# from flask import Flask, request, jsonify # import os # import pdfplumber # import pytesseract # from PIL import Image # from transformers import PegasusForConditionalGeneration, PegasusTokenizer # import torch # import logging # app = Flask(__name__) # # Set up logging # logging.basicConfig(level=logging.INFO) # logger = logging.getLogger(__name__) # # Load Pegasus Model (load once globally) # logger.info("Loading Pegasus model and tokenizer...") # tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-xsum") # model = PegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum").to("cpu") # Force CPU to manage memory # logger.info("Model loaded successfully.") # # Extract text from PDF with page limit # def extract_text_from_pdf(file_path, max_pages=5): # text = "" # try: # with pdfplumber.open(file_path) as pdf: # total_pages = len(pdf.pages) # pages_to_process = min(total_pages, max_pages) # logger.info(f"Extracting text from {pages_to_process} of {total_pages} pages in {file_path}") # for i, page in enumerate(pdf.pages[:pages_to_process]): # try: # extracted = page.extract_text() # if extracted: # text += extracted + "\n" # else: # logger.info(f"No text on page {i+1}, attempting OCR...") # image = page.to_image().original # text += pytesseract.image_to_string(image) + "\n" # except Exception as e: # logger.warning(f"Error processing page {i+1}: {e}") # continue # except Exception as e: # logger.error(f"Failed to process PDF {file_path}: {e}") # return "" # return text.strip() # # Extract text from image (OCR) # def extract_text_from_image(file_path): # try: # logger.info(f"Extracting text from image {file_path} using OCR...") # image = Image.open(file_path) # text = pytesseract.image_to_string(image) # return text.strip() # except Exception as e: # logger.error(f"Failed to process image {file_path}: {e}") # return "" # # Summarize text with chunking for large inputs # def summarize_text(text, max_input_length=512, max_output_length=150): # try: # logger.info("Summarizing text...") # # Tokenize and truncate to max_input_length # inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=max_input_length, padding=True) # input_length = inputs["input_ids"].shape[1] # logger.info(f"Input length: {input_length} tokens") # # Adjust generation params for efficiency # summary_ids = model.generate( # inputs["input_ids"], # max_length=max_output_length, # min_length=30, # num_beams=2, # Reduce beams for speedup # early_stopping=True, # length_penalty=1.0, # Encourage shorter outputs # ) # summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) # logger.info("Summarization completed.") # return summary # except Exception as e: # logger.error(f"Error during summarization: {e}") # return "" # @app.route('/summarize', methods=['POST']) # def summarize_document(): # if 'file' not in request.files: # logger.error("No file uploaded in request.") # return jsonify({"error": "No file uploaded"}), 400 # file = request.files['file'] # filename = file.filename # if not filename: # logger.error("Empty filename in request.") # return jsonify({"error": "No file uploaded"}), 400 # file_path = os.path.join("/tmp", filename) # try: # file.save(file_path) # logger.info(f"File saved to {file_path}") # if filename.lower().endswith('.pdf'): # text = extract_text_from_pdf(file_path, max_pages=2) # Reduce to 2 pages # elif filename.lower().endswith(('.png', '.jpeg', '.jpg')): # text = extract_text_from_image(file_path) # else: # logger.error(f"Unsupported file format: {filename}") # return jsonify({"error": "Unsupported file format. Use PDF, PNG, JPEG, or JPG"}), 400 # if not text: # logger.warning(f"No text extracted from {filename}") # return jsonify({"error": "No text extracted from the file"}), 