Update pages/llm.py
Browse files- pages/llm.py +7 -7
pages/llm.py
CHANGED
@@ -7,7 +7,7 @@ import os
|
|
7 |
from PyPDF2 import PdfReader
|
8 |
from transformers import pipeline
|
9 |
from transformers import AutoModel
|
10 |
-
from googletrans import Translator
|
11 |
#from transformers import *
|
12 |
|
13 |
|
@@ -22,7 +22,7 @@ from googletrans import Translator
|
|
22 |
|
23 |
# PDF in String umwandeln
|
24 |
def get_pdf_text(folder_path):
|
25 |
-
translator = Translator()
|
26 |
text = ""
|
27 |
# Durchsuche alle Dateien im angegebenen Verzeichnis
|
28 |
for filename in os.listdir(folder_path):
|
@@ -36,7 +36,7 @@ def get_pdf_text(folder_path):
|
|
36 |
#text += '\n'
|
37 |
text=text.replace("\n", " ")
|
38 |
text=text.replace("- ", "")
|
39 |
-
text = translator.translate(text, dest ='en').text
|
40 |
st.text(text)
|
41 |
return text
|
42 |
|
@@ -83,8 +83,8 @@ def get_llm_answer(user_question):
|
|
83 |
#user_question = st.text_area("Stell mir eine Frage: ")
|
84 |
#if os.path.exists("./Store"): #Nutzereingabe nur eingelesen, wenn vectorstore angelegt
|
85 |
# Retriever sucht passende Textausschnitte in den PDFs (unformatiert)
|
86 |
-
translator = Translator()
|
87 |
-
translator.translate(user_question, dest='en')
|
88 |
retriever=get_vectorstore().as_retriever()
|
89 |
retrieved_docs=retriever.invoke(
|
90 |
user_question
|
@@ -100,8 +100,8 @@ def get_llm_answer(user_question):
|
|
100 |
|
101 |
# Frage beantworten mit Q&A Pipeline
|
102 |
answer = qa_pipeline(question=user_question, context=context, max_length=200)
|
103 |
-
antw = translator.translate(answer["answer"],dest='de')
|
104 |
-
return antw
|
105 |
|
106 |
def main():
|
107 |
st.set_page_config(
|
|
|
7 |
from PyPDF2 import PdfReader
|
8 |
from transformers import pipeline
|
9 |
from transformers import AutoModel
|
10 |
+
#from googletrans import Translator
|
11 |
#from transformers import *
|
12 |
|
13 |
|
|
|
22 |
|
23 |
# PDF in String umwandeln
|
24 |
def get_pdf_text(folder_path):
|
25 |
+
#translator = Translator()
|
26 |
text = ""
|
27 |
# Durchsuche alle Dateien im angegebenen Verzeichnis
|
28 |
for filename in os.listdir(folder_path):
|
|
|
36 |
#text += '\n'
|
37 |
text=text.replace("\n", " ")
|
38 |
text=text.replace("- ", "")
|
39 |
+
#text = translator.translate(text, dest ='en').text
|
40 |
st.text(text)
|
41 |
return text
|
42 |
|
|
|
83 |
#user_question = st.text_area("Stell mir eine Frage: ")
|
84 |
#if os.path.exists("./Store"): #Nutzereingabe nur eingelesen, wenn vectorstore angelegt
|
85 |
# Retriever sucht passende Textausschnitte in den PDFs (unformatiert)
|
86 |
+
#translator = Translator()
|
87 |
+
#translator.translate(user_question, dest='en')
|
88 |
retriever=get_vectorstore().as_retriever()
|
89 |
retrieved_docs=retriever.invoke(
|
90 |
user_question
|
|
|
100 |
|
101 |
# Frage beantworten mit Q&A Pipeline
|
102 |
answer = qa_pipeline(question=user_question, context=context, max_length=200)
|
103 |
+
#antw = translator.translate(answer["answer"],dest='de')
|
104 |
+
return answer#antw
|
105 |
|
106 |
def main():
|
107 |
st.set_page_config(
|