import chainlit as cl
import pandas as pd
import time
from typing import Dict, Any
import os
import json
from datetime import datetime
import hashlib
import uuid

from agents.table_selection import table_selection_agent
from agents.data_retrieval import sample_data_retrieval_agent
from agents.sql_generation import sql_generation_agent
from agents.validation import query_validation_and_optimization
from agents.execution import execution_agent
from utils.bigquery_utils import init_bigquery_connection
from utils.feedback_utils import save_feedback_to_bigquery

# File to store chat history
HISTORY_DIR = "chat_history"
os.makedirs(HISTORY_DIR, exist_ok=True)

# Function to get or create a user ID
def get_user_id():
    # Check if user already has an ID
    user_id = cl.user_session.get("user_id")
    
    if not user_id:
        # Generate a new user ID if none exists
        # In a real app, you might use authentication or cookies
        user_id = str(uuid.uuid4())
        cl.user_session.set("user_id", user_id)
        
        # Create a user directory for this user
        user_dir = os.path.join(HISTORY_DIR, user_id)
        os.makedirs(user_dir, exist_ok=True)
    
    return user_id

# Function to save chat history
def save_chat_history(user_id, conversation_id, user_message, assistant_response):
    user_dir = os.path.join(HISTORY_DIR, user_id)
    os.makedirs(user_dir, exist_ok=True)
    
    history_file = os.path.join(user_dir, f"{conversation_id}.json")
    
    # Load existing history if it exists
    if os.path.exists(history_file):
        with open(history_file, 'r') as f:
            history = json.load(f)
    else:
        history = {
            "conversation_id": conversation_id,
            "created_at": datetime.now().isoformat(),
            "messages": []
        }
    
    # Add new messages
    timestamp = datetime.now().isoformat()
    history["messages"].append({
        "timestamp": timestamp,
        "user": user_message,
        "assistant": assistant_response
    })
    
    # Save updated history
    with open(history_file, 'w') as f:
        json.dump(history, f, indent=2)

# Function to load chat history for a user
def load_chat_history(user_id, conversation_id=None):
    user_dir = os.path.join(HISTORY_DIR, user_id)
    
    if not os.path.exists(user_dir):
        return []
    
    if conversation_id:
        # Load specific conversation
        history_file = os.path.join(user_dir, f"{conversation_id}.json")
        if os.path.exists(history_file):
            with open(history_file, 'r') as f:
                return json.load(f)
        return None
    else:
        # Load all conversations (just metadata)
        conversations = []
        for filename in os.listdir(user_dir):
            if filename.endswith('.json'):
                with open(os.path.join(user_dir, filename), 'r') as f:
                    data = json.load(f)
                    conversations.append({
                        "conversation_id": data["conversation_id"],
                        "created_at": data["created_at"],
                        "message_count": len(data["messages"])
                    })
        return sorted(conversations, key=lambda x: x["created_at"], reverse=True)

@cl.on_chat_start
async def start():
    # Get or create user ID
    user_id = get_user_id()
    
    # Create a new conversation ID for this session
    conversation_id = str(uuid.uuid4())
    cl.user_session.set("conversation_id", conversation_id)
    
    # Load previous conversations for this user
    conversations = load_chat_history(user_id)
    
    if conversations:
        # Display a welcome back message
        await cl.Message(
            content=f"Welcome back! You have {len(conversations)} previous conversations.",
        ).send()
        
        # Create a dropdown to select previous conversations
        options = [{"value": conv["conversation_id"], "label": f"Conversation from {conv['created_at'][:10]} ({conv['message_count']} messages)"} for conv in conversations]
        options.insert(0, {"value": "new", "label": "Start a new conversation"})
        
        res = await cl.AskUserMessage(
            content="Would you like to continue a previous conversation or start a new one?",
            timeout=30,
            select={"options": options, "value": "new"}
        ).send()
        
        if res and res.get("value") != "new":
            # User selected a previous conversation
            selected_conv_id = res.get("value")
            cl.user_session.set("conversation_id", selected_conv_id)
            
            # Load the selected conversation
            conversation = load_chat_history(user_id, selected_conv_id)
            if conversation:
                # Display previous messages
                await cl.Message(content="Loading your previous conversation...").send()
                for msg in conversation["messages"]:
                    await cl.Message(content=msg["user"], author="You").send()
                    await cl.Message(content=msg["assistant"]).send()
    else:
        # First time user
        await cl.Message(
            content="Welcome to the E-commerce Analytics Assistant! How can I help you today?",
        ).send()
    
