Update train.py

train.py

@@ -1,244 +1,107 @@
-import json
-import argparse
+import gradio as gr
+import fitz  # PyMuPDF
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain_huggingface import HuggingFaceEmbeddings
 import os
-import random
-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
-from sklearn.metrics import precision_recall_fscore_support
-
-# --- Configuration ---
-MAX_BBOX_DIMENSION = 1000
-MAX_SHIFT = 30
-AUGMENTATION_FACTOR = 1
-BASE_MODEL_ID = "heerjtdev/MLP_LayoutLM"
-
-# -------------------------
-# Step 1: Preprocessing
-# -------------------------
-def preprocess_labelstudio(input_path, output_path):
-    with open(input_path, "r", encoding="utf-8") as f:
-        data = json.load(f)
-
-    processed = []
-    print(f"🔄 Starting preprocessing of {len(data)} documents...")
-
-    for item in data:
-        words = item["data"]["original_words"]
-        bboxes = item["data"]["original_bboxes"]
-        labels = ["O"] * len(words)
-
-        clamped_bboxes = []
-        for bbox in bboxes:
-            x_min, y_min, x_max, y_max = bbox
-            new_x_min = max(0, min(x_min, 1000))
-            new_y_min = max(0, min(y_min, 1000))
-            new_x_max = max(0, min(x_max, 1000))
-            new_y_max = max(0, min(y_max, 1000))
-            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
-            clamped_bboxes.append([new_x_min, new_y_min, new_x_max, new_y_max])
-
-        if "annotations" in item:
-            for ann in item["annotations"]:
-                for res in ann["result"]:
-                    if "value" in res and "labels" in res["value"]:
-                        text = res["value"]["text"]
-                        tag = res["value"]["labels"][0]
-                        text_tokens = text.split()
-                        for i in range(len(words) - len(text_tokens) + 1):
-                            if words[i:i + len(text_tokens)] == text_tokens:
-                                labels[i] = f"B-{tag}"
-                                for j in range(1, len(text_tokens)):
-                                    labels[i + j] = f"I-{tag}"
-                                break
-
-        processed.append({"tokens": words, "labels": labels, "bboxes": clamped_bboxes})
-
-    with open(output_path, "w", encoding="utf-8") as f:
-        json.dump(processed, f, indent=2, ensure_ascii=False)
-    return output_path
-
-# -------------------------
-# Step 1.5: Augmentation
-# -------------------------
-def translate_bbox(bbox, shift_x, shift_y):
-    x_min, y_min, x_max, y_max = bbox
-    new_x_min = max(0, min(x_min + shift_x, 1000))
-    new_y_min = max(0, min(y_min + shift_y, 1000))
-    new_x_max = max(0, min(x_max + shift_x, 1000))
-    new_y_max = max(0, min(y_max + shift_y, 1000))
-    return [new_x_min, new_y_min, new_x_max, new_y_max]
-
-def augment_sample(sample):
-    shift_x = random.randint(-MAX_SHIFT, MAX_SHIFT)
-    shift_y = random.randint(-MAX_SHIFT, MAX_SHIFT)
-    new_sample = sample.copy()
-    new_sample["bboxes"] = [translate_bbox(b, shift_x, shift_y) for b in sample["bboxes"]]
-    return new_sample
-
-def augment_and_save_dataset(input_json_path, output_json_path):
-    with open(input_json_path, 'r', encoding="utf-8") as f:
-        training_data = json.load(f)
-    augmented_data = []
-    for original_sample in training_data:
-        augmented_data.append(original_sample)
-        for _ in range(AUGMENTATION_FACTOR):
-            augmented_data.append(augment_sample(original_sample))
-    with open(output_json_path, 'w', encoding="utf-8") as f:
-        json.dump(augmented_data, f, indent=2, ensure_ascii=False)
-    return output_json_path
-
-# -------------------------
-# Step 2: Dataset Class
-# -------------------------
-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"]
-        encodings = self.tokenizer(words, boxes=bboxes, padding="max_length", truncation=True, max_length=self.max_len, return_tensors="pt")
-        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"])
-            else:
-                label_ids.append(self.label2id.get(labels[word_id], self.label2id["O"]))
-        encodings["labels"] = torch.tensor(label_ids)
-        return {key: val.squeeze(0) for key, val in encodings.items()}
-
-# -------------------------
-# Step 3: Model Architecture (Non-Linear Head)
-# -------------------------
-
-class LayoutLMv3CRF(nn.Module):
-    def __init__(self, num_labels):
-        super().__init__()
-        # Initializing from scratch (Base weights only)
-        print(f"🔄 Initializing backbone from {BASE_MODEL_ID}...")
