app.py

# import os
# import pdfplumber
# import re
# import gradio as gr
# from transformers import pipeline, AutoModelForQuestionAnswering, AutoTokenizer
# from io import BytesIO
# import torch

# """
# Extract the text from a section of a PDF file  between 'wanted_section' and 'next_section'.
# Parameters: 
# - path (str): The file path to the PDF file.
# - wanted_section (str): The section to start extracting text from.
# - next_section (str): The section to stop extracting text at.
# Returns:
# - text (str): The extracted text from the specified section range.
# """


# def get_section(path, wanted_section, next_section):
#     print(wanted_section)

#     # Open the PDF file
#     doc = pdfplumber.open(BytesIO(path)) 
#     start_page = []
#     end_page = []

#     # Find the all the pages for the specified sections
#     for page in range(len(doc.pages)):
#         if len(doc.pages[page].search(wanted_section, return_chars=False, case=False)) > 0:
#             start_page.append(page)
#         if len(doc.pages[page].search(next_section, return_chars=False, case=False)) > 0:
#             end_page.append(page)

#     # Extract the text between the start and end page of the wanted section
#     text = []
#     for page_num in range(max(start_page), max(end_page)+1):
#         page = doc.pages[page_num]
#         text.append(page.extract_text())
#     text = " ".join(text)
#     final_text = text.replace("\n", " ")
#     return final_text


# def extract_between(big_string, start_string, end_string):
#     # Use a non-greedy match for content between start_string and end_string
#     pattern = re.escape(start_string) + '(.*?)' + re.escape(end_string)
#     match = re.search(pattern, big_string, re.DOTALL)

#     if match:
#         # Return the content without the start and end strings
#         return match.group(1)
#     else:
#         # Return None if the pattern is not found
#         return None

# def format_section1(section1_text):
#     result_section1_dict = {}

#     result_section1_dict['TOPIC'] = extract_between(section1_text, "Sektor", "EZ-Programm")
#     result_section1_dict['PROGRAM'] = extract_between(section1_text, "Sektor", "EZ-Programm")
#     result_section1_dict['PROJECT DESCRIPTION'] = extract_between(section1_text, "EZ-Programmziel", "Datum der letzten BE")
#     result_section1_dict['PROJECT NAME'] = extract_between(section1_text, "Modul", "Modulziel")
#     result_section1_dict['OBJECTIVE'] = extract_between(section1_text, "Modulziel", "Berichtszeitraum")
#     result_section1_dict['PROGRESS'] = extract_between(section1_text, "Zielerreichung des Moduls", "Massnahme im Zeitplan")
#     result_section1_dict['STATUS'] = extract_between(section1_text, "Massnahme im Zeitplan", "Risikoeinschätzung")
#     result_section1_dict['RECOMMENDATIONS'] = extract_between(section1_text, "Vorschläge zur Modulanpas-", "Voraussichtliche")

#     return result_section1_dict

# def answer_questions(text,language="de"):
#     # Initialize the zero-shot classification pipeline
#     model_name = "deepset/gelectra-large-germanquad"
#     model = AutoModelForQuestionAnswering.from_pretrained(model_name)
#     tokenizer = AutoTokenizer.from_pretrained(model_name)

#     # Initialize the QA pipeline
#     qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer)
#     questions = [
#         "Welches ist das Titel des Moduls?",
#         "Welches ist das Sektor oder das Kernthema?",
#         "Welches ist das Land?",
#         "Zu welchem Program oder EZ-Programm gehort das Projekt?"
#         #"Welche Durchführungsorganisation aus den 4 Varianten 'giz', 'kfw', 'ptb' und 'bgr' implementiert das Projekt?"
#         # "In dem Dokument was steht bei Sektor?",
#         # "In dem Dokument was steht von 'EZ-Programm' bis 'EZ-Programmziel'?",
#         # "In dem Dokument was steht bei EZ-Programmziel?",
#         # "In dem Dokument in dem Abschnitt '1. Kurzbeschreibung' was steht bei Modul?",
#         # "In dem Dokument was steht bei Zielerreichung des Moduls?",
#         # "In dem Dokument in dem Abschnitt '1. Kurzbeschreibung' was steht bei Maßnahme im Zeitplan?",
#         # "In dem Dokument was steht bei Vorschläge zur Modulanpassung?",
#         # "In dem Dokument in dem Abschnitt 'Anlage 1: Wirkungsmatrix des Moduls' was steht unter Laufzeit als erstes Datum?",
#         # "In dem Dokument in dem Abschnitt 'Anlage 1: Wirkungsmatrix des Moduls' was steht unter Laufzeit als zweites Datum?"
#     ]

