Create app.py

app.py

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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.")