from config import initialize
from utilities.vectorstore.SummaryManager import SummaryManager

def manage_collection(file_name, collection_name):
    
    if not qdrant_manager.get_collection(collection_name):
        if not qdrant_manager.create_collection(collection_name):
            print("❌ Error: Failed to create collection in Qdrant. Exiting application.", flush=True)
            return
        
        success, total_tokens, text = qdrant_manager.insert_document(file_name)
        if success:
            print(f"✅ Documento inserito correttamente. Token totali: {total_tokens}")
            if text:
                print(f"✅ Testo completo disponibile (entro il limite token): {text[:100]}...")
        else:
            print("❌ Errore durante l'inserimento del documento.")

    #qdrant_manager.delete_collection(collection_name)

def get_initial_summary(collection_name):
    """Retrieve initial summary from a Qdrant collection."""
    
    # Carica la collection se esiste
    if not qdrant_manager.get_collection(collection_name):
        print(f"❌ Collection '{collection_name}' non trovata.")
        return None

    # Inizializza il SummaryManager
    summary_manager = SummaryManager(language="en", qdrant_manager=qdrant_manager)

    # Genera il riassunto iniziale
    return summary_manager.do_initial_summary()

def get_summary(collection_name, type="map_reduce"):
    """Retrieve initial summary from a Qdrant collection."""
    
    # Carica la collection se esiste
    if not qdrant_manager.get_collection(collection_name):
        print(f"❌ Collection '{collection_name}' non trovata.")
        return None

    # Inizializza il SummaryManager
    summary_manager = SummaryManager(language="en", qdrant_manager=qdrant_manager)

    if type == "map_reduce":
        return summary_manager.do_summary_map_reduce()
    elif type == "stuff":
        print("Using stuff method")
        return summary_manager.do_summary_stuff()
    else:
        return None

def chat_with_bot(llm_manager, contextualize=True):
    print("🤖 Chatbot! Write 'exit' or 'quit' to close the conversation.\n")

    try:
        if contextualize:
            llm_manager.initialize_conversation()
            print(f"🤖 ELI: {llm_manager.messages[-1].content}\n")
    except Exception as e:
        print(f"⚠️ Could not load initial summary: {e}\n")

    while True:
        try:
            user_input = input("👤 You: ")
            if user_input.lower() in ["exit", "quit"]:
                print("👋 End of conversation.")
                break

            response = llm_manager.send_message(user_input, contextualize=contextualize)
            print(f"🤖 ELI: {response}\n")

        except KeyboardInterrupt:
            print("\n👋 Conversation stopped.")
            break

        except Exception as e:
            print(f"⚠️ Error: {e}\n")

def get_chunk(collection_name, chunk_id):
    """Retrieve a specific chunk from a Qdrant collection."""
    
    # Carica la collection se esiste
    if not qdrant_manager.get_collection(collection_name):
        print(f"❌ Collection '{collection_name}' non trovata.")
        return None

    return qdrant_manager.get_chunk_by_index(chunk_id)

llm_manager, qdrant_manager = initialize()

# file_name="data/txt/Key statisitcs startups.txt"
collection_name="key_statistics"

llm_manager, qdrant_manager = initialize()
# if qdrant_manager.get_collection(collection_name):
#     llm_manager.set_qdrant_manager(qdrant_manager)

#chat_with_bot(llm_manager)

#manage_collection(file_name, collection_name)
#summary=get_initial_summary(collection_name)
#summary=get_summary(collection_name,"map_reduce")
#summary=get_summary(collection_name,type="stuff")

# if summary:
#     print(f"✅ Summary:\n{summary}")
# else:
#     print("⚠️ Nessun riassunto generato.")

text=get_chunk(collection_name, 1)
print(text)