Create app.py

app.py

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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()