Update app.py

app.py

@@ -67,11 +67,13 @@ def initialize_llm_chain(temperature, max_tokens, vector_db):
 def conversation(qa_chain, message, history):
     if qa_chain is None:
         return None, "Der QA-Chain wurde nicht initialisiert!", history
+    if not message.strip():
+        return qa_chain, "Bitte eine Frage eingeben!", 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)
+        sources_text = "\n".join(sources) if sources else "Keine Quellen verfügbar"
         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
@@ -79,8 +81,9 @@ def conversation(qa_chain, message, history):
 # **Gradio-Demo erstellen**
 def demo():
     with gr.Blocks() as demo:
-        vector_db = gr.State()
-        qa_chain = gr.State()
+        vector_db = gr.State()  # Zustand für die Vektordatenbank
+        qa_chain = gr.State()  # Zustand für den QA-Chain
+        chat_history = gr.State([])  # Chatverlauf speichern
 
         gr.HTML("<center><h1>RAG Chatbot mit FAISS und lokalen Modellen</h1></center>")
         with gr.Row():
@@ -93,14 +96,28 @@ def demo():
                 qachain_btn = gr.Button("Initialisiere QA-Chatbot")
 
             with gr.Column():
-                chatbot = gr.Chatbot(type='messages', height=400)
-                msg = gr.Textbox(placeholder="Frage eingeben...")
+                chatbot = gr.Chatbot(label="Chatbot", type='messages', height=400)
+                msg = gr.Textbox(label="Deine Frage:", 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_wrapper, [slider_temperature, slider_max_tokens, vector_db], [qa_chain])
-        submit_btn.click(conversation, [qa_chain, msg, []], [qa_chain, "message", "history"])
+        # **Button-Events definieren**
+        db_btn.click(
+            initialize_database,
+            inputs=[document],  # Eingabe der hochgeladenen Dokumente
+            outputs=[vector_db, db_status]  # Ausgabe: Vektor-Datenbank und Status
+        )
+        
+        qachain_btn.click(
+            initialize_llm_chain_wrapper,
+            inputs=[slider_temperature, slider_max_tokens, vector_db],
+            outputs=[qa_chain, db_status]
+        )
+
+        submit_btn.click(
+            conversation,
+            inputs=[qa_chain, msg, chat_history],  # Chatkette, Nutzerfrage, Chatverlauf
+            outputs=[qa_chain, chatbot, chat_history]  # Antwort der Kette, Chatbot-Ausgabe, neuer Verlauf
+        )
 
     demo.launch(debug=True, enable_queue=True)