Update app.py

app.py

@@ -26,6 +26,14 @@ def create_db(docs):
     embeddings = HuggingFaceEmbeddings(model_name=EMBEDDINGS_MODEL_NAME)
     return FAISS.from_documents(docs, embeddings)
 
+def initialize_database(list_file_obj):
+    if not list_file_obj or all(x is None for x in list_file_obj):
+        return None, "Fehler: Keine Dateien hochgeladen!"
+    list_file_path = [x.name for x in list_file_obj if x is not None]
+    doc_splits = load_and_split_docs(list_file_path)
+    vector_db = create_db(doc_splits)
+    return vector_db, "Datenbank erfolgreich erstellt!"
+
 def initialize_llm_chain(llm_model, temperature, max_tokens, vector_db):
     local_pipeline = pipeline(
         "text2text-generation",
@@ -43,6 +51,18 @@ def initialize_llm_chain(llm_model, temperature, max_tokens, vector_db):
         return_source_documents=True
     )
 
+def conversation(qa_chain, message, history):
+    if qa_chain is None:
+        return None, "Der QA-Chain wurde nicht initialisiert!", history
+    try:
+        response = qa_chain({"question": message, "chat_history": history})
+        response_text = response["answer"]
+        sources = [doc.metadata["source"] for doc in response["source_documents"]]
+        sources_text = "\n".join(sources)
+        return qa_chain, f"{response_text}\n\n**Quellen:**\n{sources_text}", history + [(message, response_text)]
+    except Exception as e:
+        return qa_chain, f"Fehler: {str(e)}", history
+
 def demo():
     with gr.Blocks() as demo:
         vector_db = gr.State()
@@ -64,7 +84,7 @@ def demo():
                 submit_btn = gr.Button("Absenden")
 
         db_btn.click(initialize_database, [document], [vector_db, db_status])
-        qachain_btn.click(initialize_llm_chain_wrapper, [slider_temperature, slider_max_tokens, vector_db], [qa_chain])
+        qachain_btn.click(initialize_llm_chain, [LLM_MODEL_NAME, slider_temperature, slider_max_tokens, vector_db], [qa_chain])
         submit_btn.click(conversation, [qa_chain, msg, []], [qa_chain, "message", "history"])
 
     demo.launch(debug=True, enable_queue=True)