app.py

import json
import os
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from sklearn.metrics import accuracy_score
from torch.utils.data import DataLoader
from transformers import Trainer, TrainingArguments
import time
import requests
from bs4 import BeautifulSoup
import tempfile
import zipfile
import mimetypes
from tqdm import tqdm
import logging
import gradio as gr

# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# --- URL and File Processing Functions ---
def fetch_content(url):
    """Fetch content from a given URL."""
    try:
        response = requests.get(url, timeout=10)
        response.raise_for_status()
        return response.text
    except requests.RequestException as e:
        logger.error(f"Error fetching {url}: {e}")
        return None

def extract_text(html):
    """Extract text from HTML content."""
    soup = BeautifulSoup(html, 'html.parser')
    for script in soup(["script", "style"]):
        script.decompose()
    text = soup.get_text()
    lines = (line.strip() for line in text.splitlines())
    chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
    return '\n'.join(chunk for chunk in chunks if chunk)

def process_urls(urls):
    """Process a list of URLs and return their extracted text."""
    dataset = []
    for url in tqdm(urls, desc="Fetching URLs"):
        html = fetch_content(url)
        if html:
            text = extract_text(html)
            dataset.append({
                "source": "url",
                "url": url,
                "content": text
            })
        time.sleep(1)  # Be polite to the server
    return dataset

def process_file(file):
    """Process uploaded files (including zip files) and extract text."""
    dataset = []
    with tempfile.TemporaryDirectory() as temp_dir:
        if zipfile.is_zipfile(file.name):
            with zipfile.ZipFile(file.name, 'r') as zip_ref:
                zip_ref.extractall(temp_dir)
            # Process each extracted file
            for root, _, files in os.walk(temp_dir):
                for filename in files:
                    filepath = os.path.join(root, filename)
                    mime_type, _ = mimetypes.guess_type(filepath)
                    if mime_type and mime_type.startswith('text'):
                        with open(filepath, 'r', errors='ignore') as f:
                            content = f.read()
                        dataset.append({
                            "source": "file",
                            "filename": filename,
                            "content": content
                        })
                    else:
                        dataset.append({
                            "source": "file",
                            "filename": filename,
                            "content": "Binary file - content not extracted"
                        })
        else:
            mime_type, _ = mimetypes.guess_type(file.name)
            if mime_type and mime_type.startswith('text'):
                content = file.read().decode('utf-8', errors='ignore')
                dataset.append({
                    "source": "file",
                    "filename": os.path.basename(file.name),
                    "content": content
                })
            else:
                dataset.append({
                    "source": "file",
                    "filename": os.path.basename(file.name),
                    "content": "Binary file - content not extracted"
                })
    return dataset

def process_text(text):
    """Process raw text input."""
    return [{
        "source": "text_input",
        "content": text
    }]

def create_dataset(urls, file, text_input):
    """Create a combined dataset from URLs, uploaded files, and text input."""
    dataset = []
    if urls:
        dataset.extend(process_urls([url.strip() for url in urls.split(',') if url.strip()]))
    if file:
        dataset.extend(process_file(file))
    if text_input:
        dataset.extend(process_text(text_input))

    output_file = 'combined_dataset.json'
    with open(output_file, 'w') as f:
        json.dump(dataset, f, indent=2)

    return output_file

# --- Model Training and Evaluation Functions ---
class CustomDataset(torch.utils.data.Dataset):
    def __init__(self, data, tokenizer, max_length=512):
        self.data = data
        self.tokenizer = tokenizer
        self.max_length = max_length

