Replace README.md relevant to dyslexia module (function 3)

README.md

-# LearnJoy-ML
+# Learn Joy - Function 3
 
-Machine Learning Model for Learnjoy app
+## Table of Contents
+[Function 3 Development](#function-3-development)
+  * [1. About Function 3](#1-About-Function-3)
+  * [2. Function 3 processing & ML Model Development](#3-Function-3-processing-&-ML-Model-Development-folder)
+  * [3. Service Files](#3-Service-Files-folder)
+  * [4. Fast API & app file](#4-Fast-API-&-app-file-folder)
 
-------------------
-## FastAPI Development
+-------------------------------------------------
+## Function 3 Development
 
+### 1.About Function 3
 
-- 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 three main parts:
+- 1. Speech to text : Convert the voice to text children with Dyslexia 
+- 2. Accuracy : The accuracy of the pronunciation should be given as a percentage.
+- 3. Text to speech : Convert the the Missing or wrong pronounsation words to voice.
 
-  ```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:
-
-   ```bash
-   python main.py
-   ```
-
-2. You can access the project output in your browser using the following URL:
-
-   ```bash
-   http://127.0.0.1:8000
-   ```
-3. To explore the API documentation, visit:
-
-   ```bash
-   http://127.0.0.1:8000/docs
-   ```
 
-------------------------
+### 2. Function 1 processing & ML Model Development ()
 
-## Setting Up the Development Environment
+- Inside this folder, you'll come across one Jupyter Notebooks dedicated to data preprocessing and model training:
 
-Follow these steps to set up your development environment for this project:
+  - `lj_function03.ipynb`: This file primarily involves to loard the speech to text & text to spech pre training models and finaly return the accuracy score.
 
-### Create a New `venv`
+### 3. Service Files ()
+- Within this directory, there are one python ".py" files:
 
-1. Navigate to your project directory:
+  - `lj_functiono3.py`: This service file include three main functions(speech to text , scoring , text to speech)
+    - input : sound file and text  `def speech_to_text(audio_file) , def scoring(words,transcriptions) ,def text_to_speech(text,return_tensors="pt")`
+    - Attached models : speech to text ([Link](https://drive.google.com/drive/folders/1jvP4lyhkyLbhtv0fUohLHaeQOnjGsttH?usp=drive_link))
+                       - License : ` Apache License` ([Link](https://github.com/huggingface/transformers/blob/3cefac1d974db5e2825a0cb2b842883a628be7a0/src/transformers/models/wav2vec2/processing_wav2vec2.py))
+                      Text to speech ([Link](https://drive.google.com/drive/folders/1pWtfsLg4IyvPTjKC-a-0-PEIWbZTyYE_?usp=drive_link))
+                       - License : `  MIT License` ([Link](https://github.com/microsoft/SpeechT5?tab=MIT-1-ov-file#readme))
 
-   ```bash
-   cd /path/to/your/project
-   ```
+    - Process : This method is responsible for get the speech and convertto text and compaired with orginal text and return to speech accurasy and missing word speech
+    - Output : sentences and word scoring, missing words and voice of missing words.
 
-2. Create a virtual environment:
+---------------------
 
-   ```bash
-   python -m venv <venv_name>
+### 4. Fast API & app file ()
+- Within this app.py module include one Fast API end points for function 3 :
+  - 1. Request body : orginal word , audio file
+      ```python
+      @app.post("/function3/STT")    
+      async def main_fun03(words:str, audio_file: UploadFile = File(...)):
        ```
       
-### Activate and Deactivate `venv`
+    - Request URL: ` https://Public_or_local_host.app`
+    - Response body: 
+      ```json
+           {
+       "Scoring": {
+         "final_sent_score": 100,
+         "final_word_score": 100,
+         "missing_voice2": []
+       },
+       "audio_file": {
+         "path": "C:\\Users\\KAUSH\\AppData\\Local\\Temp\\tmpefzwhc1x.wav",
+         "status_code": 200,
+         "filename": "speech.wav",
+         "media_type": "audio/wav",
+         "background": null,
+         "raw_headers": [
+           [
+             "content-type",
+             "audio/wav"
+           ],
+           [
+             "content-disposition",
+             "attachment; filename=\"speech.wav\""
+           ]
+         ],
+         "_headers": {
+           "content-type": "audio/wav",
+           "content-disposition": "attachment; filename=\"speech.wav\""
+         },
+         "stat_result": null
+       }
+     }
 
-- In `cmd`:
-
-   ```bash
-   <venv_name>\Scripts\activate
        ```
 
-- In bash:
-
-   ```bash
-   source <venv_name>/Scripts/activate
 
-   # 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`.
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