app.py

import os
import gradio as gr
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from googleapiclient.discovery import build
from google.oauth2.service_account import Credentials
import pickle
import time

# Initialize embeddings model
embeddings = HuggingFaceEmbeddings(
    model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
)

def load_or_create_vector_store():
    """Load existing vector store or create new one"""
    if os.path.exists("vector_store.pkl"):
        with open("vector_store.pkl", "rb") as f:
            return pickle.load(f)
    
    # Google Sheets setup
    SCOPES = ["https://www.googleapis.com/auth/spreadsheets.readonly"]
    SPREADSHEET_ID = "13mB_WfOQtOxE1bYO1o-f8aYfh-ooRV1fhOASk9M2DwI"
    
    # Load credentials from environment
    gcp_credentials = os.getenv("GCP_CREDENTIALS")
    if not gcp_credentials:
        return None
        
    with open("gcp_credentials.json", "w") as f:
        f.write(gcp_credentials)
        
    creds = Credentials.from_service_account_file(
        "gcp_credentials.json", 
        scopes=SCOPES
    )
    service = build("sheets", "v4", credentials=creds)
    
    # Load all sheets
    sheets = [
        "geburtstagsinfos",
        "oeffnungszeiten",
        "hallenregeln",
        "hinweise",
        "kontakt",
        "attraktionen"
    ]
    
    # Prepare documents for embeddings
    documents = []
    for sheet_name in sheets:
        try:
            result = service.spreadsheets().values().get(
                spreadsheetId=SPREADSHEET_ID,
                range=sheet_name
            ).execute()
            rows = result.get('values', [])[1:]  # Skip header
            
            for row in rows:
                if len(row) >= 2:
                    doc_text = f"Category: {sheet_name}\nQuestion: {row[0]}\nAnswer: {row[1]}"
                    documents.append(doc_text)
        except Exception as e:
            print(f"Error loading {sheet_name}: {e}")
    
    # Create vector store
    vector_store = FAISS.from_texts(documents, embeddings)
    
    # Save for future use
    with open("vector_store.pkl", "wb") as f:
        pickle.dump(vector_store, f)
    
    return vector_store

# Load or create the vector store
vector_store = load_or_create_vector_store()

def format_response(results):
    """Format search results into a nice response"""
    if not results:
        return """Ups! 😅 Dazu habe ich leider keine passende Information gefunden.
                 
Aber ich kenne mich super aus mit:
- Unseren Öffnungszeiten ⏰
- Den Hallenregeln 📋
- Geburtstagsfeiern 🎂
- Unseren tollen Attraktionen 🎯
- Anfahrt & Kontakt 📍

Frag mich einfach danach!"""

    # Kategorien prüfen
    query_text = results[0].page_content.lower()
    
    # Adress-/Kontaktanfragen
    if 'kontakt' in query_text or 'wo finde' in query_text or 'adresse' in query_text:
        for result in results:
            if 'contact' in result.metadata.get('source', ''):
                return f"Cool, dass Du zu uns kommen möchtest! Hier findest Du uns:\n\n{result.page_content}\n\nBis bald! 🚀"
                
    # Geburtstagsfragen
    if 'geburtstag' in query_text or 'feiern' in query_text:
        birthday_info = []
        for result in results:
            if 'geburtstagsinfos' in result.metadata.get('source', '').lower():
                birthday_info.append(result.page_content)
        if birthday_info:
            return "Hier sind alle wichtigen Infos zu Kindergeburtstagen:\n\n• " + "\n\n• ".join(birthday_info)
    
    # Standardantwort mit dem besten Match
    return results[0].page_content

def chat(message, history):
    """Main chat function"""
    try:
        if not message.strip():
            return "", history
            
        if not vector_store:
            return "Entschuldigung, der Service ist gerade nicht verfügbar. Bitte versuche es später noch einmal.", history
            
        # Search for similar content
        results = vector_store.similarity_search(message, k=1)
        response = format_response(results)
        
        history.append({"role": "user", "content": message})
        history.append({"role": "assistant", "content": response})
        
        return "", history
        
    except Exception as e:
        print(f"Error in chat: {e}")
        return "Entschuldigung, es ist ein Fehler aufgetreten. Bitte versuche es später noch einmal.", history

# Gradio Interface
with gr.Blocks(css="""
    .gradio-container {max-width: 800px !important}
    .message-wrap {max-width: 800px !important}
    .message {padding: 15px !important; border-radius: 12px !important; margin: 8px !important}
    .user-message {background-color: #ff6b00 !important; color: white !important; margin-left: 15% !important}
    .bot-message {background-color: #f5f5f5 !important; margin-right: 15% !important}
    .input-row {margin-top: 20px !important}
    footer {display: none !important}
""") as demo:
    
    chatbot = gr.Chatbot(
        value=[],
        elem_id="chatbot",
        height=500,
        avatar_images=("🧑", "🤖"),
        type="messages"
    )
    
    msg = gr.Textbox(
        label="Deine Nachricht",
        placeholder="Was möchtest Du wissen?",
        lines=2
    )
    submit = gr.Button("Senden", variant="primary")

    msg.submit(chat, [msg, chatbot], [msg, chatbot])
    submit.click(chat, [msg, chatbot], [msg, chatbot])
    
    # Example questions
    gr.Examples(
        examples=[
            "Was sind die Öffnungszeiten?",
            "Wie kann ich einen Kindergeburtstag feiern?",
            "Welche Regeln gibt es?",
            "Was kostet ein Kindergeburtstag?",
            "Wo finde ich euch?"
        ],
        inputs=msg
    )

demo.launch()