Please correct and enhance the following:       # Install necessary libraries
!pip install transformers datasets pandas numpy nltk torch scikit-learn ipywidgets evaluate --quiet
!pip install sacremoses sentencepiece --quiet  # Required for some tokenizers

# Import libraries
import pandas as pd
import numpy as np
import os
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, TrainingArguments, Trainer
from datasets import Dataset, DatasetDict, concatenate_datasets, load_dataset
from sklearn.model_selection import train_test_split
import nltk
nltk.download('punkt')
from nltk.tokenize import sent_tokenize
from tqdm.notebook import tqdm
import warnings
warnings.filterwarnings('ignore')
import ipywidgets as widgets
from IPython.display import display, clear_output
import evaluate

# Check if GPU is available
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f'Using device: {device}')

# Function to load datasets
def load_datasets():
    datasets = {}

    # MedNLI Dataset
    mednli = load_dataset('mednli')
    datasets['mednli'] = mednli['train']

    # Disease Symptoms Dataset
    symptoms_url = 'https://raw.githubusercontent.com/iamAnki/Medicine-Chatbot/master/dataset.csv'
    symptoms_data = pd.read_csv(symptoms_url)
    datasets['symptoms'] = Dataset.from_pandas(symptoms_data)

    # PubMedQA Dataset
    pubmed_qa = load_dataset('pubmed_qa', 'pqa_labeled')
    datasets['pubmed_qa'] = pubmed_qa['train']

    # MedQA Dataset (USMLE)
    medqa_us = load_dataset('medqa_usmle')
    datasets['medqa_usmle'] = medqa_us['train']

    # MIMIC-CXR Dataset (Chest X-ray Reports)
    mimic_cxr = load_dataset('mimic_cxr', split='train')
    datasets['mimic_cxr'] = mimic_cxr

    # BioASQ Dataset (Question Answering)
    bioasq = load_dataset('bioasq', 'bioasq_factoid')
    datasets['bioasq'] = bioasq['train']

    # CheXpert Dataset (Chest X-ray Classification)
    chexpert = load_dataset('chexpert', split='train')
    datasets['chexpert'] = chexpert

    # ClinicalBERT Dataset (Clinical Text Classification)
    clinicalbert = load_dataset('clinicalbert', split='train')
    datasets['clinicalbert'] = clinicalbert

    # MultiMedQA Dataset (Multimodal Question Answering)
    multimedqa = load_dataset('multimedqa', split='train')
    datasets['multimedqa'] = multimedqa

    # COVID-19 Open Research Dataset Challenge (CORD-19)
    cord19 = load_dataset('cord19', split='train')
    datasets['cord19'] = cord19

    # Additional datasets can be added here
    # ...

    return datasets

# Load datasets
datasets = load_datasets()

# Preprocess datasets for chain-of-thought reasoning
def preprocess_for_cot(datasets):
    cot_data = []

    # Preprocess Disease Symptoms dataset for CoT
    if 'symptoms' in datasets:
        df = datasets['symptoms'].to_pandas().dropna()
        for _, row in df.iterrows():
            symptoms = ', '.join([str(row[f'Symptom_{i}']) for i in range(1, 18) if pd.notnull(row.get(f'Symptom_{i}', None))])
            diagnosis = row['Disease']
            question = f"Patient presents with symptoms: {symptoms}. What is the most probable diagnosis?"
            reasoning = f"The symptoms presented are {symptoms}, which are commonly associated with {diagnosis}."
            answer = f"{reasoning} Therefore, the most probable diagnosis is {diagnosis}."
            cot_data.append({'question': question, 'answer': answer})

    # Preprocess PubMedQA dataset for CoT
    if 'pubmed_qa' in datasets:
        df = datasets['pubmed_qa'].to_pandas()
        for _, row in df.iterrows():
            question_text = row['question']
            context = row['context']
            long_answer = row['final_answer']
            reasoning = f"From the context: {context}"
            answer = f"{reasoning} Therefore, the answer is: {long_answer}"
            cot_data.append({'question': question_text, 'answer': answer})

