correction

HF_LayoutLM_with_Passage.py

@@ -1,488 +1,3 @@
-# import json
-# import argparse
-# import os
-# import random
-# import numpy as np
-# import torch
-# import torch.nn as nn
-# from torch.utils.data import Dataset, DataLoader, random_split
-# from transformers import LayoutLMv3TokenizerFast, LayoutLMv3Model
-# from TorchCRF import CRF
-# from torch.optim import AdamW
-# from tqdm import tqdm  # Keep for evaluate
-# from sklearn.metrics import precision_recall_fscore_support
-# import fitz  # PyMuPDF
-# import pytesseract
-# from PIL import Image
-# from pdf2image import convert_from_path
-#
-# # --- Configuration for Augmentation ---
-# MAX_BBOX_DIMENSION = 999
-# MAX_SHIFT = 30
-# AUGMENTATION_FACTOR = 1
-#
-#
-# # -------------------------------------
-#
-#
-# # -------------------------
-# # Step 1: Preprocessing (Label Studio → BIO + bboxes)
-# # -------------------------
-# def preprocess_labelstudio(input_path, output_path):
-#     with open(input_path, "r", encoding="utf-8") as f:
-#         data = json.load(f)
-#
-#     processed = []
-#     total_items = len(data)
-#     print(f"🔄 Starting preprocessing of {total_items} documents...")
-#
-#     for i, item in enumerate(data):
-#         words = item["data"]["original_words"]
-#         bboxes = item["data"]["original_bboxes"]
-#         labels = ["O"] * len(words)
-#
-#         if "annotations" in item:
-#             for ann in item["annotations"]:
-#                 for res in ann["result"]:
-#                     # Check if the result item is a span annotation
-#                     if "value" in res and "labels" in res["value"]:
-#                         text = res["value"]["text"]
-#                         tag = res["value"]["labels"][0]
-#                         # Some tokenizers may split words, so we must find a consecutive word match.
-#                         text_tokens = text.split()
-#
-#                         for j in range(len(words) - len(text_tokens) + 1):
-#                             if words[j:j + len(text_tokens)] == text_tokens:
-#                                 labels[j] = f"B-{tag}"
-#                                 for k in range(1, len(text_tokens)):
-#                                     labels[j + k] = f"I-{tag}"
-#                                 break  # Move to next annotation if a match is found
-#
-#         processed.append({"tokens": words, "labels": labels, "bboxes": bboxes})
-#
-#         # --- HEARTBEAT LOGGING ---
-#         if (i + 1) % 50 == 0:
-#             print(f"--- HEARTBEAT: Preprocessed {i + 1}/{total_items} documents ---")
-#         # -------------------------
-#
-#     print(f"✅ Preprocessed data saved to {output_path}")
-#     return output_path
-#
-#
-# # -------------------------
-# # Step 1.5: Bounding Box Augmentation
-# # -------------------------
-#
-# def translate_bbox(bbox, shift_x, shift_y):
-#     """
-#     Translates a single bounding box [x_min, y_min, x_max, y_max] by (shift_x, shift_y)
-#     and clamps the coordinates to the valid range [0, MAX_BBOX_DIMENSION].
-#     """
-#     x_min, y_min, x_max, y_max = bbox
-#
-#     new_x_min = x_min + shift_x
-#     new_y_min = y_min + shift_y
-#     new_x_max = x_max + shift_x
-#     new_y_max = y_max + shift_y
-#
-#     # Clamp the new coordinates
-#     new_x_min = max(0, min(new_x_min, MAX_BBOX_DIMENSION))
-#     new_y_min = max(0, min(new_y_min, MAX_BBOX_DIMENSION))
-#     new_x_max = max(0, min(new_x_max, MAX_BBOX_DIMENSION))
-#     new_y_max = max(0, min(new_y_max, MAX_BBOX_DIMENSION))
-#
-#     # Safety check
-#     if new_x_min > new_x_max: new_x_min = new_x_max
-#     if new_y_min > new_y_max: new_y_min = new_y_max
-#
-#     return [new_x_min, new_y_min, new_x_max, new_y_max]
-#
-#
-# def augment_sample(sample):
-#     """
-#     Generates a new sample by translating all bounding boxes.
