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import csv | |
import gradio as gr | |
import pandas as pd | |
from sentiment_analyser import RandomAnalyser, RoBERTaAnalyser, ChatGPTAnalyser | |
import matplotlib.pyplot as plt | |
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix | |
def plot_bar(value_counts): | |
fig, ax = plt.subplots(figsize=(6, 6)) | |
value_counts.plot.barh(ax=ax) | |
ax.bar_label(ax.containers[0]) | |
plt.title('Frequency of Predictions') | |
return fig | |
def plot_confusion_matrix(y_pred, y_true): | |
cm = confusion_matrix(y_true, y_pred, normalize='true') | |
fig, ax = plt.subplots(figsize=(6, 6)) | |
labels = [] | |
for label in SENTI_MAPPING.keys(): | |
if (label in y_pred.values) or (label in y_true.values): | |
labels.append(label) | |
disp = ConfusionMatrixDisplay(confusion_matrix=cm, | |
display_labels=labels) | |
disp.plot(cmap="Blues", values_format=".2f", ax=ax, colorbar=False) | |
plt.title("Normalized Confusion Matrix") | |
return fig | |
def classify(num: int): | |
samples_df = df.sample(num) | |
X = samples_df['Text'].tolist() | |
y = samples_df['Label'] | |
roberta = MODEL_MAPPING[OUR_MODEL] | |
y_pred = pd.Series(roberta.predict(X), index=samples_df.index) | |
samples_df['Predict'] = y_pred | |
bar = plot_bar(y_pred.value_counts()) | |
cm = plot_confusion_matrix(y_pred, y) | |
plt.close() | |
return samples_df, bar, cm | |
def analysis(Text): | |
keys = [] | |
values = [] | |
for name, model in MODEL_MAPPING.items(): | |
keys.append(name) | |
values.append(SENTI_MAPPING[model.predict([Text])[0]]) | |
return pd.DataFrame([values], columns=keys) | |
def analyse_file(file): | |
output_name = 'output.csv' | |
with open(output_name, mode='w', newline='') as output: | |
writer = csv.writer(output) | |
header = ['Text', 'Label'] | |
writer.writerow(header) | |
model = MODEL_MAPPING[OUR_MODEL] | |
with open(file.name) as f: | |
for line in f: | |
text = line[:-1] | |
sentiment = model.predict([text]) | |
writer.writerow([text, sentiment[0]]) | |
return output_name | |
MODEL_MAPPING = { | |
'Random': RandomAnalyser(), | |
'RoBERTa': RoBERTaAnalyser(), | |
'ChatGPT': RandomAnalyser(), | |
} | |
OUR_MODEL = 'RoBERTa' | |
SENTI_MAPPING = { | |
'negative': '😭', | |
'neutral': '😶', | |
'positive': '🥰' | |
} | |
TITLE = "Sentiment Analysis on Software Engineer Texts" | |
DESCRIPTION = ( | |
"这里是第16组“睿王和他的五个小跟班”软工三迭代三模型演示页面。" | |
"模型链接:[Cloudy1225/stackoverflow-roberta-base-sentiment]" | |
"(https://huggingface.co/Cloudy1225/stackoverflow-roberta-base-sentiment) " | |
) | |
MAX_SAMPLES = 64 | |
df = pd.read_csv('./SOF4423.csv') | |
with gr.Blocks(title=TITLE) as demo: | |
gr.HTML(f"<H1>{TITLE}</H1>") | |
gr.Markdown(DESCRIPTION) | |
gr.HTML("<H2>Model Inference</H2>") | |
gr.Markdown(( | |
"在左侧文本框中输入文本并按回车键,右侧将输出情感分析结果。" | |
"这里我们展示了三种结果,分别是随机结果、模型结果和 ChatGPT 结果。" | |
)) | |
with gr.Row(): | |
with gr.Column(): | |
text_input = gr.Textbox(label='Input', | |
placeholder="Enter a positive or negative sentence here...") | |
with gr.Column(): | |
senti_output = gr.Dataframe(type="pandas", value=[['😋', '😋', '😋']], | |
headers=list(MODEL_MAPPING.keys()), interactive=False) | |
text_input.submit(analysis, inputs=text_input, outputs=senti_output, show_progress=True) | |
gr.Markdown(( | |
"在左侧文件框中上传 txt/csv 文件,模型会对输入文本的每一行当作一个文本进行情感分析。" | |
"可以在右侧下载输出文件,输出文件为两列 csv 格式,第一列为原始文本,第二列为分类结果。" | |
)) | |
with gr.Row(): | |
with gr.Column(): | |
file_input = gr.File(label='File', | |
file_types=['.txt', '.csv']) | |
with gr.Column(): | |
file_output = gr.File(label='Output') | |
file_input.upload(analyse_file, inputs=file_input, outputs=file_output) | |
gr.HTML("<H2>Model Evaluation</H2>") | |
gr.Markdown(( | |
"这里是在 StackOverflow4423 数据集上评估我们的模型。" | |
"滑动 Slider,将会从 StackOverflow4423 数据集中抽样出指定数量的样本,预测其情感标签。" | |
"并根据预测结果绘制标签分布图和混淆矩阵。" | |
)) | |
input_models = list(MODEL_MAPPING) | |
input_n_samples = gr.Slider( | |
minimum=4, | |
maximum=MAX_SAMPLES, | |
value=8, | |
step=4, | |
label='Number of samples' | |
) | |
with gr.Row(): | |
with gr.Column(): | |
bar_plot = gr.Plot(label='Predictions Frequency') | |
with gr.Column(): | |
cm_plot = gr.Plot(label='Confusion Matrix') | |
with gr.Row(): | |
dataframe = gr.Dataframe(type="pandas", wrap=True, headers=['Text', 'Label', 'Predict']) | |
input_n_samples.change(fn=classify, inputs=input_n_samples, outputs=[dataframe, bar_plot, cm_plot]) | |
demo.launch() | |