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import streamlit as st
import os
import json
import datetime
import openai
from datasets import load_dataset, Dataset, concatenate_datasets
from huggingface_hub import login
# -- Einstellungen für Hugging Face Dataset Repository --
# Ersetze "your_username/customer_memory" durch deinen eigenen Repository-Namen!
DATASET_REPO = "your_username/customer_memory"
# Hugging Face Login
hf_token = st.sidebar.text_input("Enter your Hugging Face Token", type="password")
if hf_token:
login(token=hf_token)
st.sidebar.success("Logged in to Hugging Face!")
# Hilfsfunktion: Versuche, das Dataset vom HF Hub zu laden; falls nicht vorhanden, initialisiere es
def load_memory_dataset():
try:
ds = load_dataset(DATASET_REPO, split="train")
st.write("Dataset loaded from HF Hub.")
except Exception as e:
st.write("Dataset not found on HF Hub. Creating a new one...")
# Leeres Dataset mit den Spalten: user_id, query, response
data = {"user_id": [], "query": [], "response": []}
ds = Dataset.from_dict(data)
ds.push_to_hub(DATASET_REPO)
st.write("New dataset created and pushed to HF Hub.")
return ds
# Hilfsfunktion: Füge einen neuen Eintrag (Memory) hinzu und pushe das aktualisierte Dataset
def add_to_memory(user_id, query, response):
ds = load_memory_dataset()
# Neuer Eintrag als kleines Dataset
new_entry = Dataset.from_dict({
"user_id": [user_id],
"query": [query],
"response": [response]
})
# Bestehendes Dataset mit dem neuen Eintrag zusammenführen
updated_ds = concatenate_datasets([ds, new_entry])
# Aktualisiere das Dataset auf HF Hub
updated_ds.push_to_hub(DATASET_REPO)
st.write("Memory updated.")
# Hilfsfunktion: Filtere das Dataset nach einer bestimmten customer_id
def get_memory(user_id):
ds = load_memory_dataset()
return ds.filter(lambda x: x["user_id"] == user_id)
# OpenAI GPT-4 API-Anbindung
def generate_response(prompt):
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are a customer support AI for TechGadgets.com."},
{"role": "user", "content": prompt}
]
)
return response.choices[0].message.content
# Streamlit App UI
st.title("AI Customer Support Agent with Memory 🛒")
st.caption("Chat with a customer support assistant who remembers your past interactions.")
# OpenAI API Key Eingabe
openai_api_key = st.text_input("Enter OpenAI API Key", type="password")
if openai_api_key:
os.environ["OPENAI_API_KEY"] = openai_api_key
openai.api_key = openai_api_key
# Sidebar: Customer ID und Optionen
st.sidebar.title("Enter your Customer ID:")
customer_id = st.sidebar.text_input("Customer ID")
# Optional: Synthetic Data generieren (Beispiel-Daten)
if st.sidebar.button("Generate Synthetic Data"):
if customer_id:
synthetic_data = {
"name": "Max Mustermann",
"recent_order": {
"product": "High-end Smartphone",
"order_date": (datetime.datetime.now() - datetime.timedelta(days=10)).strftime("%B %d, %Y"),
"delivery_date": (datetime.datetime.now() + datetime.timedelta(days=2)).strftime("%B %d, %Y"),
"order_number": "ORD123456"
},
"previous_orders": [
{"product": "Laptop", "order_date": "January 12, 2025"},
{"product": "Tablet", "order_date": "March 01, 2025"}
],
"customer_service_interactions": [
"Asked about order status",
"Inquired about warranty"
]
}
st.session_state.customer_data = synthetic_data
st.sidebar.success("Synthetic data generated!")
else:
st.sidebar.error("Please enter a customer ID first.")
if st.sidebar.button("View Customer Profile"):
if "customer_data" in st.session_state and st.session_state.customer_data:
st.sidebar.json(st.session_state.customer_data)
else:
st.sidebar.info("No synthetic data available.")
if st.sidebar.button("View Memory Info"):
if customer_id:
memories = get_memory(customer_id)
st.sidebar.write(f"Memory for customer **{customer_id}**:")
for mem in memories:
st.sidebar.write(f"**Query:** {mem['query']}\n**Response:** {mem['response']}\n---")
else:
st.sidebar.error("Please enter a customer ID.")
# Initialisiere Chatverlauf in session_state
if "messages" not in st.session_state:
st.session_state.messages = []
# Zeige bisherigen Chatverlauf
for message in st.session_state.messages:
st.chat_message(message["role"]).markdown(message["content"])
# Haupt-Chat: Benutzer-Eingabe
query = st.chat_input("How can I assist you today?")
if query and customer_id:
# Hole bisherigen Memory-Context
memories = get_memory(customer_id)
context = ""
for mem in memories:
context += f"Query: {mem['query']}\nResponse: {mem['response']}\n"
# Kombiniere Kontext mit aktueller Anfrage
full_prompt = context + f"\nCustomer: {query}\nSupport Agent:"
with st.spinner("Generating response..."):
answer = generate_response(full_prompt)
# Aktualisiere den Chatverlauf
st.session_state.messages.append({"role": "user", "content": query})
st.session_state.messages.append({"role": "assistant", "content": answer})
st.chat_message("assistant").markdown(answer)
# Speicher die Interaktion in der Memory (Dataset)
add_to_memory(customer_id, query, answer)
elif query and not customer_id:
st.error("Please enter a customer ID to start the chat.")
else:
st.warning("Please enter your OpenAI API key to use the customer support agent.")
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