-#     """
-#     shift_x = random.randint(-MAX_SHIFT, MAX_SHIFT)
-#     shift_y = random.randint(-MAX_SHIFT, MAX_SHIFT)
-#
-#     new_sample = sample.copy()
-#
-#     # Ensure tokens and labels are copied (they remain unchanged)
-#     new_sample["tokens"] = sample["tokens"]
-#     new_sample["labels"] = sample["labels"]
-#
-#     # Translate all bounding boxes
-#     new_bboxes = [translate_bbox(bbox, shift_x, shift_y) for bbox in sample["bboxes"]]
-#     new_sample["bboxes"] = new_bboxes
-#
-#     return new_sample
-#
-#
-# def augment_and_save_dataset(input_json_path, output_json_path):
-#     """
-#     Loads preprocessed data, performs augmentation, and saves the result.
-#     """
-#     print(f"🔄 Loading preprocessed data from {input_json_path} for augmentation...")
-#     with open(input_json_path, 'r', encoding="utf-8") as f:
-#         training_data = json.load(f)
-#
-#     augmented_data = []
-#     original_count = len(training_data)
-#
-#     print(f"🔄 Starting augmentation (Factor: {AUGMENTATION_FACTOR}, {original_count} documents)...")
-#
-#     for i, original_sample in enumerate(training_data):
-#         # 1. Add the original sample
-#         augmented_data.append(original_sample)
-#
-#         # 2. Generate augmented samples
-#         for _ in range(AUGMENTATION_FACTOR):
-#             if "tokens" in original_sample and "labels" in original_sample and "bboxes" in original_sample:
-#                 augmented_data.append(augment_sample(original_sample))
-#             else:
-#                 print(f"Warning: Skipping augmentation for sample {i} due to missing keys.")
-#
-#         # --- HEARTBEAT LOGGING ---
-#         if (i + 1) % 50 == 0:
-#             print(f"--- HEARTBEAT: Augmented {i + 1}/{original_count} original documents ---")
-#         # -------------------------
-#
-#     augmented_count = len(augmented_data)
-#     print(f"Dataset Augmentation: Original samples: {original_count}, Total samples: {augmented_count}")
-#
-#     # Save the augmented dataset
-#     with open(output_json_path, 'w', encoding="utf-8") as f:
-#         json.dump(augmented_data, f, indent=2, ensure_ascii=False)
-#
-#     print(f"✅ Augmented data saved to {output_json_path}")
-#     return output_json_path
-#
-#
-# # -------------------------
-# # Step 2: Dataset Class (Unchanged)
-# # -------------------------
-# class LayoutDataset(Dataset):
-#     def __init__(self, json_path, tokenizer, label2id, max_len=512):
-#         with open(json_path, "r", encoding="utf-8") as f:
-#             self.data = json.load(f)
-#         self.tokenizer = tokenizer
-#         self.label2id = label2id
-#         self.max_len = max_len
-#
-#     def __len__(self):
-#         return len(self.data)
-#
-#     def __getitem__(self, idx):
-#         item = self.data[idx]
-#         words, bboxes, labels = item["tokens"], item["bboxes"], item["labels"]
-#
-#         # Tokenize
-#         encodings = self.tokenizer(
-#             words,
-#             boxes=bboxes,
-#             padding="max_length",
-#             truncation=True,
-#             max_length=self.max_len,
-#             return_offsets_mapping=True,
-#             return_tensors="pt"
-#         )
-#
-#         # Align labels to word pieces
-#         word_ids = encodings.word_ids(batch_index=0)
-#         label_ids = []
-#         for word_id in word_ids:
