ai-station/bck/app-bck212.py

import os
import re
import uuid
import shutil
from datetime import datetime
from typing import Optional, Dict, List
import chainlit as cl
import ollama
import fitz  # PyMuPDF
from qdrant_client import AsyncQdrantClient
from qdrant_client.models import PointStruct, Distance, VectorParams
from chainlit.data.sql_alchemy import SQLAlchemyDataLayer

# === FIX IMPORT ROBUSTO ===
# Gestisce le differenze tra le versioni di Chainlit 2.x
try:
    from chainlit.data.storage_clients import BaseStorageClient
except ImportError:
    try:
        from chainlit.data.base import BaseStorageClient
    except ImportError:
        from chainlit.data.storage_clients.base import BaseStorageClient

# === CONFIGURAZIONE ===
DATABASE_URL = os.getenv("DATABASE_URL", "postgresql+asyncpg://ai_user:secure_password_here@postgres:5432/ai_station")
OLLAMA_URL = os.getenv("OLLAMA_URL", "http://192.168.1.243:11434")
QDRANT_URL = os.getenv("QDRANT_URL", "http://qdrant:6333")
WORKSPACES_DIR = "./workspaces"
STORAGE_DIR = "./.files"

os.makedirs(STORAGE_DIR, exist_ok=True)
os.makedirs(WORKSPACES_DIR, exist_ok=True)

# === MAPPING UTENTI E RUOLI ===
USER_PROFILES = {
    "giuseppe@defranceschi.pro": {
        "role": "admin",
        "name": "Giuseppe",
        "workspace": "admin_workspace",
        "rag_collection": "admin_docs",
        "capabilities": ["debug", "system_prompts", "user_management", "all_models"],
        "show_code": True
    },
    "federica.tecchio@gmail.com": {
        "role": "business",
        "name": "Federica",
        "workspace": "business_workspace",
        "rag_collection": "contabilita",
        "capabilities": ["pdf_upload", "basic_chat"],
        "show_code": False
    },
    "giuseppe.defranceschi@gmail.com": {
        "role": "admin",
        "name": "Giuseppe",
        "workspace": "admin_workspace",
        "rag_collection": "admin_docs",
        "capabilities": ["debug", "system_prompts", "user_management", "all_models"],
        "show_code": True
    },
        "riccardob545@gmail.com": {
        "role": "engineering",
        "name": "Riccardo",
        "workspace": "engineering_workspace",
        "rag_collection": "engineering_docs",
        "capabilities": ["code_execution", "data_viz", "advanced_chat"],
        "show_code": True
    },
    "giuliadefranceschi05@gmail.com": {
        "role": "architecture",
        "name": "Giulia",
        "workspace": "architecture_workspace",
        "rag_collection": "architecture_manuals",
        "capabilities": ["visual_chat", "pdf_upload", "image_gen"],
        "show_code": False
    }
}

# === CUSTOM LOCAL STORAGE CLIENT (FIXED) ===# Questa classe ora implementa tutti i metodi astratti richiesti da Chainlit 2.8.3
class LocalStorageClient(BaseStorageClient):
    """Storage locale su filesystem per file/elementi"""
    
    def __init__(self, storage_path: str):
        self.storage_path = storage_path
        os.makedirs(storage_path, exist_ok=True)
    
    async def upload_file(
        self,
        object_key: str,
        data: bytes,
        mime: str = "application/octet-stream",
        overwrite: bool = True,
    ) -> Dict[str, str]:
        file_path = os.path.join(self.storage_path, object_key)
        os.makedirs(os.path.dirname(file_path), exist_ok=True)
        with open(file_path, "wb") as f:
            f.write(data)
        return {"object_key": object_key, "url": f"/files/{object_key}"}

