Update app.py

app.py

@@ -9,9 +9,11 @@ from langchain.memory import ConversationBufferMemory
 from langchain_community.llms import HuggingFacePipeline
 from transformers import pipeline
 
+# Embeddings- und LLM-Modelle
 EMBEDDINGS_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
 LLM_MODEL_NAME = "google/flan-t5-small"
 
+# **Dokumente laden und aufteilen**
 def load_and_split_docs(list_file_path):
     if not list_file_path:
         return [], "Fehler: Keine Dokumente gefunden!"
@@ -22,10 +24,12 @@ def load_and_split_docs(list_file_path):
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=32)
     return text_splitter.split_documents(documents)
 
+# **Vektor-Datenbank mit FAISS erstellen**
 def create_db(docs):
     embeddings = HuggingFaceEmbeddings(model_name=EMBEDDINGS_MODEL_NAME)
     return FAISS.from_documents(docs, embeddings)
 
+# **Datenbank initialisieren**
 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!"
@@ -34,10 +38,18 @@ def initialize_database(list_file_obj):
     vector_db = create_db(doc_splits)
     return vector_db, "Datenbank erfolgreich erstellt!"
 
-def initialize_llm_chain(llm_model, temperature, max_tokens, vector_db):
+# **LLM-Kette initialisieren (Wrapper)**
+def initialize_llm_chain_wrapper(temperature, max_tokens, vector_db):
+    if vector_db is None:
+        return None, "Fehler: Vektordatenbank nicht initialisiert!"
+    qa_chain = initialize_llm_chain(temperature, max_tokens, vector_db)
+    return qa_chain, "QA-Chatbot ist bereit!"
+
+# **LLM-Kette erstellen**
+def initialize_llm_chain(temperature, max_tokens, vector_db):
     local_pipeline = pipeline(
         "text2text-generation",
-        model=llm_model,
+        model=LLM_MODEL_NAME,
         max_length=max_tokens,
         temperature=temperature
     )
@@ -51,6 +63,7 @@ def initialize_llm_chain(llm_model, temperature, max_tokens, vector_db):
         return_source_documents=True
     )
 
+# **Konversation mit QA-Kette führen**
 def conversation(qa_chain, message, history):
     if qa_chain is None:
         return None, "Der QA-Chain wurde nicht initialisiert!", history
@@ -63,6 +76,7 @@ def conversation(qa_chain, message, history):
     except Exception as e:
         return qa_chain, f"Fehler: {str(e)}", history
 
+# **Gradio-Demo erstellen**
 def demo():
     with gr.Blocks() as demo:
         vector_db = gr.State()
@@ -83,8 +97,9 @@ def demo():
                 msg = gr.Textbox(placeholder="Frage eingeben...")
                 submit_btn = gr.Button("Absenden")
 
+        # Button-Events definieren
         db_btn.click(initialize_database, [document], [vector_db, db_status])
-        qachain_btn.click(initialize_llm_chain, [LLM_MODEL_NAME, slider_temperature, slider_max_tokens, vector_db], [qa_chain])
+        qachain_btn.click(initialize_llm_chain_wrapper, [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)