RFQ Splitter Dummy - `Vue + VITE`

To run the programme go to Server Folder First and type
```
cd Server
```
Then Install all dependencies by typing
```
pip install -r requirements.txt
```
Then Run the flask server by
```
python app.py
```
Then return to the root directory
```
cd ..
```
Then go to the Client folder
```
cd Client
```
Then install all the dependencies by typing
```
npm i
```
Then start the Vite Server by
```
npm run dev
```
Then Check the website at your https://localhost:5173
To Check the API go to https://localhost:3000

Details

This is the Frontend and a supporting dummy backend for a project of RFQ Splitter
To Update the backend just modify the GPTmodel.py file inside the server folder

Technologies Used

Frontend

Created with VUE JS + VITE

choose a file to upload
upload the file using the upload button
then generate response using the generate button
then download the generated responses seperately using download button under each generated section
clear everything using the clear button

Backend

Machine Learning Model

I am not making the model but if it is made it can be used in the GPTmodel.py file by modifying it

Flask API

The API is built on FLASK and has 4 endpoints

/upload

@app.route("/upload", methods=['POST'])
def upload_file():
    if request.method == 'POST':
        file = request.files['file']
        if file.filename != '':
            # Save the file to a temporary location
            upload_folder = 'path'
            os.makedirs(upload_folder, exist_ok=True)
            file_path = os.path.join(upload_folder, file.filename)
            file.save(file_path)
            path[0]=file_path
            initialize()
            return "File uploaded successfully"
        else:
            return 'No file uploaded'

This end point basically supports the upload feature in the frontend by storing the file uploaded in a path folder which if doesn't exist it creates automatically
To add any functionality while uploading the file for easier generation add that to the initialize() function in the GPTmodel.py

/response
```
@app.route("/response", methods=['GET'])
def get_response():
    return giveResults(path[0])
```
- This generates the Response that needs to be shown in the frontend as well as creates .txt files which can be downloaded by calling the download endpoint
- To modify anything in this response, modify the getResults() function in the GPTmodel.py

/download/<filetype>

@app.route("/download/<filetype>", methods=['GET'])
def download_file(filetype):
    if os.path.exists("path\\"+filetype+"_results.txt"):
        return send_file("path\\"+filetype+"_results.txt", mimetype='*/*', as_attachment=True)
    else:
        return 'No Such File Exists'

This Returns the generated files for download

/cleardata

@app.route("/cleardata", methods=['GET'])
def clear_data():
    shutil.rmtree('path')
    return "Data Cleared"

This endpoint deletes the path folder after all operations has been performed

halcyon-past/Vue-Project-With-Dummy-Backend