemented are:
+- Random Forest (for classification tasks)
+- Histogram-based Gradient Boosting Regression Tree (for regression tasks)
+
+ℹ️ For more information about how these models work, please refer to scikit-learn official documentation: [RandomForest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier), [HistGradientBoostingRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#sklearn.ensemble.HistGradientBoostingRegressor)
+
+## 🏁 Quick Start
+
+### 🏎️ How to run the app on your browser?
+
+- Visit [predict4me app](https://gcbsdev-predict-for-me.streamlit.app/)
+- Select the type of task you wish: **Classification** or **Regression** (not sure which one to choose? Have a look at [this tutorial](https://machinelearningmastery.com/classification-versus-regression-in-machine-learning/))
+- Upload your data:
+  - The requirements to get the model running on your data are:
+    - File format: csv
+    - The data needs to have at least one feature column
+    - The data needs to have only one target column (this may change in the future)
+    - Nominal categorical features **must** be specified, if there are any
+    - Numerical categorical features **do not** need to be specified
+    - It is advised to clean the data before jumping on the model
+  - You must submit one file for training the model (step 1), and one file containg the actual data you want to predict (step 2)
+    - For example, if you want to predict the house pricing on a city, provide a dataset with information about houses in the area and their prices on step 1, and a dataset with the information about the house you want to predict on step 2
+- If there are any nominal features (i.e. *strings*), provide their names on step 3
+- Click on *RUN MODEL*
+- Save the results as a csv file
+
+### 💻 How to install and run the app locally?
+Install Python >= 3.9  
+
+Clone the repository:
+``` shell
+$ git clone https://github.com/gabrielgcbs/predict-for-me
+```
+
+ℹ️ Recommended: Create and activate a virtual environment
+
+On Windows:  
+``` shell
+C:\> python -m venv .venv
+C:\> .venv\Scripts\activate.bat
+```
+
+On Linux:
+``` shell
+$ python -m venv .venv
+$ source .venv/bin/activate
+```
+
+Install the dependencies:
+``` shell
+$ pip install requirements.txt
+```
+
+Run the app:
+``` shell
+$ streamlit run src/main.py
+```
+
+### ❤️ Guidelines for contributing
+
+Contributions are welcomed. For that, please follow this simple guidelines:
+- Check if there is already an issue opened
+- If not, create an issue to describe your request and specify a label for it, e.g *bug*, *feature* or *documentation*
+- If you want to make some changes on the code, create a pull request and link it to an existing issue:
+  - Documentation is important, so for each significant changes made on the code, please provide a documentation about it, both in-code and on the pull request itself

From 5eff6dac1782d729c3a714e316be2be9596165e9 Mon Sep 17 00:00:00 2001
From: Gabriel Caldas <87482328+gabrielgcbs@users.noreply.github.com>
Date: Tue, 13 Jun 2023 03:27:43 -0300
Subject: [PATCH 2/2] Update Predict4Me name on web app

---
 src/main.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/main.py b/src/main.py
index bbfda16..450cbcb 100644
--- a/src/main.py
+++ b/src/main.py
@@ -163,7 +163,7 @@ def main():
 
     st.write(
         """
-            # :robot_face: PredictMe
+            # :robot_face: Predict4Me
             > A web Artificial Intelligence model for everyone - just upload your dataset and let it predict for you
         """
     )
@@ -259,4 +259,4 @@ def main():
     )
         
 if __name__ == '__main__':
-    main()
\ No newline at end of file
+    main()