app.py

import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
from Prediction import *
import os
from datetime import datetime


# 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 = [
    "Ends tonight! Shop select certifiably comfortable shoes!",
    "Just Do it!",
    "Don't miss our products!",
    "What are some of your favorite jokes? Let us know!",
    "Is anyone being creative with their snow day?",
    "Did you see our latest movie?",
    "Hey beautiful people! What would you like to see us doing more (or less) of!",
    "In fact, we discovered that Woollip works better than what we imagined.",
    "It is made of Titanium Grade 5, a material famous for being very strong yet very light.",
    "Each game already comes with six characters.",
    "We thank you personally for the trust you are putting in us and our company.",
    "I wear it everyday and am very happy with it!",
    "We are so grateful for our everyday heroes who never cease to amaze us!"
]

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


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_Online_Demo'><img src='https://img.shields.io/badge/Project_Page-Oliver12315/Brand_Tone_of_Voice_Online_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.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 = gr.Button("Submit", variant="primary")
            button.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

            ...
            """
        )
demo.launch()