app.py

from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments
from datasets import load_dataset
import csv

# Read requirements.txt file
with open('requirements.txt', 'r') as req_file:
    requirements = req_file.read().splitlines()

# Load and preprocess the IMDB dataset from CSV
preprocessed_data = []
with open('IMDB Dataset.csv', 'r') as csv_file:
    csv_reader = csv.DictReader(csv_file)
    for row in csv_reader:
        text = row['review']
        label = row['sentiment']
        preprocessed_entry = {
            'text': text,
            'label': label
        }
        preprocessed_data.append(preprocessed_entry)

# Convert the preprocessed data to a dataset
dataset = load_dataset('csv', data=preprocessed_data, delimiter=',')

# Tokenize the dataset
tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-560m")
def tokenize_function(examples):
    return tokenizer(examples["text"], padding="max_length", truncation=True)

tokenized_datasets = dataset.map(tokenize_function, batched=True)

# Fine-tune the Bloom model
model = AutoModelForSequenceClassification.from_pretrained("bigscience/bloom-560m", num_labels=2)

training_args = TrainingArguments(output_dir="test_trainer")

import numpy as np
import evaluate

metric = evaluate.load("accuracy")