400 # summary = summarize_text(text) # if not summary: # logger.warning("Summarization failed to produce output.") # return jsonify({"error": "Failed to generate summary"}), 500 # logger.info(f"Summary generated for {filename}") # return jsonify({"summary": summary}) # except Exception as e: # logger.error(f"Unexpected error processing {filename}: {e}") # return jsonify({"error": str(e)}), 500 # finally: # if os.path.exists(file_path): # try: # os.remove(file_path) # logger.info(f"Cleaned up file: {file_path}") # except Exception as e: # logger.warning(f"Failed to delete {file_path}: {e}") # if __name__ == '__main__': # logger.info("Starting Flask app...") # app.run(host='0.0.0.0', port=7860) import os import pdfplumber from PIL import Image import pytesseract import numpy as np from flask import Flask, request, jsonify from flask_cors import CORS from transformers import PegasusForConditionalGeneration, PegasusTokenizer, BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments from datasets import load_dataset, concatenate_datasets import torch from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity app = Flask(__name__) CORS(app) UPLOAD_FOLDER = 'uploads' PEGASUS_MODEL_DIR = 'fine_tuned_pegasus' BERT_MODEL_DIR = 'fine_tuned_bert' LEGALBERT_MODEL_DIR = 'fine_tuned_legalbert' MAX_FILE_SIZE = 100 * 1024 * 1024 os.makedirs(UPLOAD_FOLDER, exist_ok=True) transformers.logging.set_verbosity_error() os.environ["HF_HUB_DISABLE_SYMLINKS_WARNING"] = "1" # Pegasus Fine-Tuning def load_or_finetune_pegasus(): if os.path.exists(PEGASUS_MODEL_DIR): print("Loading fine-tuned Pegasus model...") tokenizer = PegasusTokenizer.from_pretrained(PEGASUS_MODEL_DIR) model = PegasusForConditionalGeneration.from_pretrained(PEGASUS_MODEL_DIR) else: print("Fine-tuning Pegasus on CNN/Daily Mail and XSUM...") tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-xsum") model = PegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum") # Load and combine datasets cnn_dm = load_dataset("cnn_dailymail", "3.0.0", split="train[:5000]") # 5K samples xsum = load_dataset("xsum", split="train[:5000]") # 5K samples combined_dataset = concatenate_datasets([cnn_dm, xsum]) def preprocess_function(examples): inputs = tokenizer(examples["article"] if "article" in examples else examples["document"], max_length=512, truncation=True, padding="max_length") targets = tokenizer(examples["highlights"] if "highlights" in examples else examples["summary"], max_length=400, truncation=True, padding="max_length") inputs["labels"] = targets["input_ids"] return inputs tokenized_dataset = combined_dataset.map(preprocess_function, batched=True) train_dataset = tokenized_dataset.select(range(8000)) # 80% eval_dataset = tokenized_dataset.select(range(8000, 10000)) # 20% training_args = TrainingArguments( output_dir="./pegasus_finetune", num_train_epochs=3, # Increased for better fine-tuning per_device_train_batch_size=1, per_device_eval_batch_size=1, warmup_steps=500, weight_decay=0.01, logging_dir="./logs", logging_steps=10, eval_strategy="epoch", save_strategy="epoch", load_best_model_at_end=True, ) trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset, ) trainer.train() trainer.save_model(PEGASUS_MODEL_DIR) tokenizer.save_pretrained(PEGASUS_MODEL_DIR) print(f"Fine-tuned Pegasus saved to {PEGASUS_MODEL_DIR}") return tokenizer, model # BERT Fine-Tuning def load_or_finetune_bert(): if os.path.exists(BERT_MODEL_DIR): print("Loading fine-tuned BERT model...") tokenizer = BertTokenizer.from_pretrained(BERT_MODEL_DIR) model = BertForSequenceClassification.from_pretrained(BERT_MODEL_DIR, num_labels=2) else: print("Fine-tuning BERT on CNN/Daily Mail for extractive summarization...") tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2) # Load