#     # Iterate over each question and get answers
#     answers_dict = {}

#     for question in questions:
#         result = qa_pipeline(question=question, context=text)
#         # print(f"Question: {question}")
#         # print(f"Answer: {result['answer']}\n")
#         answers_dict[question] = result['answer']
#     return answers_dict


# def process_pdf(path):
#     results_dict = {}
#     results_dict["1. Kurzbeschreibung"] = \
#         get_section(path, "1. Kurzbeschreibung", "2. Einordnung des Moduls")
#     answers = answer_questions(results_dict["1. Kurzbeschreibung"])
#     return answers

# def get_first_page_text(file_data):
#     doc = pdfplumber.open(BytesIO(file_data)) 
#     if len(doc.pages):
#         return doc.pages[0].extract_text()

# if __name__ == "__main__":
    
#     # Define the Gradio interface
#     # iface = gr.Interface(fn=process_pdf, 
#     # demo = gr.Interface(fn=process_pdf, 
#     #                  inputs=gr.File(type="binary", label="Upload PDF"),
#     #                  outputs=gr.Textbox(label="Extracted Text"),
#     #                  title="PDF Text Extractor",
#     #                  description="Upload a PDF file to extract.")
#     # demo.launch()
#      demo = gr.Interface(fn=process_pdf, 
#                      inputs=gr.File(type="pdf"),
#                      outputs="text,
#                      title="PDF Text Extractor",
#                      description="Upload a PDF file to extract.")
#     demo.launch()

import gradio as gr
import pdfplumber
from transformers import pipeline
from io import BytesIO
import re

# Initialize the question-answering pipeline with a specific pre-trained model
qa_pipeline = pipeline("question-answering", model="deepset/gelectra-large-germanquad")

def extract_text_from_pdf(file_obj):
    """Extracts text from a PDF file."""
    text = []
    with pdfplumber.open(file_obj) as pdf:
        for page in pdf.pages:
            page_text = page.extract_text()
            if page_text:  # Make sure there's text on the page
                text.append(page_text)
    return " ".join(text)

def answer_questions(context):
    """Generates answers to predefined questions based on the provided context."""
    questions = [
        "Welches ist das Titel des Moduls?",
        "Welches ist das Sektor oder das Kernthema?",
        "Welches ist das Land?",
        "Zu welchem Program oder EZ-Programm gehört das Projekt?"
    ]
    answers = {q: qa_pipeline(question=q, context=context)['answer'] for q in questions}
    return answers

def process_pdf(file):
    """Process a PDF file to extract text and then use the text to answer questions."""
    # Read the PDF file from Gradio's file input, which is a temporary file path
    with file as file_path:
        text = extract_text_from_pdf(BytesIO(file_path.read()))
        results = answer_questions(text)
        return "\n".join(f"{q}: {a}" for q, a in results.items())

# Define the Gradio interface
iface = gr.Interface(
    fn=process_pdf,
    inputs=gr.inputs.File(type="pdf", label="Upload your PDF file"),
    outputs=gr.outputs.Textbox(label="Extracted Information and Answers"),
    title="PDF Text Extractor and Question Answerer",
    description="Upload a PDF file to extract text and answer predefined questions based on the content."
)

if __name__ == "__main__":
    iface.launch()