_brock_1app.py

# =============================================================================
# DON'T STEAL THE FREE CODE OF DEVS! Use it for free an do not touch credits!
# If you steal this code, in the future you will pay for apps like this!
# A bit of respect goes a long way – all rights reserved under German law.
# Copyright Volkan Kücükbudak https://github.com/volkansah
# Repo URL: https://github.com/AiCodeCraft
# =============================================================================

import streamlit as st
import os
import json
import datetime
import openai
from datetime import timedelta
import logging
from datasets import load_dataset, Dataset, concatenate_datasets

# ------------------ Logging konfigurieren ------------------
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

# ------------------ Initialisierung ------------------
def main():
    logger.info("App-Initialisierung gestartet...")
    
    # ------------------ Hugging Face Token ------------------
    HF_TOKEN_MEMORY = os.getenv("HF_TOKEN_MEMORY", st.secrets.get("HF_TOKEN_MEMORY", ""))
    if not HF_TOKEN_MEMORY:
        st.warning("Hugging Face Token fehlt (für Dataset-Zugriff)")
    
    # ------------------ Streamlit UI ------------------
    st.title("AI Customer Support Agent with Memory 🛒")
    st.caption("Chat with an assistant who remembers past interactions")

    # OpenAI Key Eingabe
    openai_api_key = st.text_input("Enter OpenAI API Key", type="password", key="openai_key")
    if not openai_api_key:
        st.warning("⚠️ API-Key benötigt")
        st.stop()

    openai.api_key = openai_api_key

    # ------------------ Dataset Funktionen ------------------
    DATASET_REPO = "AiCodeCarft/customer_memory" 

    @st.cache_resource
    def load_memory_dataset():
        """
        Versucht, das Memory-Dataset vom HF Hub zu laden.
        Falls nicht vorhanden, wird ein leeres Dataset erstellt und gepusht.
        """
        try:
            # Versuche das Dataset zu laden mit Token-Authentifizierung
            logger.info(f"Versuche Dataset {DATASET_REPO} zu laden...")
            ds = load_dataset(DATASET_REPO, split="train", token=HF_TOKEN_MEMORY)
            logger.info(f"Dataset erfolgreich geladen mit {len(ds)} Einträgen.")
            return ds
        except Exception as e:
            logger.warning(f"Fehler beim Laden des Datasets: {str(e)}")
        
            # Erstelle ein leeres Dataset, wenn keines existiert
            logger.info("Erstelle neues Dataset...")
            data = {"user_id": [], "query": [], "response": [], "timestamp": []}
            ds = Dataset.from_dict(data)
        
            try:
                # Versuche das Dataset zu pushen
                ds.push_to_hub(DATASET_REPO, token=HF_TOKEN_MEMORY)
                logger.info("Neues Dataset erfolgreich erstellt und gepusht.")
                return ds
            except Exception as push_error:
                logger.error(f"Fehler beim Pushen des Datasets: {str(push_error)}")
                st.error("Konnte kein Dataset erstellen. Bitte überprüfe deine Berechtigungen.")
                # Fallback: Lokales Dataset zurückgeben
                return ds

    # ------------------ AI Agent Klasse ------------------
    class CustomerSupportAIAgent:
        def __init__(self):
            self.memory = load_memory_dataset()

        def handle_query(self, query, user_id):
            # Memory abrufen
            user_history = self.memory.filter(lambda x: x["user_id"] == user_id)
            
            # Kontext erstellen
            context = "Previous interactions:\n" + "\n".join(
                [f"Q: {h['query']}\nA: {h['response']}" 
                 for h in user_history]
            ) if len(user_history) > 0 else "No previous interactions"
            
            # API-Anfrage
            response = openai.ChatCompletion.create(
                model="gpt-3.5-turbo",
                messages=[
                    {"role": "system", "content": f"You are a support agent. Context:\n{context}"},
                    {"role": "user", "content": query}
                ]
            )
            
            # Antwort verarbeiten
            answer = response.choices[0].message.content
            
            # Memory aktualisieren
            new_entry = Dataset.from_dict({
                "user_id": [user_id],
                "query": [query],
                "response": [answer]
            })
            self.memory = concatenate_datasets([self.memory, new_entry])
            self.memory.push_to_hub(DATASET_REPO, token=HF_TOKEN_MEMORY)
            
            return answer

    # ------------------ App-Logik ------------------
    support_agent = CustomerSupportAIAgent()

    # Customer ID Handling
    customer_id = st.sidebar.text_input("Customer ID", key="cust_id")
    if not customer_id:
        st.sidebar.error("Bitte Customer ID eingeben")
        st.stop()

    # Chat-History
    if "messages" not in st.session_state:
        st.session_state.messages = []

    # Nachrichten anzeigen
    for msg in st.session_state.messages:
        st.chat_message(msg["role"]).write(msg["content"])

    # Eingabe verarbeiten
    if prompt := st.chat_input("Your question"):
        st.session_state.messages.append({"role": "user", "content": prompt})
        st.chat_message("user").write(prompt)
        
        with st.spinner("Denke nach..."):
            response = support_agent.handle_query(prompt, customer_id)
        
        st.session_state.messages.append({"role": "assistant", "content": response})
        st.chat_message("assistant").write(response)

# ------------------ Hauptausführung ------------------
if __name__ == "__main__":
    main()