import sys
# The pysqlite3 import and sys.modules override has been moved to app.py.
# This file should NOT have its own pysqlite3 import to prevent conflicts.

import requests
import os
import io
import re
import uuid # For generating unique IDs for ChromaDB and conversations
from PIL import Image
import json # For handling JSON string (e.g., Firebase config in local test)
import base64 # For decoding Base64 (e.g., Firebase config in local test)
from datetime import datetime # Import datetime for timestamps
import urllib.parse # For parsing URLs


# Firebase Admin SDK for Firestore
import firebase_admin
from firebase_admin import credentials, firestore

# For text extraction from PDFs (non-OCR)
from pdfminer.high_level import extract_text_to_fp
from pdfminer.layout import LAParams

# For image-based PDFs (OCR)
from pdf2image import convert_from_path
import pytesseract

# For embeddings and vector search
from FlagEmbedding import BGEM3FlagModel
import chromadb

from dotenv import load_dotenv # Import load_dotenv for local execution
# CRITICAL FIX: Load environment variables for local testing
load_dotenv(dotenv_path=os.path.join(os.path.dirname(os.path.dirname(__file__)), '.env.local'))

# Retrieve FIREBASE_CONFIG_BASE64 after loading dotenv (for local testing only)
# This value is read from config.py, which in turn reads from .env.local

# Import configurations and prompt from local modules
from config import (
    DEEPSEEK_API_URL, DEEPSEEK_HEADERS,
    EMBEDDING_MODEL_NAME, EMBEDDING_MODEL_USE_FP16,
    CHROMADB_PERSIST_DIRECTORY, CHROMADB_COLLECTION_NAME,
    CHUNK_SIZE, CHUNK_OVERLAP,
    LLM_TEMPERATURE, LLM_MAX_TOKENS, LLM_HISTORY_MAX_TOKENS,
    FIREBASE_CONFIG_BASE64
)
from pdf_processing import extract_text_from_pdf, chunk_text
from prompt import SYSTEM_PROMPT # <--- CORRECTLY IMPORTING SYSTEM_PROMPT

# --- Global Firebase Firestore Client ---
# This global is primarily for __main__ (local testing) execution.
# In production (via app.py), the Firestore instance will be passed directly to DocumentRAG's __init__.
FIRESTORE_DATABASE = None 

def initialize_firebase_client():
    """
    Initializes Firebase Admin SDK and returns the Firestore client.
    This function is called by app.py and also by __main__ for local testing.
    """
    global FIRESTORE_DATABASE # This global is modified for local testing context.
    
    if not firebase_admin._apps: # Check if Firebase Admin SDK is already initialized
        # Determine Firebase config. In deployment, it comes from env vars.
        # For local __main__ testing, it also uses env vars.

        if FIREBASE_CONFIG_BASE64:
            try:
                # Decode the Base64-encoded Firebase Service Account JSON
                cred_json = base64.b64decode(FIREBASE_CONFIG_BASE64).decode('utf-8')
                cred_dict = json.loads(cred_json)
                cred = credentials.Certificate(cred_dict)
                firebase_admin.initialize_app(cred)
                print("Firebase Admin SDK initialized successfully.")
                firestore_instance = firestore.client()
                FIRESTORE_DATABASE = firestore_instance # Set the global for local testing context
                print("Firestore client initialized successfully.")
                return firestore_instance # Return the instance for app.py to capture
            except Exception as e:
                print(f"Error initializing Firebase Admin SDK: {e}")
                print("Please ensure FIREBASE_CONFIG_BASE64 is correctly set and is a valid Base64-encoded Service Account JSON.")
                FIRESTORE_DATABASE = None
                return None
        else:
            print("Warning: FIREBASE_CONFIG_BASE64 environment variable not found. Firestore will not be available.")
            FIRESTORE_DATABASE = None
            return None
    else: # Already initialized (e.g., by app.py's first call)
        print("Firebase Admin SDK already initialized.")
        # Ensure global variable is set if already initialized, for local testing context.
        # This branch ensures the global FIRESTORE_DATABASE is available even if `app.py` already init'd it.
        if FIRESTORE_DATABASE is None: 
            FIRESTORE_DATABASE = firestore.client()
        return firestore.client() # Always return the current Firestore client instance

