legaltextclassification.py

# -*- coding: utf-8 -*-
"""LegalTextClassification.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1x6EcLSN3qEgm6sVIcmX0bYeXj7AdDQlW
!pip install gradio
    import gradio as gr

def greet(name):
    return "Hello " + name + "!!"

demo = gr.Interface(fn=greet, inputs="text", outputs="text")
demo.launch()
    
#About Data
The dataset contains a total of 25000 legal cases in the form of text documents. Each document has been annotated with catchphrases, citations sentences, citation catchphrases, and citation classes. Citation classes indicate the type of treatment given to the cases cited by the present case.

The Legal Citation Text Classification dataset is provided in CSV format. The dataset has ***four columns***, ***namely Case ID, Case Outcome, Case Title, and Case Text***. The Case ID column contains a unique identifier for each legal case, the Case Outcome column indicates the outcome of the case, the Case Title column contains the title of the legal case, and the Case Text column contains the text of the legal case.

Kaggle Dataset Link: https://www.kaggle.com/datasets/amohankumar/legal-text-classification-dataset/data

#Importing Data
"""

from google.colab import files
import pandas as pd

df = pd.read_csv('legal_text_classification.csv')
df.head()

"""#Data Preprocessing and Description"""

print(df.columns)      # Lists all column names
print(len(df.columns)) # Shows the number of columns

print(df.shape)  # Output: (rows, columns)

print(df.isnull().sum())

df = df.dropna(subset=['case_text'])

df = df.drop(columns=["case_id", "case_title"])

print(df.isnull().sum())

import re

def text_ready(text):
    text = text.lower()  #lowercase
    text = re.sub(r'[^\w\s]', '', text)  #special char
    text = re.sub(r'\s+', ' ', text).strip()  #whitespace
    return text

df["text_ready"] = df["case_text"].apply(text_ready)

import matplotlib.pyplot as plt

text_data = df['text_ready']
word_count = [len(text.split()) for text in text_data]

plt.hist(word_count, bins=50, color='skyblue', edgecolor='black')
plt.title('Distribution of Word Counts in text_ready')
plt.xlabel('Word Count')
plt.ylabel('Frequency')
plt.show()

print(df.shape)  # Output: (rows, columns)

df.describe()

df['text']=df['text_ready']
df['label']=df['case_outcome']
data=df[['text','label']]

df = df.drop(columns=["case_outcome", "case_text"])

df.head()

df = df.drop(columns=["text_ready"])

df.head()

data['label'].value_counts()

class_label=sorted(data['label'].unique())
lbl2id={label:id for id,label in enumerate(class_label)}
id2lb={id:label for label,id in lbl2id.items()}
print(lbl2id)
print(id2lb)



data.head()

data['label']=data['label'].map(lbl2id)
data.head()

data.label.value_counts()

import matplotlib.pyplot as plt

df['label'].value_counts().plot.bar()
plt.show()

from transformers import AutoModelForSequenceClassification,AutoTokenizer
model_name='nlpaueb/legal-bert-base-uncased'
tokenizer=AutoTokenizer.from_pretrained(model_name)

from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained(
    model_name,
    num_labels=len(id2lb),
    id2label=id2lb,
    label2id=lbl2id
)

!pip install datasets
from datasets import Dataset
ds=Dataset.from_pandas(data)
ds

ds['label'][:11]

from datasets import ClassLabel
unique_labels = sorted(set(ds['label']))
print(f"Unique labels in Y: {unique_labels}")

new_features = ds.features.copy()
new_features['label'] = ClassLabel(names=unique_labels)

ds = ds.cast(new_features)
data = ds.train_test_split(test_size=0.2, shuffle=True, seed=42)
data

split_ds = data['test'].remove_columns('__index_level_0__').train_test_split(test_size=0.5, shuffle=True, seed=42)
split_ds

train_data=data['train']
test_data=split_ds['train']
val_data=split_ds['test']

train_data[0]

def tokenize_fun(data):
  return tokenizer(data['text'],padding=True,truncation=True,return_tensors='pt')

tokenized_train_data=train_data.map(tokenize_fun,batched=True)

tokenized_train_data.features

!pip install evaluate
import evaluate
accuracy=evaluate.load('accuracy')

import numpy as np

def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    predictions = np.argmax(predictions, axis=1)
    return accuracy.compute(predictions=predictions, references=labels)

tokenized_test_data=test_data.map(tokenize_fun,batched=True)
tokenized_val_data=val_data.map(tokenize_fun,batched=True)

from huggingface_hub import login
login()

from transformers import Trainer,TrainingArguments

training_args=TrainingArguments(
    output_dir='./quest_model',
    learning_rate=2e-3,
    per_device_eval_batch_size=16,
    per_device_train_batch_size=16,
    num_train_epochs=2,
    weight_decay=0.01,
    eval_strategy='epoch',
    save_strategy='epoch',
    load_best_model_at_end=True,
    push_to_hub=True
)

trainer=Trainer(
    model=model,
    tokenizer=tokenizer,
    args=training_args,
    train_dataset=tokenized_train_data,
    eval_dataset=tokenized_val_data,
    compute_metrics=compute_metrics
)
trainer.train()

model.config.id2label

import os
os.environ['CUDA_LAUNCH_BLOCKING'] = '1'

model.save_pretrained('./quest_model')
tokenizer.save_pretrained("./quest_model")

tokenized_train_data[0]['text']

from transformers import pipeline
pipe=pipeline('text-classification',model='Nainglinthu/quest_model')
output=pipe('Hexal Australia Pty Ltd v Roche Therapeutics Inc (2005) 66 IPR 325, the likelihood of irreparable harm was regarded by Stone J as, indeed, a separate element that had to be established by an applicant for an interlocutory injunction.')
output

!pip install --upgrade gradio
import gradio as gr
from transformers import pipeline

# Initialize the pipeline
pipe = pipeline('text-classification', model='Nainglinthu/quest_model')

# Function to classify text
def classify_text(input_text):
    output = pipe(input_text)
    return output

# Create Gradio interface
interface = gr.Interface(
    fn=classify_text,          # Function to call
    inputs="text",             # Input type (text box)
    outputs="json",            # Output type (JSON for displaying result)
    title="Legal Text Classifier",  # Title of the Gradio app
    description="Classify legal text using the Nainglinthu/quest_model!",  # Description
)

# Launch the Gradio app
interface.launch()