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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() | |