README.md

README.md

@@ -1,6 +1,3 @@
-Sure! Here is the updated `README.md` file incorporating the existing content and modifications needed for the given Flask application:
-
-```markdown
 # Sentimaster
 
 Welcome to **Sentimaster**, your go-to sentiment analysis tool. Sentimaster leverages powerful machine learning algorithms to analyze and interpret the sentiment behind textual data, helping you make informed decisions based on customer feedback, social media comments, and more.
@@ -14,70 +11,61 @@ Welcome to **Sentimaster**, your go-to sentiment analysis tool. Sentimaster leve
 - **Secure and Reliable**: Built on robust machine learning models ensuring accurate predictions.
 
 ## Directory Structure
-
-```
 │
-├───Dataset
-├───Model
-│   ├── SVC.joblib
-│   └── CountVectorizer.joblib
-├───static
-│   └── images
-│       └── banner.jpg
-└───templates
-    ├── index.html
-    └── result.html
+├───Dataset<br>
+├───Model<br>
+│ ├── SVC.joblib<br>
+│ └── CountVectorizer.joblib<br>
+├───static<br>
+│ └── images<br>
+│     └── banner.jpg<br>
+└───templates<br>
+│ ├── index.html
+│ └── result.html
 ├── app.py
 ├── model.ipynb
 ├── model.py
 └── requirements.txt
-```
-
 ## Installation
+1. **Clone the repository**:
+    ```sh
+    git clone https://github.com/GoldSharon/Sentimaster.git
+    cd SpamShield
+    ```
 
-To get started with Sentimaster, clone the repository and install the required dependencies:
-
-```bash
-git clone https://github.com/GoldSharon/Sentimaster.git
-cd "D:/Experiment/A-Z ML/Nlp/Restrant review"
-pip install -r requirements.txt
-```
-
-## Running the Application
-
-1. Ensure you have the required model files (`SVC.joblib` and `CountVectorizer.joblib`) in the `Model` directory:
-
+2. **Set up a virtual environment**:
+    ```sh
+    python -m venv venv
+    source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
     ```
-    D:/Experiment/A-Z ML/Nlp/Restrant review/Model/
+
+3. **Install dependencies**:
+    ```sh
+    pip install -r requirements.txt
     ```
 
-2. Run the Flask application:
+4. **Download the pre-trained model and vectorizer**:
+    - Place `SVC.joblib` and `Vectorizer.joblib` in the `models/` directory.
 
-    ```bash
+## Usage
+1. **Run the application**:
+    ```sh
     python app.py
     ```
 
-3. Open your web browser and navigate to:
-
-    ```
-    http://127.0.0.1:5000/
-    ```
+2. **Access the web interface**:
+    Open your web browser and navigate to `http://127.0.0.1:5000/`.
 
 ## Usage
+Home Page: Enter a restaurant review in the text box and click the submit button.
 
-1. **Home Page:** Enter a restaurant review in the text box and click the submit button.
-
-2. **Result Page:** Displays whether the review is positive or negative based on the model's prediction.
+Result Page: Displays whether the review is positive or negative based on the model's prediction.
 
 ## License
+This project is licensed under the MIT License. See the LICENSE file for more details.
 
-This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
 
-## Acknowledgements
+This updated `README.md` file includes the necessary instructions for setting up and running the Flask application, reflecting the given directory structure and the specific requirements of the project. Adjust paths, repository URLs, and other details as needed to match your specific setup and requirements.
 
-- [Flask](https://flask.palletsprojects.com/)
-- [scikit-learn](https://scikit-learn.org/)
-- [joblib](https://joblib.readthedocs.io/)
-```
 
-This updated `README.md` file includes the necessary instructions for setting up and running the Flask application, reflecting the given directory structure and the specific requirements of the project. Adjust paths, repository URLs, and other details as needed to match your specific setup and requirements.
+