    # Preprocess MedNLI dataset for CoT
    if 'mednli' in datasets:
        df = datasets['mednli'].to_pandas()
        for _, row in df.iterrows():
            premise = row['premise']
            hypothesis = row['hypothesis']
            label = row['label']
            question = f"Given the medical note: '{premise}' Can we infer that: '{hypothesis}'?"
            reasoning = f"Based on the note, '{premise}', it is {label} that '{hypothesis}'."
            answer = f"{reasoning} Therefore, the answer is {label}."
            cot_data.append({'question': question, 'answer': answer})

    # Preprocess MedQA USMLE dataset for CoT
    if 'medqa_usmle' in datasets:
        df = datasets['medqa_usmle'].to_pandas()
        for _, row in df.iterrows():
            question_text = row['question']
            answer_text = row['answer']
            options = row['options']
            explanation = row.get('explanation', '')
            reasoning = f"Question: {question_text}\nOptions: {options}\nBased on medical knowledge, the correct answer is {answer_text}."
            if explanation:
                reasoning += f" Explanation: {explanation}"
            answer = f"{reasoning}"
            cot_data.append({'question': question, 'answer': answer})

    # Preprocess MIMIC-CXR dataset for CoT
    if 'mimic_cxr' in datasets:
        df = datasets['mimic_cxr'].to_pandas()
        for _, row in df.iterrows():
            report = row['report']
            findings = row['findings']
            impression = row['impression']
            question = f"Based on this chest X-ray report: {report}, what are the findings and impression?"
            reasoning = f"The report states: {findings} and the impression is: {impression}"
            answer = f"{reasoning} Therefore, the findings are {findings} and the impression is {impression}."
            cot_data.append({'question': question, 'answer': answer})

    # Preprocess BioASQ dataset for CoT
    if 'bioasq' in datasets:
        df = datasets['bioasq'].to_pandas()
        for _, row in df.iterrows():
            question_text = row['question']
            ideal_answer = row['ideal_answer']
            question = f"Answer the following question about biomedical research: {question_text}"
            reasoning = f"Based on scientific literature, the answer to the question is: {ideal_answer}"
            answer = f"{reasoning} Therefore, the answer is {ideal_answer}."
            cot_data.append({'question': question, 'answer': answer})

    # Preprocess CheXpert dataset for CoT
    if 'chexpert' in datasets:
        df = datasets['chexpert'].to_pandas()
        for _, row in df.iterrows():
            labels = row['labels']
            image_path = row['path']
            question = f"What are the findings in this chest X-ray image: {image_path}?"
            reasoning = f"The image shows the following findings: {labels}"
            answer = f"{reasoning} Therefore, the findings are {labels}."
            cot_data.append({'question': question, 'answer': answer})

    # Preprocess ClinicalBERT dataset for CoT
    if 'clinicalbert' in datasets:
        df = datasets['clinicalbert'].to_pandas()
        for _, row in df.iterrows():
            text = row['text']
            label = row['label']
            question = f"What is the clinical classification of the following text: {text}?"
            reasoning = f"Based on the text, the clinical classification is {label}"
            answer = f"{reasoning} Therefore, the classification is {label}."
            cot_data.append({'question': question, 'answer': answer})

    # Preprocess MultiMedQA dataset for CoT
    if 'multimedqa' in datasets:
        df = datasets['multimedqa'].to_pandas()
        for _, row in df.iterrows():
            question_text = row['question']
            answer_text = row['answer']
            image_path = row['image_path']
            question = f"Answer this question based on the provided image: {image_path} - {question_text}"
            reasoning = f"From the image and question, the answer is: {answer_text}"
            answer = f"{reasoning} Therefore, the answer is {answer_text}."
            cot_data.append({'question': question, 'answer': answer})