-        self.layoutlm = LayoutLMv3Model.from_pretrained(BASE_MODEL_ID)
-
-        hidden_size = self.layoutlm.config.hidden_size
-
-        # NON-LINEAR MLP HEAD
-        # Replacing the simple Linear layer with a deeper architecture
-        self.classifier = nn.Sequential(
-            nn.Linear(hidden_size, hidden_size),
-            nn.GELU(),                  # Non-linear activation
-            nn.LayerNorm(hidden_size),   # Stability for training from scratch
-            nn.Dropout(0.1),
-            nn.Linear(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 = outputs.last_hidden_state
-
-        # Pass through the new non-linear head
-        emissions = self.classifier(sequence_output)
-
-        if labels is not None:
-            log_likelihood = self.crf(emissions, labels, mask=attention_mask.bool())
-            return -log_likelihood.mean()
-        else:
-            return self.crf.viterbi_decode(emissions, mask=attention_mask.bool())
-
-# -------------------------
-# Step 4: Training + Evaluation
-# -------------------------
-def train_one_epoch(model, dataloader, optimizer, device):
-    model.train()
-    total_loss = 0
-    for batch in tqdm(dataloader, desc="Training"):
-        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()
-    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_valid = l[valid].tolist()
-                all_labels.extend(l_valid)
-                all_preds.extend(p[:len(l_valid)])
-    precision, recall, f1, _ = precision_recall_fscore_support(all_labels, all_preds, average="micro", zero_division=0)
-    return precision, recall, f1
-
-# -------------------------
-# Step 5: Main Execution
-# -------------------------
-def main(args):
-    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()}
-
-    TEMP_DIR = "temp_intermediate_files"
-    os.makedirs(TEMP_DIR, exist_ok=True)
-
-    # 1. Preprocess & Augment
-    initial_json = os.path.join(TEMP_DIR, "data_bio.json")
-    preprocess_labelstudio(args.input, initial_json)
-    augmented_json = os.path.join(TEMP_DIR, "data_aug.json")
-    final_data_path = augment_and_save_dataset(initial_json, augmented_json)
-
-    # 2. Setup Data
-    tokenizer = LayoutLMv3TokenizerFast.from_pretrained(BASE_MODEL_ID)
-    dataset = LayoutDataset(final_data_path, tokenizer, label2id, max_len=args.max_len)
-    val_size = int(0.2 * len(dataset))
-    train_dataset, val_dataset = random_split(dataset, [len(dataset) - val_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)
-
-    # 3. Model
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model = LayoutLMv3CRF(num_labels=len(labels)).to(device)
-    optimizer = AdamW(model.parameters(), lr=args.lr)
-
-    # 4. Loop
-    for epoch in range(args.epochs):
-        loss = train_one_epoch(model, train_loader, optimizer, device)
-        p, r, f1 = evaluate(model, val_loader, device, id2label)
-        print(f"Epoch {epoch+1} | Loss: {loss:.4f} | F1: {f1:.3f}")
-
-        ckpt_path = "checkpoints/layoutlmv3_nonlinear_scratch.pth"
-        os.makedirs("checkpoints", exist_ok=True)
-        torch.save(model.state_dict(), ckpt_path)
 
+# --- Backend Logic ---
+
+class VectorSystem:
+    def __init__(self):
+        self.vector_store = None
+        # Use a lightweight CPU-friendly model
+        self.embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+
+    def process_pdf(self, file_obj):
+        """Extracts text from PDF and builds the Vector Index"""
+        if file_obj is None:
+            return "No file uploaded."