dataset and preprocess for sentence classification cnn_dm = load_dataset("cnn_dailymail", "3.0.0", split="train[:5000]") def preprocess_for_extractive(examples): sentences = [] labels = [] for article, highlights in zip(examples["article"], examples["highlights"]): article_sents = article.split(". ") highlight_sents = highlights.split(". ") for sent in article_sents: if sent.strip(): # Label as 1 if sentence is similar to any highlight, else 0 is_summary = any(sent.strip() in h for h in highlight_sents) sentences.append(sent) labels.append(1 if is_summary else 0) return {"sentence": sentences, "label": labels} dataset = cnn_dm.map(preprocess_for_extractive, batched=True, remove_columns=["article", "highlights", "id"]) tokenized_dataset = dataset.map( lambda x: tokenizer(x["sentence"], max_length=512, truncation=True, padding="max_length"), batched=True ) tokenized_dataset = tokenized_dataset.remove_columns(["sentence"]) train_dataset = tokenized_dataset.select(range(int(0.8 * len(tokenized_dataset)))) eval_dataset = tokenized_dataset.select(range(int(0.8 * len(tokenized_dataset)), len(tokenized_dataset))) training_args = TrainingArguments( output_dir="./bert_finetune", num_train_epochs=3, per_device_train_batch_size=8, per_device_eval_batch_size=8, warmup_steps=500, weight_decay=0.01, logging_dir="./logs", logging_steps=10, eval_strategy="epoch", save_strategy="epoch", load_best_model_at_end=True, ) trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset, ) trainer.train() trainer.save_model(BERT_MODEL_DIR) tokenizer.save_pretrained(BERT_MODEL_DIR) print(f"Fine-tuned BERT saved to {BERT_MODEL_DIR}") return tokenizer, model # LegalBERT Fine-Tuning def load_or_finetune_legalbert(): if os.path.exists(LEGALBERT_MODEL_DIR): print("Loading fine-tuned LegalBERT model...") tokenizer = BertTokenizer.from_pretrained(LEGALBERT_MODEL_DIR) model = BertForSequenceClassification.from_pretrained(LEGALBERT_MODEL_DIR, num_labels=2) else: print("Fine-tuning LegalBERT on Billsum for extractive summarization...") tokenizer = BertTokenizer.from_pretrained("nlpaueb/legal-bert-base-uncased") model = BertForSequenceClassification.from_pretrained("nlpaueb/legal-bert-base-uncased", num_labels=2) # Load dataset billsum = load_dataset("billsum", split="train[:5000]") def preprocess_for_extractive(examples): sentences = [] labels = [] for text, summary in zip(examples["text"], examples["summary"]): text_sents = text.split(". ") summary_sents = summary.split(". ") for sent in text_sents: if sent.strip(): is_summary = any(sent.strip() in s for s in summary_sents) sentences.append(sent) labels.append(1 if is_summary else 0) return {"sentence": sentences, "label": labels} dataset = billsum.map(preprocess_for_extractive, batched=True, remove_columns=["text", "summary", "title"]) tokenized_dataset = dataset.map( lambda x: tokenizer(x["sentence"], max_length=512, truncation=True, padding="max_length"), batched=True ) tokenized_dataset = tokenized_dataset.remove_columns(["sentence"]) train_dataset = tokenized_dataset.select(range(int(0.8 * len(tokenized_dataset)))) eval_dataset = tokenized_dataset.select(range(int(0.8 * len(tokenized_dataset)), len(tokenized_dataset))) training_args = TrainingArguments( output_dir="./legalbert_finetune", num_train_epochs=3, per_device_train_batch_size=8, per_device_eval_batch_size=8, warmup_steps=500, weight_decay=0.01, logging_dir="./logs", logging_steps=10, eval_strategy="epoch", save_strategy="epoch", load_best_model_at_end=True, ) trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset, ) trainer.train() trainer.save_model(LEGALBERT_MODEL_DIR) tokenizer.save_pretrained(LEGALBERT_MODEL_DIR) print(f"Fine-tuned LegalBERT saved to {LEGALBERT_MODEL_DIR}") return tokenizer, model # Load models pegasus_tokenizer, pegasus_model = load_or_finetune_pegasus() bert_tokenizer, bert_model = load_or_finetune_bert() legalbert_tokenizer, legalbert_model = load_or_finetune_legalbert() def extract_text_from_pdf(file_path): text = "" with pdfplumber.open(file_path) as pdf: for page in pdf.pages: text += page.extract_text() or "" return text def extract_text_from_image(file_path): image = Image.open(file_path) text = pytesseract.image_to_string(image) return text def choose_model(text): legal_keywords = ["court", "legal", "law", "judgment", "contract", "statute", "case"] tfidf = TfidfVectorizer(vocabulary=legal_keywords) tfidf_matrix = tfidf.fit_transform([text.lower()]) score = np.sum(tfidf_matrix.toarray()) if score > 0.1: return "legalbert" elif len(text.split()) > 50: return "pegasus" else: return "bert" def summarize_with_pegasus(text): inputs = pegasus_tokenizer(text, truncation=True, padding="longest", return_tensors="pt", max_length=512) summary_ids = pegasus_model.generate( inputs["input_ids"], max_length=400, min_length=80, length_penalty=1.5, num_beams=4 ) return pegasus_tokenizer.decode(summary_ids[0], skip_special_tokens=True) def summarize_with_bert(text): sentences = text.split(". ") if len(sentences) < 6: # Ensure enough for 5 sentences return text inputs = bert_tokenizer(sentences, return_tensors="pt", padding=True, truncation=True, max_length=512) with torch.no_grad(): outputs = bert_model(**inputs) logits = outputs.logits probs = torch.softmax(logits, dim=1)[:, 1] # Probability of being a summary sentence key_sentence_idx = probs.argsort(descending=True)[:5] # Top 5 sentences return ". ".join([sentences[idx] for idx in key_sentence_idx if sentences[idx].strip()]) def summarize_with_legalbert(text): sentences = text.split(". ") if len(sentences) < 6: return text inputs = legalbert_tokenizer(sentences, return_tensors="pt", padding=True, truncation=True, max_length=512) with torch.no_grad(): outputs = legalbert_model(**inputs) logits = outputs.logits probs = torch.softmax(logits, dim=1)[:, 1] key_sentence_idx = probs.argsort(descending=True)[:5] return ". ".join([sentences[idx] for idx in key_sentence_idx if sentences[idx].strip()]) @app.route('/summarize', methods=['POST']) def summarize_document(): if 'file' not in request.files: return jsonify({"error": "No file uploaded"}), 400 file = request.files['file'] filename = file.filename file.seek(0, os.SEEK_END) file_size = file.tell() if file_size > MAX_FILE_SIZE: return jsonify({"error": f"File size exceeds {MAX_FILE_SIZE // (1024 * 1024)} MB"}), 413 file.seek(0) file_path = os.path.join(UPLOAD_FOLDER, filename) try: file.save(file_path) except Exception as e: return jsonify({"error": f"Failed to save file: {str(e)}"}), 500 try: if filename.endswith('.pdf'): text = extract_text_from_pdf(file_path) elif filename.endswith(('.png', '.jpeg', '.jpg')): text = extract_text_from_image(file_path) else: os.remove(file_path) return jsonify({"error": "Unsupported file format."}), 400 except Exception as e: os.remove(file_path) return jsonify({"error": f"Text extraction failed: {str(e)}"}), 500 if not text.strip(): os.remove(file_path) return jsonify({"error": "No text extracted"}), 400 try: model = choose_model(text) if model == "pegasus": summary = summarize_with_pegasus(text) elif model == "bert": summary = summarize_with_bert(text) elif model == "legalbert": summary = summarize_with_legalbert(text) except Exception as e: os.remove(file_path) return jsonify({"error": f"Summarization failed: {str(e)}"}), 500 os.remove(file_path) return jsonify({"model_used": model, "summary": summary}) if __name__ == '__main__': app.run(debug=True, host='0.0.0.0', port=5000)