Replace README.md relevant to Dysgraphia function

README.md

-# LearnJoy-ML
+# Learn Joy - Function 2
 
-Machine Learning Model for Learnjoy app
+## Table of Contents
+[Function 2 Development](#function-2-development)
+  * [1. About Function 2](#1-About-function-2)
+  * [2. Dataset](#2-Dataset-folder)
+  * [3. Function 2 processing & ML Model Development](#3-Function-2-processing-&-ML-Model-Development-folder)
+  * [4. Service Files](#4-Service-Files-folder)
+  * [5. Fast API & app file](#5-Fast-API-&-app-file-folder)
 
-------------------
-## FastAPI Development
+-------------------------------------------------
+## Function 2 Development
 
+### 1.About Function 2
 
-- To run the API, please ensure you have installed the required packages, "fastapi" and "uvicorn." If you haven't already installed them, you can do so using the following commands:
+- This function include one main part:
+- 1.Creating a comprehensive digital platform that easily converts handwritten
+content into digital text with the aim of reducing the difficulties caused by
+Dysgraphia:
 
-  ```bash
-  pip install fastapi
-  pip install "uvicorn[standard]"
-  ```
-
-- Once these packages are installed, you can proceed to run the API.
-
-1. To run the api, use the following command inside the API folder:
+### 2. Dataset ()
+- Within this Kaggle Link, you'll discover data for Alphabets and Digits: only use 5000 training and 1000 testing date for each classes.
   
-   ```bash
-   python main.py
-   ```
+### 3. Function 2 processing & ML Model Development ([Folder](/function2))
 
-2. You can access the project output in your browser using the following URL:
+- Inside this folder, you'll come across two Jupyter Notebooks dedicated to data preprocessing and model training:
 
-   ```bash
-   http://127.0.0.1:8000
-   ```
-3. To explore the API documentation, visit:
+  - `character-recognition.ipynb`: This file primarily involves to training Character recognition model.
+  - 
+  - ` function2.pyind`: This file is responsible for all methods of function 1.
   
-   ```bash
-   http://127.0.0.1:8000/docs
-   ```
 
-------------------------
+### 4. Service Files ()
+- Within this directory, there are one python ".py" file:
 
-## Setting Up the Development Environment
+  - `Handwriting_recognition.py`: This method is responsible for recognized the image for below mention classes.
+    - input : TWO IMAGE FILES  `def Handwriting_recognition(BB_image:str=None,WB_image:str=None)`
+       Note : Add the only one image file for one time other file was None
+    - Attached models : Handwriting_recognition_1.hdf5([Link](https://drive.google.com/file/d/1cVw535rjWQl2SitToPzcjB6UU-dkfU_E/view?usp=drive_link))
+    - Process : Recognized the characters input images for below classes . `['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']
+   ` 
+    - output : Recognized charcter.
   
-Follow these steps to set up your development environment for this project:
+---------------------
 
-### Create a New `venv`
-
-1. Navigate to your project directory:
-
-   ```bash
-   cd /path/to/your/project
+### 5. Fast API & app file ()
+- Within this app.py module include two Fast API end points for function 2 :
+  - 1. Request body : Add the white image in black background
+      ```python
+       @app.post("/function02/recognition_BB")
+       async def function2_recognition(BB_image:UploadFile =File(...)):
        ```
       
-2. Create a virtual environment:
+    - Request URL: ` https://Public_or_local_host.app/function02/recognition_BB`
+    - Response body:
+      ```json
+            {
+        "Recognized Character": "G"
+      }
         
-   ```bash
-   python -m venv <venv_name>
        ```
 
-### Activate and Deactivate `venv`
-
-- In `cmd`:
-
-   ```bash
-   <venv_name>\Scripts\activate
+  - 2. Request body : Add the black image in white background
+      ```python
+       @app.post("/function02/recognition_WB")
+       async def function2_recognition(WB_image:UploadFile =File(...)):
       ```
       
-- In bash:
+    - Request URL: ` https://Public_or_local_host.app/function02/recognition_WB`
+    - Response body:
+      ```json
 
-   ```bash
-   source <venv_name>/Scripts/activate
+               {
+           "Recognized Character": "Q"
+         }
               
-   # To deactivate the virtual environment:
-   deactivate
        ```
 
-### Create, Activate & Deactivate `venv` using conda
-
-- Use Anaconda Navigator to create a venv:
 
-   ```bash
-   # Activate the conda environment
-   conda activate <venv_name>
-
-   # To deactivate the conda environment
-   conda deactivate
-   ```
-
-### Install the Dependencies
-
-- You can also use a `requirements.txt` file to manage your project's dependencies. This file lists all the required packages and their versions.
-1. Install packages from `requirements.txt`:
-
-   ```
-   pip install -r requirements.txt
-   ```
-    This ensures that your development environment matches the exact package versions specified in `requirements.txt`.
-
-2. Verify installed packages:
-
-   ```bash
-   pip list
-   ```
-    This will display a list of packages currently installed in your virtual environment, including the ones from `requirements.txt`.
\ No newline at end of file