-#             if word_id is None:
-#                 label_ids.append(self.label2id["O"])  # [CLS], [SEP], padding
-#             else:
-#                 label_ids.append(self.label2id.get(labels[word_id], self.label2id["O"]))
-#
-#         encodings.pop("offset_mapping")
-#         encodings["labels"] = torch.tensor(label_ids)
-#
-#         return {key: val.squeeze(0) for key, val in encodings.items()}
-#
-#
-# # -------------------------
-# # Step 3: Model Architecture (Unchanged)
-# # -------------------------
-# class LayoutLMv3CRF(nn.Module):
-#     def __init__(self, model_name, num_labels):
-#         super().__init__()
-#         self.layoutlm = LayoutLMv3Model.from_pretrained(model_name)
-#         self.dropout = nn.Dropout(0.1)
-#         self.classifier = nn.Linear(self.layoutlm.config.hidden_size, num_labels)
-#         self.crf = CRF(num_labels)
-#
-#     def forward(self, input_ids, bbox, attention_mask, labels=None):
-#         outputs = self.layoutlm(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask)
-#         sequence_output = self.dropout(outputs.last_hidden_state)
-#         emissions = self.classifier(sequence_output)
-#
-#         if labels is not None:
-#             # Training mode: calculate loss
-#             log_likelihood = self.crf(emissions, labels, mask=attention_mask.bool())
-#             return -log_likelihood.mean()
-#         else:
-#             # Inference mode: decode best path
-#             best_paths = self.crf.viterbi_decode(emissions, mask=attention_mask.bool())
-#             return best_paths
-#
-#
-# # -------------------------
-# # Step 4: Training + Evaluation (Modified for Verbose Logging)
-# # -------------------------
-# def train_one_epoch(model, dataloader, optimizer, device):
-#     model.train()
-#     total_loss = 0
-#
-#     # Removed tqdm here to ensure cleaner log streaming to Gradio.
-#     for batch_idx, batch in enumerate(dataloader):
-#         batch = {k: v.to(device) for k, v in batch.items()}
-#         labels = batch.pop("labels")
-#         optimizer.zero_grad()
-#         loss = model(**batch, labels=labels)
-#         loss.backward()
-#         optimizer.step()
-#         total_loss += loss.item()
-#
-#         # VERBOSE LOGGING: Print batch progress every 5 batches to keep the Gradio connection alive
-#         if (batch_idx + 1) % 5 == 0:
-#             print(f"| Epoch Progress | Batch {batch_idx + 1}/{len(dataloader)} | Current Batch Loss: {loss.item():.4f}")
-#
-#     return total_loss / len(dataloader)
-#
-#
-# def evaluate(model, dataloader, device, id2label):
-#     model.eval()
-#     all_preds, all_labels = [], []
-#     with torch.no_grad():
-#         for batch in tqdm(dataloader, desc="Evaluating"):
-#             batch = {k: v.to(device) for k, v in batch.items()}
-#             labels = batch.pop("labels").cpu().numpy()
-#             preds = model(**batch)
-#             for p, l, mask in zip(preds, labels, batch["attention_mask"].cpu().numpy()):
-#                 valid = mask == 1
-#                 l = l[valid].tolist()
-#                 all_labels.extend(l)
-#                 all_preds.extend(p[:len(l)])
-#
-#     # Exclude the "O" label and other special tokens if necessary, but using 'micro' average
-#     # on all valid tokens is typically fine for the initial evaluation.