+
+        try:
+            # 1. Extract Text
+            doc = fitz.open(file_obj.name)
+            text = ""
+            for page in doc:
+                text += page.get_text()
+            
+            # 2. Split Text into Chunks
+            text_splitter = RecursiveCharacterTextSplitter(
+                chunk_size=800,
+                chunk_overlap=150,
+                separators=["\n\n", "\n", ".", " ", ""]
+            )
+            chunks = text_splitter.split_text(text)
+
+            if not chunks:
+                return "Could not extract text. Is the PDF scanned images?"
+
+            # 3. Build Vector Index (FAISS)
+            self.vector_store = FAISS.from_texts(chunks, self.embeddings)
+            
+            return f"✅ Success! Indexed {len(chunks)} text chunks from the PDF."
+        
+        except Exception as e:
+            return f"Error processing PDF: {str(e)}"
+
+    def retrieve_evidence(self, question, student_answer):
+        """Finds relevant text chunks based on the Question"""
+        if not self.vector_store:
+            return "⚠️ Please upload and process a PDF first."
+
+        if not question:
+            return "⚠️ Please enter a Question."
+
+        # We search primarily using the Question to find the 'Ground Truth' in the text.
+        # You could also concatenate (Question + " " + Answer) for a broader search.
+        docs = self.vector_store.similarity_search(question, k=3)
+        
+        # Format the output
+        output_text = "### 🔍 Relevant Context Found:\n\n"
+        for i, doc in enumerate(docs):
+            output_text += f"**Chunk {i+1}:**\n> {doc.page_content}\n\n"
+            
+        output_text += "---\n*These are the most relevant segments to grade the answer against.*"
+        return output_text
+
+# Initialize System
+system = VectorSystem()
+
+# --- Gradio UI ---
+
+with gr.Blocks(title="EduGenius Context Retriever") as demo:
+    gr.Markdown("# 🎓 EduGenius: PDF Context Retriever")
+    gr.Markdown("Upload a chapter, ask a question, and see exactly which part of the text proves the answer right or wrong.")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            # Step 1: Upload
+            pdf_input = gr.File(label="1. Upload PDF Chapter", file_types=[".pdf"])
+            upload_btn = gr.Button("Process PDF", variant="primary")
+            upload_status = gr.Textbox(label="Status", interactve=False)
+
+        with gr.Column(scale=2):
+            # Step 2: Query
+            question_input = gr.Textbox(label="2. Question", placeholder="e.g., What causes the chemical reaction?")
+            answer_input = gr.Textbox(label="Student Answer (Optional Context)", placeholder="e.g., The heat causes it...")
+            search_btn = gr.Button("Find Relevant Evidence", variant="secondary")
+            
+            # Output
+            evidence_output = gr.Markdown(label="Relevant Text Chunks")
+
+    # Event Handlers
+    upload_btn.click(
+        fn=system.process_pdf, 
+        inputs=[pdf_input], 
+        outputs=[upload_status]
+    )
+
+    search_btn.click(
+        fn=system.retrieve_evidence,
+        inputs=[question_input, answer_input],
+        outputs=[evidence_output]
+    )
+
+# Launch
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--mode", type=str, default="train")
-    parser.add_argument("--input", type=str, required=True)
-    parser.add_argument("--batch_size", type=int, default=4)
-    parser.add_argument("--epochs", type=int, default=10) # Increased for scratch training
-    parser.add_argument("--lr", type=float, default=2e-5)
-    parser.add_argument("--max_len", type=int, default=512)
-    args = parser.parse_args()
-    main(args)
+    demo.launch()