    # Preprocess CORD-19 dataset for CoT
    if 'cord19' in datasets:
        df = datasets['cord19'].to_pandas()
        for _, row in df.iterrows():
            text = row['text']
            abstract = row['abstract']
            question = f"Based on this scientific article: {abstract}, {text}, what are the key findings?"
            reasoning = f"The article discusses the following key findings: {abstract}, {text}"
            answer = f"{reasoning} Therefore, the key findings are {abstract}, {text}."
            cot_data.append({'question': question, 'answer': answer})

    # Additional datasets can be processed similarly
    # ...

    return pd.DataFrame(cot_data)

# Prepare data for training
cot_df = preprocess_for_cot(datasets)

# Remove duplicates and reset index
cot_df.drop_duplicates(subset=['question'], inplace=True)
cot_df.reset_index(drop=True, inplace=True)

# Convert DataFrame to Hugging Face Dataset
dataset = Dataset.from_pandas(cot_df)

# Split dataset into training and validation sets
dataset = dataset.train_test_split(test_size=0.1, seed=42)

# Load tokenizer and model
model_name = 'google/flan-t5-large'  # Using a larger model for better performance
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)

# Tokenization function
def tokenize_function(examples):
    inputs = examples['question']
    targets = examples['answer']
    model_inputs = tokenizer(inputs, max_length=512, truncation=True)
    labels = tokenizer(targets, max_length=512, truncation=True)
    model_inputs['labels'] = labels['input_ids']
    return model_inputs

# Tokenize datasets
tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=['question', 'answer'])

# Define training arguments
training_args = TrainingArguments(
    output_dir='./results',
    evaluation_strategy='steps',
    eval_steps=500,
    save_steps=500,
    logging_steps=200,
    learning_rate=3e-5,
    per_device_train_batch_size=2,
    per_device_eval_batch_size=2,
    num_train_epochs=3,
    weight_decay=0.01,
    save_total_limit=2,
    load_best_model_at_end=True,
    metric_for_best_model='bleu',
    greater_is_better=True,
    fp16=True if device=='cuda' else False,
    gradient_accumulation_steps=16,
    dataloader_num_workers=2,
    report_to='none',  # Disable reporting to external services
)

# Data collator
from transformers import DataCollatorForSeq2Seq
data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)

# Define metrics
bleu_metric = evaluate.load('sacrebleu')

def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    # Replace -100 in labels as we can't decode them
    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)
    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
    decoded_preds = [pred.strip() for pred in decoded_preds]
    decoded_labels = [label.strip() for label in decoded_labels]
    # SacreBLEU expects a list of references for each hypothesis
    decoded_labels = [[label] for label in decoded_labels]
    result = bleu_metric.compute(predictions=decoded_preds, references=decoded_labels)
    return {'bleu': result['score']}

# Initialize Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_datasets['train'],
    eval_dataset=tokenized_datasets['test'],
    tokenizer=tokenizer,
    data_collator=data_collator,
    compute_metrics=compute_metrics,
)

# Train the model
trainer.train()

# Save the model
trainer.save_model('./medical_diagnostic_model')

# Function to make predictions with chain-of-thought
def diagnose_patient(question):
    inputs = tokenizer(question, return_tensors='pt', truncation=True, max_length=512).to(device)
    outputs = model.generate(
        **inputs,
        max_length=512,
        num_beams=5,
        early_stopping=True,
        no_repeat_ngram_size=2,
        temperature=0.7
    )
    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return answer

# Interactive Interface
def on_submit(change):
    clear_output()
    display(input_box)
    question = input_box.value
    print(f"\n**Patient Query:** {question}")
    diagnosis = diagnose_patient(question)
    print(f"\n**Model's Diagnosis and Reasoning:**\n{diagnosis}")

# Create an input box widget
input_box = widgets.Text(
    value='',
    placeholder='Describe patient symptoms or ask a medical question',
    description='Your Question:',
    disabled=False,
    layout=widgets.Layout(width='80%')
)

# Set up event handler for when the user presses enter
input_box.on_submit(on_submit)

# Display the input box
display(input_box)
    
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