app.py

import gradio as gr
import pandas as pd
from Prediction import *
import os
from datetime import datetime
import re
import json
import hashlib

persistent_path = "/output"
# os.environ['HF_HOME'] = os.path.join(persistent_path, ".huggingface")
user_input_path = os.path.join(persistent_path, 'user.jsonl')
secret = "2fc9ff032e027e8f23bb9fb693234899"

def get_md5(s):
    md = hashlib.md5()
    md.update(s.encode('utf-8'))
    return md.hexdigest()

examples = []
if os.path.exists("assets/examples.txt"):
    with open("assets/examples.txt", "r", encoding="utf8") as file:
        for sentence in file:
            sentence = sentence.strip()
            examples.append(sentence)
else:
    examples = [
        "Games of the imagination teach us actions have consequences in a realm that can be reset.",
        "But New Jersey farmers are retiring and all over the state, development continues to push out dwindling farmland.",
        "He also is the Head Designer of The Design Trust so-to-speak, besides his regular job ..."
        ]

device = torch.device('cpu')
tokenizer = BertTokenizer.from_pretrained("Oliver12315/Brand_Tone_of_Voice")
model = BertForSequenceClassification.from_pretrained("Oliver12315/Brand_Tone_of_Voice")
model = model.to(device)


def single_sentence(sentence):
    predictions = predict_single(sentence, tokenizer, model, device)
    return sorted(zip(LABEL_COLUMNS, predictions), key=lambda x:x[-1], reverse=True)

def csv_process(csv_file, attr="content"):
    current_time = datetime.now()
    formatted_time = current_time.strftime("%Y_%m_%d_%H_%M_%S")
    data = pd.read_csv(csv_file.name)
    data = data.reset_index()
    os.makedirs('output', exist_ok=True)
    outputs = []
    predictions = predict_csv(data, attr, tokenizer, model, device)
    output_path = f"output/prediction_Brand_Tone_of_Voice_{formatted_time}.csv"
    predictions.to_csv(output_path)
    outputs.append(output_path)
    return outputs

def logfile_query(auth):
    if get_md5(auth) == secret and os.path.exists(user_input_path):
        return [user_input_path]
    else:
        return None


def check_save(fname, lname, cnum, email, oname, position):
    errors = []
    valid_vars = {}

    if not fname.strip() or not lname.strip():
        errors.append("Name cannot be empty")
    elif fname.isdigit() or lname.isdigit():
        errors.append("Name cannot be purely numerical")
    else:
        valid_vars["fname"] = fname
        valid_vars["lname"] = lname

    valid_vars["cnum"] = ''
    if cnum:
        if not cnum.isdigit():
            errors.append("The phone number must be a pure number")
        else:
            valid_vars["cnum"] = cnum

    if not email.strip():
        errors.append("Email cannot be empty")
    elif not re.match(r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$', email):
        errors.append("Incorrect email format")
    else:
        valid_vars["email"] = email

    if not oname.strip():
        errors.append("Organization name cannot be empty")
    elif oname.isdigit():
        errors.append("Organization cannot be purely numerical")
    else:
        valid_vars["oname"] = oname

    valid_vars["position"] = ''
    if position:
        if position.isdigit():
            errors.append("Position in your company cannot be purely numerical")
        else:
            valid_vars["position"] = position

    if errors:
        return errors
    
    current_time = datetime.now()
    formatted_time = current_time.strftime("%Y_%m_%d_%H_%M_%S")
    valid_vars['time'] = formatted_time

    with open(user_input_path, 'a+', encoding="utf8") as file:
        file.write(json.dumps(valid_vars)+"\n")

    records = {}
    with open(user_input_path, 'r', encoding="utf8") as file:
        for line in file:
            line = line.strip()
            dct = json.loads(line)
            records[dct['time']] = dct

    return records


my_theme = gr.Theme.from_hub("JohnSmith9982/small_and_pretty")
with gr.Blocks(theme=my_theme, title='Brand_Tone_of_Voice_demo') as demo:
    gr.HTML(
        """
        <div style="display: flex; justify-content: center; align-items: center; text-align: center;">
        <a href="https://github.com/xxx" style="margin-right: 20px; text-decoration: none; display: flex; align-items: center;">
        </a>
        <div>
            <h1 >Place the title of the paper here</h1>
            <h5 style="margin: 0;">If you like our project, please give us a star ✨ on Github for the latest update.</h5>
            <div style="display: flex; justify-content: center; align-items: center; text-align: center;>
                <a href="https://arxiv.org/abs/xx.xx"><img src="https://img.shields.io/badge/Arxiv-xx.xx-red"></a>
                <a href='https://huggingface.co/spaces/Oliver12315/Brand_Tone_of_Voice_demo'><img src='https://img.shields.io/badge/Project_Page-Oliver12315/Brand_Tone_of_Voice_demo' alt='Project Page'></a>
                <a href='https://github.com'><img src='https://img.shields.io/badge/Github-Code-blue'></a>
            </div>
        </div>
        </div>
        """)