    # Implementazione metodi obbligatori mancanti nella versione precedente
    async def get_read_url(self, object_key: str) -> str:
        return f"/files/{object_key}"

    async def delete_file(self, object_key: str) -> bool:
        file_path = os.path.join(self.storage_path, object_key)
        if os.path.exists(file_path):
            os.remove(file_path)
            return True
        return False

    async def close(self):
        pass

# === DATA LAYER ===
@cl.data_layer
def get_data_layer():
    return SQLAlchemyDataLayer(
        conninfo=DATABASE_URL,
        user_thread_limit=1000,
        storage_provider=LocalStorageClient(storage_path=STORAGE_DIR)
    )

# === OAUTH CALLBACK ===
@cl.oauth_callback
def oauth_callback(
    provider_id: str,
    token: str,
    raw_user_data: Dict[str, str],
    default_user: cl.User,
) -> Optional[cl.User]:
    if provider_id == "google":
        email = raw_user_data.get("email", "").lower()
        
        # Verifica se utente è autorizzato (opzionale: blocca se non in lista)
        # if email not in USER_PROFILES:
        #    return None
        
        # Recupera profilo o usa default Guest
        profile = USER_PROFILES.get(email, get_user_profile("guest"))
        
        default_user.metadata.update({
            "picture": raw_user_data.get("picture", ""),
            "role": profile["role"],
            "workspace": profile["workspace"],
            "rag_collection": profile["rag_collection"],
            "capabilities": profile["capabilities"],
            "show_code": profile["show_code"],
            "display_name": profile["name"]
        })
        return default_user
    return default_user

# === UTILITY FUNCTIONS ===
def get_user_profile(user_email: str) -> Dict:
    return USER_PROFILES.get(user_email.lower(), {
        "role": "guest",
        "name": "Ospite",
        "workspace": "guest_workspace",
        "rag_collection": "documents",
        "capabilities": [],
        "show_code": False
    })

def create_workspace(workspace_name: str) -> str:
    path = os.path.join(WORKSPACES_DIR, workspace_name)
    os.makedirs(path, exist_ok=True)
    return path

def save_code_to_file(code: str, workspace: str) -> str:
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    file_name = f"code_{timestamp}.py"
    file_path = os.path.join(WORKSPACES_DIR, workspace, file_name)
    with open(file_path, "w", encoding="utf-8") as f:
        f.write(code)
    return file_path

def extract_text_from_pdf(pdf_path: str) -> str:
    try:
        doc = fitz.open(pdf_path)
        text = "\n".join([page.get_text() for page in doc])
        doc.close()
        return text
    except Exception:
        return ""

# === QDRANT FUNCTIONS ===
async def get_qdrant_client() -> AsyncQdrantClient:
    return AsyncQdrantClient(url=QDRANT_URL)

async def ensure_collection(collection_name: str):
    client = await get_qdrant_client()
    if not await client.collection_exists(collection_name):
        await client.create_collection(
            collection_name=collection_name,
            vectors_config=VectorParams(size=768, distance=Distance.COSINE)
        )

async def get_embeddings(text: str) -> list:
    client = ollama.Client(host=OLLAMA_URL)
    try:
        response = client.embed(model='nomic-embed-text', input=text[:2000])
        if 'embeddings' in response: return response['embeddings'][0]
        return response.get('embedding', [])
    except: return []

async def index_document(file_name: str, content: str, collection_name: str) -> bool:
    try:
        await ensure_collection(collection_name)
        embedding = await get_embeddings(content)
        if not embedding: return False
        
        qdrant = await get_qdrant_client()
        await qdrant.upsert(
            collection_name=collection_name,
            points=[PointStruct(
                id=str(uuid.uuid4()),
                vector=embedding,
                payload={"file_name": file_name, "content": content[:3000], "indexed_at": datetime.now().isoformat()}
            )]
        )
        return True
    except: return False

async def search_qdrant(query: str, collection: str) -> str:
    try:
        client = await get_qdrant_client()
        if not await client.collection_exists(collection): return ""
        emb = await get_embeddings(query)
        if not emb: return ""
        res = await client.query_points(collection_name=collection, query=emb, limit=3)
        return "\n\n".join([hit.payload['content'] for hit in res.points if hit.payload])
    except: return ""