# --- Embedding Model Initialization ---
print("Loading FlagEmbedding (BGE-M3) model...")
try:
    embedding_model = BGEM3FlagModel(EMBEDDING_MODEL_NAME, use_fp16=EMBEDDING_MODEL_USE_FP16)
    print("FlagEmbedding (BGE-M3) model loaded successfully.")
except Exception as e:
    print(f"Error loading FlagEmbedding model: {e}")
    print("Ensure disk space and memory are sufficient for model download.")
    print("You might need to adjust 'use_fp16' based on your hardware (e.g., False for CPU/older GPUs).")
    sys.exit(1) # Use sys.exit for clean exit in non-FastAPI contexts


class DocumentRAG:
    def __init__(self, embedding_model, persist_directory=CHROMADB_PERSIST_DIRECTORY, collection_name=CHROMADB_COLLECTION_NAME, firestore_db_instance=None):
        self.embedding_model = embedding_model
        self.persist_directory = persist_directory
        self.collection_name = collection_name
        self.chunk_size = CHUNK_SIZE
        self.overlap = CHUNK_OVERLAP 
        self.firestore_db = firestore_db_instance # CRITICAL: Store the injected Firestore instance

        print(f"Initializing ChromaDB at: {self.persist_directory}")
        self.client = chromadb.PersistentClient(path=self.persist_directory)
        
        self.collection = self.client.get_or_create_collection(
            name=self.collection_name,
            metadata={"hnsw:space": "cosine"}
        )
        print(f"ChromaDB collection '{self.collection_name}' ready. Total chunks: {self.collection.count()}")

    def _generate_chunk_id(self, pdf_url: str, chunk_idx: int) -> str:
        """Generates a unique ID for each chunk based on PDF URL and index."""
        import hashlib
        # Extract path without query parameters for hashing
        path_without_query = urllib.parse.urlparse(pdf_url).path
        url_hash = hashlib.sha256(path_without_query.encode()).hexdigest()[:10]
        return f"{url_hash}_{chunk_idx}_{uuid.uuid4().hex}"

    def add_document(self, pdf_url: str, document_name: str = None):
        """
        Adds a PDF document to the RAG system, processing and indexing its content.
        Downloads the PDF from the URL.
        """
        # Determine display name from parsed URL path if not provided
        parsed_url_path = urllib.parse.urlparse(pdf_url).path
        display_name = document_name if document_name else os.path.basename(parsed_url_path)
        print(f"Adding document from URL: {pdf_url} (Display Name: {display_name})")
        
        results = self.collection.get(
            where={"source": pdf_url},
            limit=1
        )
        if results and results['ids']:
            print(f"  Document '{display_name}' (from {pdf_url}) already in ChromaDB. Skipping re-indexing.")
            return

        # CRITICAL FIX: Check if the file is indeed a PDF by examining the path component of the URL
        parsed_url_path = urllib.parse.urlparse(pdf_url).path
        file_extension_check = isinstance(parsed_url_path, str) and parsed_url_path.strip().lower().endswith('.pdf')
        if not file_extension_check:
            print(f"  DEBUG: Skipped document '{display_name}' (URL: {pdf_url}) - Not a PDF (based on file extension in URL path).")
            return

        try:
            response = requests.get(pdf_url, stream=True)
            print(f"  DEBUG: HTTP Status Code for {pdf_url}: {response.status_code}")
            response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)
            
            pdf_data = io.BytesIO(response.content)
            print(f"  DEBUG: BytesIO content length for {pdf_url}: {pdf_data.getbuffer().nbytes} bytes")
            
            if pdf_data.getbuffer().nbytes == 0:
                raise ValueError(f"Downloaded PDF content from {pdf_url} is empty.")

            # Create a temporary file to save the PDF for processing
            temp_pdf_path = f"/tmp/{uuid.uuid4().hex}.pdf"
            os.makedirs(os.path.dirname(temp_pdf_path), exist_ok=True) # Ensure /tmp exists
            
            with open(temp_pdf_path, 'wb') as f:
                f.write(pdf_data.getvalue())
            print(f"  DEBUG: Temporary PDF saved to: {temp_pdf_path}")
            
            extracted_text = extract_text_from_pdf(temp_pdf_path)
            os.remove(temp_pdf_path) # Clean up the temporary file after extraction
        