-#     precision, recall, f1, _ = precision_recall_fscore_support(all_labels, all_preds, average="micro", zero_division=0)
-#     return precision, recall, f1
-#
-#
-# # -------------------------
-# # Step 5: Main Pipeline (Training) - MODIFIED LABELS
-# # -------------------------
-# def main(args):
-#     # LABELS UPDATED: Added SECTION_HEADING and PASSAGE
-#     labels = [
-#         "O",
-#         "B-QUESTION", "I-QUESTION",
-#         "B-OPTION", "I-OPTION",
-#         "B-ANSWER", "I-ANSWER",
-#         "B-SECTION_HEADING", "I-SECTION_HEADING",
-#         "B-PASSAGE", "I-PASSAGE"
-#     ]
-#     label2id = {l: i for i, l in enumerate(labels)}
-#     id2label = {i: l for l, i in label2id.items()}
-#
-#     # 1. Preprocess and save the initial training data
-#     print("\n--- START PHASE: PREPROCESSING ---")
-#     initial_bio_json = "training_data_bio_bboxes.json"
-#     preprocess_labelstudio(args.input, initial_bio_json)
-#
-#     # 2. Augment the dataset with translated bboxes
-#     print("\n--- START PHASE: AUGMENTATION ---")
-#     augmented_bio_json = "augmented_training_data_bio_bboxes.json"
-#     final_data_path = augment_and_save_dataset(initial_bio_json, augmented_bio_json)
-#
-#     # Clean up the intermediary file (optional)
-#     # os.remove(initial_bio_json)
-#
-#     # 3. Load and split augmented dataset
-#     print("\n--- START PHASE: MODEL/DATASET SETUP ---")
-#     tokenizer = LayoutLMv3TokenizerFast.from_pretrained("microsoft/layoutlmv3-base")
-#     dataset = LayoutDataset(final_data_path, tokenizer, label2id, max_len=args.max_len)
-#     val_size = int(0.2 * len(dataset))
-#     train_size = len(dataset) - val_size
-#
-#     # Use a fixed seed for reproducibility in split
-#     torch.manual_seed(42)
-#     train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
-#     train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True)
-#     val_loader = DataLoader(val_dataset, batch_size=args.batch_size)
-#     print(f"Dataset split: Train samples: {train_size}, Validation samples: {val_size}")
-#
-#     # 4. Initialize and load model
-#     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-#     print(f"Using device: {device}")
-#     # Num_labels is based on the updated 'labels' list
-#     model = LayoutLMv3CRF("microsoft/layoutlmv3-base", num_labels=len(labels)).to(device)
-#     ckpt_path = "checkpoints/layoutlmv3_crf_passage.pth"
-#     os.makedirs("checkpoints", exist_ok=True)
-#     if os.path.exists(ckpt_path):
-#         # NOTE: Loading an old checkpoint will likely fail now because num_labels has changed,
-#         # unless the old checkpoint had the *exact* same number of labels.
-#         # It is recommended to start training from scratch.
-#         # print(f"🔄 Loading checkpoint from {ckpt_path}")
-#         # model.load_state_dict(torch.load(ckpt_path, map_location=device))
-#         print(f"⚠️ Starting fresh training. Old checkpoint {ckpt_path} may be incompatible with new label count.")
-#
-#     optimizer = AdamW(model.parameters(), lr=args.lr)
-#
-#     # 5. Training loop
-#     for epoch in range(args.epochs):
-#         print(f"\n--- START PHASE: EPOCH {epoch + 1}/{args.epochs} TRAINING ---")
-#         avg_loss = train_one_epoch(model, train_loader, optimizer, device)
-#
-#         print(f"\n--- START PHASE: EPOCH {epoch + 1}/{args.epochs} EVALUATION ---")
-#         precision, recall, f1 = evaluate(model, val_loader, device, id2label)
-#
-#         print(
-#             f"Epoch {epoch + 1}/{args.epochs} | Avg Loss: {avg_loss:.4f} | P: {precision:.3f} R: {recall:.3f} F1: {f1:.3f}")
-#         torch.save(model.state_dict(), ckpt_path)
-#         print(f"💾 Model saved at {ckpt_path}")
-#
-#
-# def run_inference(pdf_path, model_path, output_path):
-#     # LABELS UPDATED: Added SECTION_HEADING and PASSAGE (Must match main)
-#     labels = [
-#         "O",
-#         "B-QUESTION", "I-QUESTION",
-#         "B-OPTION", "I-OPTION",
-#         "B-ANSWER", "I-ANSWER",
-#         "B-SECTION_HEADING", "I-SECTION_HEADING",
-#         "B-PASSAGE", "I-PASSAGE"
-#     ]
-#     label2id = {l: i for i, l in enumerate(labels)}
-#     id2label = {i: l for l, i in label2id.items()}
-#     tokenizer = LayoutLMv3TokenizerFast.from_pretrained("microsoft/layoutlmv3-base")
-#
-#     # Load the trained model
-#     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-#     model = LayoutLMv3CRF("microsoft/layoutlmv3-base", num_labels=len(labels)).to(device)
-#     try:
-#         model.load_state_dict(torch.load(model_path, map_location=device))
-#     except Exception as e:
-#         print(
-#             f"❌ Error loading model state: {e}. Ensure the model at {model_path} has been successfully trained with the new labels.")