# === CHAINLIT HANDLERS ===

@cl.on_chat_start
async def on_chat_start():
    user = cl.user_session.get("user")
    
    if not user:
        # Fallback locale se non c'è auth
        user_email = "guest@local"
        profile = get_user_profile(user_email)
    else:
        user_email = user.identifier
        # I metadati sono già popolati dalla callback oauth
        profile = USER_PROFILES.get(user_email, get_user_profile("guest"))

    # Salva in sessione
    cl.user_session.set("email", user_email)
    cl.user_session.set("role", profile["role"])
    cl.user_session.set("workspace", profile["workspace"])
    cl.user_session.set("rag_collection", profile["rag_collection"])
    cl.user_session.set("show_code", profile["show_code"])
    
    create_workspace(profile["workspace"])

    # === SETTINGS WIDGETS ===
    settings_widgets = [
        cl.input_widget.Select(
            id="model",
            label="Modello AI",
            values=["glm-4.6:cloud", "llama3.2", "mistral", "qwen2.5-coder:32b"],
            initial_value="glm-4.6:cloud",
        ),
        cl.input_widget.Slider(
            id="temperature",
            label="Temperatura",
            initial=0.7, min=0, max=2, step=0.1,
        ),
    ]
    if profile["role"] == "admin":
        settings_widgets.append(cl.input_widget.Switch(id="rag_enabled", label="Abilita RAG", initial=True))
    
    await cl.ChatSettings(settings_widgets).send()
    
    await cl.Message(
        content=f"👋 Ciao **{profile['name']}**!\n"
                f"Ruolo: `{profile['role']}` | Workspace: `{profile['workspace']}`\n"
    ).send()

@cl.on_settings_update
async def on_settings_update(settings):
    cl.user_session.set("settings", settings)
    await cl.Message(content="✅ Impostazioni aggiornate").send()

@cl.on_message
async def on_message(message: cl.Message):
    workspace = cl.user_session.get("workspace")
    rag_collection = cl.user_session.get("rag_collection")
    user_role = cl.user_session.get("role")
    show_code = cl.user_session.get("show_code")
    
    settings = cl.user_session.get("settings", {})
    model = settings.get("model", "glm-4.6:cloud")
    temperature = settings.get("temperature", 0.7)
    rag_enabled = settings.get("rag_enabled", True) if user_role == "admin" else True

    # 1. GESTIONE FILE
    if message.elements:
        for element in message.elements:
            dest = os.path.join(WORKSPACES_DIR, workspace, element.name)
            shutil.copy(element.path, dest)
            if element.name.endswith(".pdf"):
                text = extract_text_from_pdf(dest)
                if text:
                    await index_document(element.name, text, rag_collection)
                    await cl.Message(content=f"✅ **{element.name}** indicizzato.").send()

    # 2. RAG
    context = ""
    if rag_enabled:
        context = await search_qdrant(message.content, rag_collection)
    
    system_prompt = "Sei un assistente esperto."
    if context: system_prompt += f"\n\nCONTESTO:\n{context}"
    
    # 3. GENERAZIONE
    client = ollama.AsyncClient(host=OLLAMA_URL)
    msg = cl.Message(content="")
    await msg.send()
    
    stream = await client.chat(
        model=model,
        messages=[{"role": "system", "content": system_prompt}, {"role": "user", "content": message.content}],
        options={"temperature": temperature},
        stream=True
    )
    
    full_resp = ""
    async for chunk in stream:
        token = chunk['message']['content']
        full_resp += token
        await msg.stream_token(token)
    await msg.update()

    # 4. SALVATAGGIO CODICE
    if show_code:
        blocks = re.findall(r"``````", full_resp, re.DOTALL)
        elements = []
        for code in blocks:
            path = save_code_to_file(code.strip(), workspace)
            elements.append(cl.File(name=os.path.basename(path), path=path, display="inline"))
        if elements:
            await cl.Message(content="💾 Codice salvato", elements=elements).send()