        except requests.exceptions.RequestException as e:
            print(f"Error downloading PDF from {pdf_url}: {e}")
            return
        except ValueError as e: 
            print(f"Error processing downloaded PDF {pdf_url}: {e}")
            return
        except Exception as e:
            print(f"Error processing downloaded PDF {pdf_url}: {e}")
            return

        if not extracted_text:
            print(f"Warning: No text extracted from {display_name} ({pdf_url}). Skipping.")
            return

        chunks = chunk_text(extracted_text, self.chunk_size, self.overlap)
        if not chunks:
            print(f"Warning: No chunks generated for {display_name} ({pdf_url}). Skipping.")
            return

        documents_to_add = []
        metadatas_to_add = []
        ids_to_add = []

        print(f"  Generating embeddings for {len(chunks)} chunks and preparing for ChromaDB: {display_name}...")
        
        encoded_results = self.embedding_model.encode(
            chunks, 
            batch_size=32,
            return_dense=True, 
            return_sparse=False, 
            return_colbert_vecs=False
        )
        chunk_embeddings = encoded_results["dense_vecs"]

        for i, chunk in enumerate(chunks):
            unique_id = self._generate_chunk_id(pdf_url, i)
            documents_to_add.append(chunk)
            metadatas_to_add.append({"source": pdf_url, "display_name": display_name, "chunk_id": i})
            ids_to_add.append(unique_id)
        
        self.collection.add(
            documents=documents_to_add,
            embeddings=chunk_embeddings.tolist(),
            metadatas=metadatas_to_add,
            ids=ids_to_add
        )
        
        print(f"  {len(documents_to_add)} chunks from '{display_name}' added to ChromaDB.")
        print(f"  Total chunks in collection: {self.collection.count()}")

    def retrieve_context(self, query: str, top_k: int = 3) -> list[dict]: 
        """
        Retrieves top_k most relevant document chunks for a given query from ChromaDB.
        Returns a list of dictionaries, each containing 'text' and 'source' (URL or display name).
        """
        if self.collection.count() == 0:
            print("Error: No documents indexed in ChromaDB. Cannot retrieve context.")
            return []

        print(f"Retrieving context for query: '{query}'")
        
        query_embedding_result = self.embedding_model.encode(
            [query], 
            batch_size=1,
            return_dense=True, 
            return_sparse=False, 
            return_colbert_vecs=False
        )
        query_embedding = query_embedding_result["dense_vecs"].tolist()

        results = self.collection.query(
            query_embeddings=query_embedding,
            n_results=top_k,
            include=['documents', 'distances', 'metadatas']
        )
        
        retrieved_chunks_info = []
        if results and results['documents']:
            for i, doc_text in enumerate(results['documents'][0]):
                source_url = results['metadatas'][0][i].get('source', 'Unknown URL')
                display_name = results['metadatas'][0][i].get('display_name', os.path.basename(urllib.parse.urlparse(source_url).path))
                chunk_id_info = results['metadatas'][0][i].get('chunk_id', 'N/A')
                distance_info = results['distances'][0][i]

                retrieved_chunks_info.append({
                    "text": doc_text,
                    "source_url": source_url,
                    "display_name": display_name
                })
                print(f"  Retrieved chunk {i+1} (distance: {distance_info:.4f}) from '{display_name}' (chunk {chunk_id_info}).")
        else:
            print("  No relevant chunks found in ChromaDB.")
            
        return retrieved_chunks_info

    def get_conversation_history(self, conversation_id: str) -> list[dict]:
        """Loads chat history from Firestore for a given conversation ID."""
        if self.firestore_db is None: # Use self.firestore_db
            print("Firestore not initialized. Cannot load conversation history.")
            return []
        
        doc_ref = self.firestore_db.collection('conversations').document(conversation_id) # Use self.firestore_db
        doc = doc_ref.get()
        if doc.exists:
            # History now expects a 'messages' array, and user ID might be at root
            doc_data = doc.to_dict()
            history = doc_data.get('messages', [])
            user_id_from_db = doc_data.get('userId', 'unknown_user_from_db')
            print(f"Loaded history for {conversation_id} (User: {user_id_from_db}): {len(history)} messages.")
            return history
        print(f"No history found for conversation ID: {conversation_id}")
        return []

    def save_conversation_history(self, conversation_id: str, user_id: str, history: list[dict]):
        """Saves chat history to Firestore for a given conversation ID, including user ID."""
        if self.firestore_db is None: # Use self.firestore_db
            print("Firestore not initialized. Cannot save conversation history.")
            return

        doc_ref = self.firestore_db.collection('conversations').document(conversation_id) # Use self.firestore_db
        # Store user ID at the top level of the document, along with the messages array
        doc_ref.set({'userId': user_id, 'messages': history})
        print(f"Saved history for {conversation_id} (User: {user_id}): {len(history)} messages.")