-#         return
-#
-#     model.eval()
-#
-#     # Process PDF with OCR
-#     try:
-#         doc = fitz.open(pdf_path)
-#     except Exception as e:
-#         print(f"❌ Error opening PDF: {e}")
-#         return
-#
-#     all_predictions = []
-#     tesseract_config = '--psm 6'
-#
-#     for page_num in range(len(doc)):
-#         page = doc.load_page(page_num)
-#
-#         # Get a high-resolution image of the page for Tesseract
-#         pix = page.get_pixmap(dpi=300)
-#         img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
-#
-#         # Get page dimensions from PyMuPDF
-#         page_width, page_height = page.bound().width, page.bound().height
-#
-#         # Get OCR data (words and bboxes)
-#         ocr_data = pytesseract.image_to_data(img, output_type=pytesseract.Output.DICT, config=tesseract_config)
-#         words = [word for word in ocr_data['text'] if word.strip()]
-#
-#         # Skip empty pages
-#         if not words:
-#             continue
-#
-#         # Get the scaling factors from the image resolution to the PDF's native resolution
-#         x_scale = page_width / pix.width
-#         y_scale = page_height / pix.height
-#
-#         # Create original pixel bboxes
-#         bboxes_raw = [[
-#             ocr_data['left'][i],
-#             ocr_data['top'][i],
-#             ocr_data['left'][i] + ocr_data['width'][i],
-#             ocr_data['top'][i] + ocr_data['height'][i]
-#         ] for i in range(len(ocr_data['text'])) if ocr_data['text'][i].strip()]
-#
-#         # Normalize bboxes to 0-1000 scale using the correct scaling factors
-#         normalized_bboxes = [[
-#             int(1000 * (b[0] * x_scale) / page_width),
-#             int(1000 * (b[1] * y_scale) / page_height),
-#             int(1000 * (b[2] * x_scale) / page_width),
-#             int(1000 * (b[3] * y_scale) / page_height)
-#         ] for b in bboxes_raw]
-#
-#         # Tokenize and run inference
-#         inputs = tokenizer(words, boxes=normalized_bboxes, return_tensors="pt", truncation=True).to(device)
-#
-#         with torch.no_grad():
-#             # The model is run on the normalized bboxes
-#             preds = model(**inputs)
-#
-#         # Align predictions back to words
-#         word_ids = inputs.word_ids(batch_index=0)
-#         final_preds = []
-#         previous_word_idx = None
-#         for idx, word_id in enumerate(word_ids):
-#             if word_id is not None and word_id != previous_word_idx:
-#                 # The model returns a list of predicted classes for each token
-#                 final_preds.append(id2label[preds[0][idx]])
-#             previous_word_idx = word_id
-#
-#         # Prepare structured output
-#         page_results = []
-#         # Tesseract returns word list that is shorter than ocr_data if it contains empty strings.
-#         # We need to use the cleaned 'words' list and its corresponding filtered bboxes.
-#         # Note: We must ensure that the word and bbox lists passed to tokenizer and the filtered
-#         # final_preds list are all correctly aligned with the original ocr_data indices.
-#         # Since 'words' and 'bboxes_raw' are filtered exactly the same way (by word.strip()),
-#         # and 'final_preds' is aligned back to 'words', we can zip them.
