app.py

import torch
import gradio as gr
import pandas as pd
from utils import save_dataframe_to_file, tokenize_Df
from model import load_model

with open("./description.md", "r", encoding="utf-8") as file:
    description_text = file.read()

with open("./input_demo.txt", "r", encoding="utf-8") as file:
    demo = file.read()

def process_data(task_name, model_name, pooling_method, input_text=None, file=None):
    output = ""
    dataframe_output = pd.DataFrame()
    file_output = None

    # 情况 1: file 和 input_text 都为 None
    if file is None and (input_text is None or input_text.strip() == ""):
        output = "No valid input detected. Please check your input and ensure it follows the expected format."

    # 情况 2: file 和 input_text 都不为 None
    elif file is not None and input_text is not None:
        output = "Detected both text and file input. Prioritizing file input."
        # 检查文件类型
        if not (file.name.endswith('.csv') or file.name.endswith('.xlsx')):
            output += " File format must be xlsx or csv."
        elif task_name == "Appropriateness" and model_name == "One-phase Fine-tuned BERT":
            output += " One-phase Fine-tuned BERT model does not support Appropriateness task."
        else:
            # 读取文件
            df = pd.read_csv(file) if file.name.endswith('.csv') else pd.read_excel(file)
            # 检查第一行是否为 "prompt" 和 "response"
            if list(df.columns) == ['prompt', 'response']:
                dataframe_output = df
            else:
                df_values = [list(df.columns)] + df.values.tolist()
                dataframe_output = pd.DataFrame(df_values, columns=['prompt', 'response'])

            # model 运行
            loaded_net = load_model(model_name, pooling_method)
            example = tokenize_Df(dataframe_output)
            with torch.no_grad():  
                score = loaded_net(example)

            if model_name == "One-phase Fine-tuned BERT":
                dataframe_output['evaluation'] = score.numpy()
            else: 
                dataframe_output['evaluation'] = score[0].numpy() if task_name=='Creativity' else score[1].numpy()
            file_output = save_dataframe_to_file(dataframe_output, file_format="csv") 
            output += f" Processed {len(dataframe_output)} rows from uploaded file using task: {task_name}, model: {model_name}, pooling: {pooling_method}."

    # 情况 3: 只有 file
    elif file is not None:
        # 检查文件类型
        if not (file.name.endswith('.csv') or file.name.endswith('.xlsx')):
            output = "File format must be xlsx or csv."
        elif task_name == "Appropriateness" and model_name == "One-phase Fine-tuned BERT":
            output = " One-phase Fine-tuned BERT model does not support Appropriateness task."            
        else:
            # 读取文件
            df = pd.read_csv(file) if file.name.endswith('.csv') else pd.read_excel(file)

            # 检查第一行是否为 "prompt" 和 "response"
            if list(df.columns) == ['prompt', 'response']:
                dataframe_output = df
            else:
                df_values = [list(df.columns)] + df.values.tolist()
                dataframe_output = pd.DataFrame(df_values, columns=['prompt', 'response'])
            
            # model 运行
            loaded_net = load_model(model_name, pooling_method)
            example = tokenize_Df(dataframe_output)
            with torch.no_grad():
                score = loaded_net(example)
            
            if model_name == "One-phase Fine-tuned BERT":
                dataframe_output['evaluation'] = score.numpy()
            else: 
                dataframe_output['evaluation'] = score[0].numpy() if task_name=='Creativity' else score[1].numpy()
            file_output = save_dataframe_to_file(dataframe_output, file_format="csv") 
            output = f"Processed {len(dataframe_output)} rows from uploaded file using task: {task_name}, model: {model_name}, pooling: {pooling_method}."

    # 情况 4: 只有 input_text
    elif input_text is not None:
        if task_name == "Appropriateness" and model_name == "One-phase Fine-tuned BERT":
            output = "One-phase Fine-tuned BERT model does not support Appropriateness task."
        else:
            lines = input_text.strip().split("\n")
            rows = []
            for line in lines:
                try:
                    split_line = line.split(",", maxsplit=1)
                    if len(split_line) == 2:
                        rows.append(split_line)
                except Exception as e:
                    output = f"Error processing line: {line}"
                    break
            
            if output == "":
                dataframe_output = pd.DataFrame(rows[1:], columns=['prompt', 'response']) if rows[0] == ['prompt', 'response'] else pd.DataFrame(rows, columns=['prompt', 'response'])
                
                # model 运行
                loaded_net = load_model(model_name, pooling_method)
                example = tokenize_Df(dataframe_output)
                with torch.no_grad(): 
                    score = loaded_net(example)

                if model_name == "One-phase Fine-tuned BERT":
                    dataframe_output['evaluation'] = score.numpy()
                else: 
                    dataframe_output['evaluation'] = score[0].numpy() if task_name=='Creativity' else score[1].numpy()
                file_output = save_dataframe_to_file(dataframe_output, file_format="csv") 
                output = f"Processed {len(dataframe_output)} rows of text using task: {task_name}, model: {model_name}, pooling: {pooling_method}."

    return output, dataframe_output, file_output

## 输入组件
task_dropdown = gr.Dropdown(
                label="Task Name", 
                choices=["Creativity", "Appropriateness"], 
                value="Appropriateness")

model_dropdown = gr.Dropdown(
                label="Model Name", 
                choices=[
                    "One-phase Fine-tuned BERT",
                    "Two-phase Fine-tuned BERT"], 
                value="Two-phase Fine-tuned BERT")

pooling_dropdown = gr.Dropdown(
                label="Pooling", 
                choices=["mean", "cls"], 
                value="cls")

text_input = gr.Textbox(
                label="Text Input", 
                lines=10, 
                value=demo)

file_input = gr.File(
                label="Input File", 
                type="filepath", 
                file_types=[".csv", ".xlsx"])

## 输出组件
output_box = gr.Textbox(label="Output", lines=5, interactive=False)

dataframe_output = gr.Dataframe(label="DataFrame", interactive=False)

file_output = gr.File(label="Output File", interactive=False)

# 构建Gradio界面
interface = gr.Interface(
    fn=process_data,
    inputs=[task_dropdown, model_dropdown, pooling_dropdown, text_input, file_input],
    outputs=[output_box, dataframe_output, file_output],
    css=(""".file-download {display: none !important;}
            h1 {text-align: center;}"""),
    title="TwoPhaseLLMs-CreativityAutoEvaluation",
    description=description_text,
    theme=gr.themes.Soft(),
)

# 启动界面
interface.launch()