    def truncate_history(self, messages: list[dict], max_tokens: int = LLM_HISTORY_MAX_TOKENS) -> list[dict]:
        """
        Truncates conversation history to fit within a max_tokens limit for the LLM.
        This is a simplistic truncation and doesn't use a tokenizer for exact token count.
        """
        current_len = sum(len(m['content']) for m in messages)
        while current_len > max_tokens and len(messages) > 1: # Keep at least 1 message
            if messages[0]['role'] == 'system':
                if len(messages) >= 3:
                    removed_user_msg = messages.pop(1)
                    removed_ai_msg = messages.pop(1)
                    current_len -= (len(removed_user_msg['content']) + len(removed_ai_msg['content']))
                else:
                    break
            else:
                removed_user_msg = messages.pop(0)
                removed_ai_msg = messages.pop(0)
                current_len -= (len(removed_user_msg['content']) + len(removed_ai_msg['content']))
        return messages


    def answer_question(self, question: str, conversation_id: str = None, user_id: str = "anonymous_user") -> tuple[str, str]:
        """
        Answers a question by retrieving context, and querying DeepSeek.
        Manages conversational memory.
        Returns a tuple: (answer_text, final_conversation_id_used).
        """
        # >>> MODIFICATION: Ensure conversation_id is always present and return it <<<
        if conversation_id is None:
            conversation_id = str(uuid.uuid4()) # Generate new ID if not provided
            print(f"No conversation_id provided. Generating new one: {conversation_id}")
        # >>> END MODIFICATION <<<

        # Get relevant context from ChromaDB
        context_chunks_info = self.retrieve_context(question) 
        
        context_parts = []
        citation_info = {} # To store unique display names for citation

        for chunk_info in context_chunks_info:
            context_parts.append(chunk_info["text"])
            source_key = chunk_info.get("display_name", chunk_info["source_url"])
            if source_key not in citation_info:
                citation_info[source_key] = True # Using a dict/set for unique sources

        context = "\n\n".join(context_parts)

        context_prompt = ""
        if context:
            context_prompt = f"Using the following context:\n\n{context}\n\n"
        else:
            print("Warning: No relevant context found. Answering based on general knowledge or indicating lack of information.")

        # --- Use the imported SYSTEM_PROMPT from prompt.py ---
        messages = [{"role": "system", "content": SYSTEM_PROMPT}] # Use the imported SYSTEM_PROMPT

        # Use the (possibly newly generated) conversation_id
        history = self.get_conversation_history(conversation_id)
        if history:
            messages.extend(history)

        # Add current user question with timestamp
        messages.append({
            "role": "user",
            "content": f"{context_prompt}Question: {question}",
            "timestamp": datetime.now().isoformat() # Add timestamp
        })

        # Truncate conversation history if it's too long
        messages = self.truncate_history(messages)

        # Call DeepSeek API via OpenRouter
        print("\nSending request to DeepSeek API...")
        data = {
            "model": "deepseek/deepseek-chat:free",
            "messages": messages,
            "temperature": LLM_TEMPERATURE,
            "max_tokens": LLM_MAX_TOKENS,
        }

        # CRITICAL FIX: Use DEEPSEEK_HEADERS imported from config.py
        response = requests.post(DEEPSEEK_API_URL, json=data, headers=DEEPSEEK_HEADERS) 

        if response.status_code == 200:
            ai_response = response.json()
            answer = ai_response['choices'][0]['message']['content']
            print("\nDeepSeek Response:")
            print(answer)

            # Logic to append sources to the answer
            if citation_info:
                # Get unique display names and sort them for consistent output
                unique_sources = sorted(list(citation_info.keys()))
                citations_str = "\n\n**Sources:**\n" + "\n".join([f"- {name}" for name in unique_sources])
                answer += citations_str

            # Save updated history with AI response and timestamp
            messages.append({
                "role": "assistant",
                "content": answer,
                "timestamp": datetime.now().isoformat() # Add timestamp
            })
            self.save_conversation_history(conversation_id, user_id, messages) # Pass user_id to save

