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import gradio as gr
from langchain.chains import RetrievalQA
from langchain.vectorstores import Chroma
from langchain.document_loaders import PyPDFLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.schema import Document
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
# OCR-Ersatz: LayoutLMv3 für Textextraktion aus PDFs
from transformers import LayoutLMv3Processor
from pdf2image import convert_from_path
from PIL import Image
import torch
class LayoutLMv3OCR:
def __init__(self):
self.processor = LayoutLMv3Processor.from_pretrained("microsoft/layoutlmv3-base")
self.model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/layoutlmv3-base-finetuned", num_labels=2)
def extract_text(self, pdf_path):
pages = convert_from_path(pdf_path)
extracted_texts = []
for page in pages:
encoding = self.processor(images=page, return_tensors="pt")
outputs = self.model(**encoding)
logits = outputs.logits
predictions = torch.argmax(logits, dim=-1).squeeze()
tokens = self.processor.tokenizer.convert_ids_to_tokens(encoding.input_ids[0])
page_text = " ".join([token for token, pred in zip(tokens, predictions) if pred == 1])
extracted_texts.append(page_text)
return extracted_texts
# Initialisiere OCR
ocr_tool = LayoutLMv3OCR()
# Embeddings und LLM konfigurieren
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
model_name = "google/flan-t5-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
def flan_generate(input_text):
inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
outputs = model.generate(**inputs, max_length=512)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
def process_pdf_and_create_rag(pdf_path):
extracted_text = ocr_tool.extract_text(pdf_path)
documents = []
for page_num, text in enumerate(extracted_text, start=1):
doc = Document(page_content=text, metadata={"page": page_num})
documents.append(doc)
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
split_docs = text_splitter.split_documents(documents)
vector_store = Chroma.from_documents(split_docs, embedding=embeddings)
retriever = vector_store.as_retriever()
qa_chain = RetrievalQA(retriever=retriever, combine_documents_chain=flan_generate)
return qa_chain
def chatbot_response(pdf_file, question):
qa_chain = process_pdf_and_create_rag(pdf_file.name)
response = qa_chain.run(question)
relevant_pages = set()
for doc in qa_chain.retriever.get_relevant_documents(question):
relevant_pages.add(doc.metadata.get("page", "Unbekannt"))
page_info = f" (Referenz: Seite(n) {', '.join(map(str, relevant_pages))})"
return response + page_info
def gradio_interface():
pdf_input = gr.File(label="PDF hochladen")
question_input = gr.Textbox(label="Ihre Frage", placeholder="Geben Sie Ihre Frage hier ein...")
response_output = gr.Textbox(label="Antwort")
interface = gr.Interface(
fn=chatbot_response,
inputs=[pdf_input, question_input],
outputs=response_output,
title="RAG Chatbot (Deutsch)"
)
return interface
if __name__ == "__main__":
interface = gradio_interface()
interface.launch()
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