import fitz
import faiss
import numpy as np
import torch
from model_loader import load_model

def extract_text_from_pdf(file_path):
    with fitz.open(file_path) as doc:
        return " ".join(page.get_text() for page in doc)

def process_pdf(pdf_text):
    chunks = [pdf_text[i:i+512] for i in range(0, len(pdf_text), 512)]
    tokenizer, model = load_model()
    embeddings = []
    for chunk in chunks:
        inputs = tokenizer(chunk, return_tensors="pt", padding=True, truncation=True, max_length=512)
        with torch.no_grad():
            outputs = model(**inputs)
        embeddings.append(outputs.last_hidden_state.mean(dim=1).squeeze().numpy())
    embeddings = np.array(embeddings)
    index = faiss.IndexFlatL2(embeddings.shape[1])
    index.add(embeddings.astype('float32'))
    return chunks, index