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        try:
            text = self.data[idx]['content ']
            label = self.data[idx].get('label', 0)

            encoding = self.tokenizer.encode_plus(
                text,
                max_length=self.max_length,
                padding='max_length',
                truncation=True,
                return_attention_mask=True,
                return_tensors='pt',
            )

            return {
                'input_ids': encoding['input_ids'].squeeze(),
                'attention_mask': encoding['attention_mask'].squeeze(),
                'labels': torch.tensor(label, dtype=torch.long)
            }
        except Exception as e:
            logger.error(f"Error in processing item {idx}: {e}")
            raise

def train_model(model_name, data, batch_size, epochs, learning_rate=1e-5, max_length=2048):
    try:
        model = AutoModelForSequenceClassification.from_pretrained(model_name)
        tokenizer = AutoTokenizer.from_pretrained(model_name)
        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        model.to(device)

        dataset = CustomDataset(data, tokenizer, max_length=max_length)
        train_size = int(0.8 * len(dataset))
        val_size = len(dataset) - train_size
        train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])

        training_args = TrainingArguments(
            output_dir='./results',
            num_train_epochs=epochs,
            per_device_train_batch_size=batch_size,
            per_device_eval_batch_size=batch_size,
            evaluation_strategy='epoch',
            learning_rate=learning_rate,
            save_steps=500,
            load_best_model_at_end=True,
            metric_for_best_model='accuracy',
            greater_is_better=True,
            save_total_limit=2,
            seed=42,
            dataloader_num_workers=4,
            fp16=torch.cuda.is_available()
        )

        trainer = Trainer(
            model=model,
            args=training_args,
            train_dataset=train_dataset,
            eval_dataset=val_dataset,
            compute_metrics=lambda pred: {
                'accuracy': accuracy_score(pred.label_ids, pred.predictions.argmax(-1))
            }
        )

        logger.info("Starting model training...")
        start_time = time.time()
        trainer.train()
        end_time = time.time()
        logger.info(f'Training time: {end_time - start_time:.2f} seconds')

        logger.info("Evaluating model...")
        eval_result = trainer.evaluate()
        logger.info(f'Evaluation result: {eval_result}')

        trainer.save_model('./model')

        return model, tokenizer

    except Exception as e:
        logger.error(f"Error during training: {e}")
        raise

def deploy_model(model, tokenizer):
    try:
        model.save_pretrained('./model')
        tokenizer.save_pretrained('./model')

        deployment_script = f'''
        import torch
        from transformers import AutoModelForSequenceClassification, AutoTokenizer
        model = AutoModelForSequenceClassification.from_pretrained('./model')
        tokenizer = AutoTokenizer.from_pretrained('./model')
        def predict(text):
            encoding = tokenizer.encode_plus(
                text,
                max_length=512,
                padding='max_length',
                truncation=True,
                return_attention_mask=True,
                return_tensors='pt',
            )
            input_ids = encoding['input_ids'].to('cuda' if torch.cuda.is_available() else 'cpu')
            attention_mask = encoding['attention_mask'].to('cuda' if torch.cuda.is_available() else 'cpu')
            outputs = model(input_ids, attention_mask=attention_mask)
            logits = outputs.logits
            return torch.argmax(logits, dim=1).cpu().numpy()[0]
        '''

        with open('./deployment.py', 'w') as f:
            f.write(deployment_script)

        logger.info('Model deployed successfully. To use the model, run: python deployment.py')

    except Exception as e:
        logger.error(f"Error deploying model: {e}")
        raise

# Gradio Interface
def gradio_interface(urls, file, text_input, model_name, batch_size, epochs):
    dataset_file = create_dataset(urls, file, text_input)
    
    with open(dataset_file, 'r') as f:
        dataset = json.load(f)

    model, tokenizer = train_model(model_name, dataset, batch_size, epochs)

    deploy_model(model, tokenizer)

    return dataset_file

iface = gr.Interface(
    fn=gradio_interface,
    inputs=[
        gr.Textbox(lines=5, label="Enter comma-separated URLs"),
        gr.File(label="Upload file (including zip files)", type="filepath"),
        gr.Textbox(lines=10, label="Enter or paste large text"),
        gr.Textbox(label="Model name", value="distilbert-base-uncased"),
        gr.Number(label="Batch size", value=8),
        gr.Number(label="Epochs", value=3),
    ],
 outputs=gr.File(label="Download Combined Dataset"),
    title="Dataset Creation and Model Training",
    description="Enter URLs, upload files (including zip files), and/or paste text to create a dataset and train a model.",
)

# Launch the interface
if __name__ == "__main__":
    iface.launch()