Delete final_module.py

package_used/final_module.py

-from pdf2image import convert_from_path
-from PIL import Image
-import torch
-from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
-import textract
-import os
-
-# Set the device
-DEVICE = "cpu"
-
-def generate(model, processor, img, questions):
-    inputs = processor(images=[img for _ in range(len(questions))],
-                      text=questions, return_tensors="pt").to(DEVICE)
-    predictions = model.generate(**inputs, max_new_tokens=256)
-    return zip(questions, processor.batch_decode(predictions, skip_special_tokens=True))
-
-def convert_pdf_to_image(filename, page_no, image_filename):
-    images = convert_from_path(filename)
-    if page_no < 1 or page_no > len(images):
-        raise ValueError(f"Page {page_no} is out of range for the provided PDF.")
-
-    image = images[page_no - 1]
-    image.save(image_filename)
-    return image
-
-def extract_text_from_image(image):
-    # Use Textract to extract text from the image
-    text = textract.process(image)
-    return text.decode("utf-8")
-
-def process_pdf_to_image_and_text(filename, page_no, questions, rawtext_filename, image_filename):
-    # Load the Pix2Struct model and processor
-    model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-docvqa-large").to(DEVICE)
-    processor = Pix2StructProcessor.from_pretrained("google/pix2struct-docvqa-large")
-
-    image = convert_pdf_to_image(filename, page_no, image_filename)
-    completions = generate(model, processor, image, questions)
-
-    # Print the completions to the terminal
-    answers = []
-    for question, answer in completions:
-        answers.append(answer)
-        print(f"Question: {question}")
-        print(f"Answer: {answer}\n")
-
-    # Extract text from the image using Textract
-    extracted_text = extract_text_from_image(image_filename)
-
-    # Write the extracted text to a text file
-    with open(rawtext_filename, "w") as output_file:
-        output_file.write(extracted_text)
-
-
-    # Write the completions to a text file
-    completions_filename = "output_text.txt"
-    with open(completions_filename, "w") as output_file:
-        for question, answer in zip(questions, answers):
-            output_file.write(f"Question: {question}\nAnswer: {answer}\n\n")
-
-    return completions_filename
-
-
-# filename = "invoice_4.pdf"
-# page_no = 1  # Change to the desired page number
-# questions = ["what is the name of company?",
-#                  "what is the date of issue?",
-#                  "What is the invoice number?",
-#                  "What is the billed to address?",
-#                  "what is company?"]  # Add your questions
-
-# completions_filename = process_pdf_to_image_and_text(filename, page_no, questions, "out.txt", "img.png")
-
-