Update app.py

app.py

@@ -1,123 +0,0 @@
-import os
-import pdfplumber
-import re
-import gradio as gr
-from transformers import pipeline, AutoModelForQuestionAnswering, AutoTokenizer
-
-"""
-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
-    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 result_section1_dict['TOPIC']
-
-def get_first_page_text(file_data):
-    doc = pdfplumber.open(BytesIO(file_data)) 
-    if len(doc.pages):
-        return doc.pages[0].extract_text()
-
-# Define the Gradio interface
-# iface = gr.Interface(fn=process_pdf, 
-iface = gr.Interface(fn=get_first_page_text, 
-                     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.")
-
-iface.launch()