test.py

from transformers import BertForQuestionAnswering, BertTokenizer
import torch
import PyPDF2

# Load the model & tokenizer
model_loc = "D://code/bert_easy/bert-large-uncased-whole-word-masking-finetuned-squad"
model = BertForQuestionAnswering.from_pretrained(model_loc)
tokenizer = BertTokenizer.from_pretrained(model_loc)

# Extract text from PDF
pdf_path = "C://Users/HP/Downloads/Resumes/Akshat_Thakkar_2022BTCS008.pdf"
text = ""

try:
    pdf_reader = PyPDF2.PdfReader(pdf_path)
    for page in pdf_reader.pages:
        page_text = page.extract_text()
        if page_text:  # Ensure text is extracted
            text += page_text + "\n"
except Exception as e:
    print(f"Error reading PDF: {e}")
    text = ""

# Ensure valid context
if not text.strip():
    print("No valid text extracted from PDF.")
    exit()

# Limit context to 512 tokens
tokens = tokenizer.tokenize(text)
context = tokenizer.convert_tokens_to_string(tokens[:1512])  # Truncate to 512 tokens

# Define question
question = "What is my name?"

# Tokenize input
inputs = tokenizer(question, context, return_tensors="pt", truncation=True, max_length=512)

# Get model predictions
with torch.no_grad():
    outputs = model(**inputs)

# Extract answer
answer_start = torch.argmax(outputs.start_logits)
answer_end = torch.argmax(outputs.end_logits) + 1
answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs["input_ids"][0][answer_start:answer_end]))

print("summary:", answer)
print("Answer:", answer)