MaxGit32 commited on
Commit
883c8fa
1 Parent(s): 1f3f1ca

Update pages/llm.py

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Files changed (1) hide show
  1. pages/llm.py +7 -7
pages/llm.py CHANGED
@@ -7,7 +7,7 @@ import os
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  from PyPDF2 import PdfReader
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  from transformers import pipeline
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  from transformers import AutoModel
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- from googletrans import Translator
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  #from transformers import *
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@@ -22,7 +22,7 @@ from googletrans import Translator
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  # PDF in String umwandeln
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  def get_pdf_text(folder_path):
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- translator = Translator()
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  text = ""
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  # Durchsuche alle Dateien im angegebenen Verzeichnis
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  for filename in os.listdir(folder_path):
@@ -36,7 +36,7 @@ def get_pdf_text(folder_path):
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  #text += '\n'
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  text=text.replace("\n", " ")
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  text=text.replace("- ", "")
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- text = translator.translate(text, dest ='en').text
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  st.text(text)
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  return text
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@@ -83,8 +83,8 @@ def get_llm_answer(user_question):
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  #user_question = st.text_area("Stell mir eine Frage: ")
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  #if os.path.exists("./Store"): #Nutzereingabe nur eingelesen, wenn vectorstore angelegt
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  # Retriever sucht passende Textausschnitte in den PDFs (unformatiert)
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- translator = Translator()
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- translator.translate(user_question, dest='en')
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  retriever=get_vectorstore().as_retriever()
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  retrieved_docs=retriever.invoke(
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  user_question
@@ -100,8 +100,8 @@ def get_llm_answer(user_question):
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  # Frage beantworten mit Q&A Pipeline
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  answer = qa_pipeline(question=user_question, context=context, max_length=200)
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- antw = translator.translate(answer["answer"],dest='de')
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- return antw
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  def main():
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  st.set_page_config(
 
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  from PyPDF2 import PdfReader
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  from transformers import pipeline
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  from transformers import AutoModel
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+ #from googletrans import Translator
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  #from transformers import *
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  # PDF in String umwandeln
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  def get_pdf_text(folder_path):
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+ #translator = Translator()
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  text = ""
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  # Durchsuche alle Dateien im angegebenen Verzeichnis
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  for filename in os.listdir(folder_path):
 
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  #text += '\n'
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  text=text.replace("\n", " ")
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  text=text.replace("- ", "")
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+ #text = translator.translate(text, dest ='en').text
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  st.text(text)
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  return text
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  #user_question = st.text_area("Stell mir eine Frage: ")
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  #if os.path.exists("./Store"): #Nutzereingabe nur eingelesen, wenn vectorstore angelegt
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  # Retriever sucht passende Textausschnitte in den PDFs (unformatiert)
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+ #translator = Translator()
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+ #translator.translate(user_question, dest='en')
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  retriever=get_vectorstore().as_retriever()
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  retrieved_docs=retriever.invoke(
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  user_question
 
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  # Frage beantworten mit Q&A Pipeline
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  answer = qa_pipeline(question=user_question, context=context, max_length=200)
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+ #antw = translator.translate(answer["answer"],dest='de')
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+ return answer#antw
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  def main():
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  st.set_page_config(