    with gr.Column(visible=True) as regis:
        gr.Markdown("# Welcome to BTV!  Please fill out the form below to continue.\nI’m assuming that you mention somewhere that this project/research is conducted by the University of Manchester/AMBS. By ticking this box, I consent to be approached by the research team of the University of Manchester.")
        with gr.Column(variant='panel'):   
            fname_tb = gr.Textbox(label="First Name: ", type='text')
            lname_tb = gr.Textbox(label="Last Name: ", type='text')
            email_tb = gr.Textbox(label="Email: ", type='email')
            cnum_tb = gr.Textbox(label="Contact: (Optional)", type='text')
            oname_tb = gr.Textbox(label="Organization name: ", type='text')
            position_tb = gr.Textbox(label="Positions in your company: (Optional)", type='text')
        error_box = gr.HTML(value="", visible=False)
        submit_btn = gr.Button("Click here to start if you have fullfill all the item!")

    with gr.Row(visible=False) as mainrow:

        with gr.Tab("Single Sentence"):
            with gr.Row():
                tbox_input = gr.Textbox(label="Input",
                                        info="Please input a sentence here:")
                gr.Markdown("""
                    # Detailed information about our model:
                    ...
                    """)
            tab_output = gr.DataFrame(label='Predictions:', 
                                    headers=["Label", "Probability"],
                                    datatype=["str", "number"],
                                    interactive=False)
            with gr.Row():
                button_ss = gr.Button("Submit", variant="primary")
                button_ss.click(fn=single_sentence, inputs=[tbox_input], outputs=[tab_output])
                gr.ClearButton([tbox_input, tab_output])

            gr.Examples(
                examples=examples,
                inputs=tbox_input,
                examples_per_page=len(examples)
            )

        with gr.Tab("Csv File"):
            with gr.Row():
                csv_input = gr.File(label="CSV File:",
                                    file_types=['.csv'],
                                    file_count="single"
                                    )
                csv_output = gr.File(label="Predictions:")

            with gr.Row():
                button_cf = gr.Button("Submit", variant="primary")
                button_cf.click(fn=csv_process, inputs=[csv_input], outputs=[csv_output])
                gr.ClearButton([csv_input, csv_output])

            gr.Markdown("## Examples \n The incoming CSV must include the ``content`` field, which represents the text that needs to be predicted!")
            gr.DataFrame(label='Csv input format:',
                        value=[[i, examples[i]] for i in range(len(examples))],
                        headers=["index", "content"],
                        datatype=["number","str"],
                        interactive=False
                        )

        with gr.Tab("Readme"):
            gr.Markdown(
                """
                # Paper Name

                # Authors

                + First author
                + Corresponding author
                
                # Detailed Information

                ...
                """
            )

        with gr.Tab("Log File"):
            with gr.Row():
                auth_token = gr.Textbox(label="Authentication Tokens: ", info="Enter the key to download persistent stored log information.")
                log_output = gr.File(label="Log file: ")

            with gr.Row():
                button_lf = gr.Button("Validate", variant="primary")
                button_lf.click(fn=logfile_query, inputs=[auth_token], outputs=[log_output])
                gr.ClearButton([auth_token, log_output])


    def submit(*user_input):
        res = check_save(*user_input)
        if isinstance(res, list):
            return {
                error_box: gr.HTML(
                    value=f"""
                    <div style="display: flex; justify-content: center; align-items: center; text-align: center;">
                    <div>
                        <p style="color:red;">{"; ".join(res)}</p>
                    </div>
                    </div>
                    """, 
                    visible=True)
            }
        else:
            return {
                mainrow: gr.Row(visible=True),
                regis: gr.Row(visible=False),
                error_box: gr.HTML(visible=False)
            }

    submit_btn.click(
        submit,
        [fname_tb, lname_tb, cnum_tb, email_tb, oname_tb, position_tb],
        [mainrow, regis, error_box],
    )
demo.launch()