            # Return the answer text AND the conversation_id
            return answer, conversation_id
        else:
            error_message = f"Failed to fetch data from DeepSeek API. Status Code: {response.status_code}. Response: {response.text}"
            print(error_message)
            return f"Error: Could not get an answer from the AI. Details: {error_message}", conversation_id # Still return conv_id even on error

# --- Main execution logic for local testing (only runs when script is executed directly) ---
if __name__ == "__main__":

    # For local testing, initialize Firebase and capture the instance
    local_firestore_instance = initialize_firebase_client()

    rag_system = DocumentRAG(
        embedding_model=embedding_model,
        persist_directory=CHROMADB_PERSIST_DIRECTORY,
        collection_name=CHROMADB_COLLECTION_NAME,
        firestore_db_instance=local_firestore_instance # Pass the instance here for local testing
    )

    print("\n--- Indexing Documents ---")
    if local_firestore_instance: # Use local_firestore_instance for checking
        try:
            docs_ref = local_firestore_instance.collection('documents').stream()
            firestore_pdf_infos = []
            documents_processed_count = 0 
            documents_skipped_non_pdf_count = 0 

            for doc in docs_ref:
                documents_processed_count += 1
                doc_data = doc.to_dict()
                print(f"  DEBUG: Processing document ID: {doc.id}, Data: {doc_data}")
                
                if 'fileUrl' in doc_data:
                    pdf_url = doc_data['fileUrl']
                    print(f"  DEBUG: Found 'fileUrl': {pdf_url}")
                    
                    # add_document now handles the PDF check internally, so no need for it here
                    display_name = doc_data.get('name_en', None) 
                    firestore_pdf_infos.append({"url": pdf_url, "name": display_name})
                else:
                    documents_skipped_non_pdf_count += 1
                    print(f"  DEBUG: Document ID: {doc.id} does not contain 'fileUrl'. Document data: {doc.data}")

            if documents_processed_count == 0:
                print("No documents found in Firestore collection 'documents' via stream(). Please check collection name and security rules.")
            elif documents_processed_count > 0 and not firestore_pdf_infos:
                print(f"Found {documents_processed_count} documents in Firestore, but none matched the '.pdf' criteria or had 'fileUrl'.")
            elif documents_skipped_non_pdf_count > 0:
                print(f"Found {documents_processed_count} documents in Firestore. {len(firestore_pdf_infos)} URLs found, {documents_skipped_non_pdf_count} documents skipped (non-URL or non-PDF by add_document).")


            if firestore_pdf_infos:
                for pdf_info in firestore_pdf_infos:
                    # rag_system.add_document will internally check for PDF extension
                    rag_system.add_document(pdf_info['url'], pdf_info['name']) 
            else:
                pass 
                
        except Exception as e:
            print(f"Error fetching documents from Firestore: {e}")
            print("Please ensure your Firestore database is accessible and the service account key is correct.")
    else:
        print("Firestore client not initialized. Cannot fetch documents from Firestore.")
        print("Using local PDF_DOCUMENT_PATHS as a fallback for testing purposes (ensure these files exist).")
        # This import is moved here to avoid circular dependency if config imports rag_system
        from config import PDF_DOCUMENT_PATHS # This path is for local testing only
        for pdf_path in PDF_DOCUMENT_PATHS:
            if os.path.exists(pdf_path):
                rag_system.add_document(pdf_path)
            else:
                print(f"Error: Local PDF file not found at {pdf_path}. Skipping.")


    print("\n--- Chat With CompassIA (Type 'q' to exit) ---")
    current_conversation_id = str(uuid.uuid4()) 
    # For local testing, we'll use a static user ID. In a real app, this would come from authentication.
    current_user_id = "local_test_user_123" 
    print(f"Starting new local conversation with ID: {current_conversation_id} for user: {current_user_id}")

    while True:
        user_question = input("\nHow can I help you? ")
        if user_question.lower() == 'q':
            print("Exiting chat...")
            break
        
        # Pass both conversation ID and user ID to the answer_question method
        answer_text, _ = rag_system.answer_question(user_question, conversation_id=current_conversation_id, user_id=current_user_id)
        # For local testing, we print the answer directly
        print(f"\nAI: {answer_text}")