-#         for word, bbox, label in zip(words, bboxes_raw, final_preds):
-#             page_results.append({
-#                 "word": word,
-#                 "bbox": bbox,
-#                 "predicted_label": label
-#             })
-#         all_predictions.extend(page_results)
-#
-#     doc.close()
-#     with open(output_path, "w") as f:
-#         json.dump(all_predictions, f, indent=2, ensure_ascii=False)
-#     print(f"✅ Inference complete. Predictions saved to {output_path}")
-#
-#
-# # -------------------------
-# # Step 7: Main Execution (Unchanged)
-# # -------------------------
-# if __name__ == "__main__":
-#     parser = argparse.ArgumentParser(description="LayoutLMv3 Fine-tuning and Inference Script.")
-#     parser.add_argument("--mode", type=str, required=True, choices=["train", "infer"],
-#                         help="Select mode: 'train' or 'infer'")
-#     parser.add_argument("--input", type=str, help="Path to input file (Label Studio JSON for train, PDF for infer).")
-#     parser.add_argument("--batch_size", type=int, default=4)
-#     parser.add_argument("--epochs", type=int, default=5)
-#     parser.add_argument("--lr", type=float, default=5e-5)
-#     parser.add_argument("--max_len", type=int, default=512)
-#     args = parser.parse_args()
-#
-#     if args.mode == "train":
-#         if not args.input:
-#             parser.error("--input is required for 'train' mode.")
-#         main(args)
-#     elif args.mode == "infer":
-#         if not args.input:
-#             parser.error("--input is required for 'infer' mode.")
-#         # NOTE: The model path here should ideally match the ckpt_path in main: checkpoints/layoutlmv3_crf_passage.pth
-#         run_inference(args.input, "checkpoints/layoutlmv3_crf_new_passage.pth", "inference_predictions.json")
-
 
 import json
 import argparse
@@ -688,7 +203,8 @@ class LayoutDataset(Dataset):
 class LayoutLMv3CRF(nn.Module):
     def __init__(self, model_name, num_labels):
         super().__init__()
-        self.layoutlm = LayoutLMv3Model.from_pretrained(model_name)
+        # self.layoutlm = LayoutLMv3Model.from_pretrained(model_name)
+        self.layoutlm = LayoutLMv3Model.from_pretrained("heerjtdev/edugenius")
         self.dropout = nn.Dropout(0.1)
         self.classifier = nn.Linear(self.layoutlm.config.hidden_size, num_labels)
         self.crf = CRF(num_labels)
@@ -786,7 +302,10 @@ def main(args):
 
     # 3. Load and split augmented dataset
     print("\n--- START PHASE: MODEL/DATASET SETUP ---")
-    tokenizer = LayoutLMv3TokenizerFast.from_pretrained("microsoft/layoutlmv3-base")
+    MODEL_ID = "heerjtdev/edugenius"
+    # tokenizer = LayoutLMv3TokenizerFast.from_pretrained("microsoft/layoutlmv3-base")
+    tokenizer = LayoutLMv3TokenizerFast.from_pretrained(MODEL_ID)
+
     dataset = LayoutDataset(final_data_path, tokenizer, label2id, max_len=args.max_len)
     val_size = int(0.2 * len(dataset))
     train_size = len(dataset) - val_size
@@ -801,7 +320,8 @@ def main(args):
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     print(f"Using device: {device}")
     # Num_labels is based on the updated 'labels' list
-    model = LayoutLMv3CRF("microsoft/layoutlmv3-base", num_labels=len(labels)).to(device)
+    # model = LayoutLMv3CRF("microsoft/layoutlmv3-base", num_labels=len(labels)).to(device)
+    model = LayoutLMv3CRF(MODEL_ID, num_labels=len(labels)).to(device)
     ckpt_path = "checkpoints/layoutlmv3_crf_passage.pth"
     os.makedirs("checkpoints", exist_ok=True)
     if os.path.exists(ckpt_path):