    # Add option to clear history
    await cl.Action(name="clear_history", label="Clear Chat History", description="Erase all previous conversation history").send()

@cl.on_message
async def on_message(message: cl.Message):
    # Get user and conversation IDs
    user_id = cl.user_session.get("user_id")
    conversation_id = cl.user_session.get("conversation_id")
    
    # Process the message and generate a response
    # Replace this with your actual processing logic
    response = f"You said: {message.content}"
    
    # Send the response
    await cl.Message(content=response).send()
    
    # Save to chat history
    save_chat_history(user_id, conversation_id, message.content, response)

@cl.action_callback("clear_history")
async def clear_history_callback(action):
    user_id = cl.user_session.get("user_id")
    conversation_id = cl.user_session.get("conversation_id")
    
    # Clear specific conversation or all conversations
    user_dir = os.path.join(HISTORY_DIR, user_id)
    history_file = os.path.join(user_dir, f"{conversation_id}.json")
    
    if os.path.exists(history_file):
        os.remove(history_file)
        await cl.Message(content="Your current conversation history has been cleared.").send()
    
    # Create a new conversation ID
    new_conversation_id = str(uuid.uuid4())
    cl.user_session.set("conversation_id", new_conversation_id)

@cl.on_message
async def on_message(message: cl.Message):
    """Handle user messages."""
    query = message.content
    
    # Check if we're in "awaiting feedback" mode
    awaiting_feedback = cl.user_session.get("awaiting_feedback", False)
    if awaiting_feedback:
        client = cl.user_session.get("client")
        original_query = cl.user_session.get("original_query")
        generated_sql = cl.user_session.get("generated_sql")
        optimized_sql = cl.user_session.get("optimized_sql")
        
        # Save the detailed feedback
        feedback_details = f"negative: {query}"
        success = save_feedback_to_bigquery(
            client, 
            original_query, 
            generated_sql, 
            optimized_sql, 
            feedback_details
        )
        
        # Reset the awaiting feedback flag
        cl.user_session.set("awaiting_feedback", False)
        
        if success:
            await cl.Message(content="Thanks for your detailed feedback! I've saved it to improve future responses.", author="SQL Assistant").send()
        else:
            await cl.Message(content="Thanks for your feedback! (Note: There was an issue saving it to the database)", author="SQL Assistant").send()
        return
    
    # If not in feedback mode, process as a regular query
    # Get the BigQuery client from the user session
    client = cl.user_session.get("client")
    
    # Store the original query in the user session for feedback
    cl.user_session.set("original_query", query)
    
    # Send a thinking message
    thinking_msg = await cl.Message(content="🤔 Thinking...", author="SQL Assistant").send()
    
    try:
        # Step 1: Analyze relevant tables
        thinking_msg.content = "🔍 Analyzing relevant tables..."
        await thinking_msg.update()
        
        # Initialize the state with the query
        state = {"sql_query": query, "client": client}
        tables_state = table_selection_agent(state)
        relevant_tables = tables_state.get("relevant_tables", [])
        
        # Send the tables analysis with a slight delay for better UX
        await cl.sleep(1)
        if relevant_tables:
            tables_text = "I've identified these relevant tables for your query:\n\n"
            tables_text += "\n".join([f"- `{table}`" for table in relevant_tables])
            await cl.Message(content=tables_text, author="SQL Assistant").send()
        
        # Step 2: Retrieve sample data
        thinking_msg.content = "📊 Retrieving sample data..."
        await thinking_msg.update()
        await cl.sleep(1)
        
        # Update state with relevant tables and get sample data
        state.update(tables_state)
        sample_data_state = sample_data_retrieval_agent(state)
        
        # Step 3: Generate SQL
        thinking_msg.content = "💻 Generating SQL query..."
        await thinking_msg.update()
        await cl.sleep(1)
        
        # Update state with sample data and generate SQL
        state.update(sample_data_state)
        sql_state = sql_generation_agent(state)
        generated_sql = sql_state.get("generated_sql", "No SQL generated")
        
        # Store the generated SQL in the user session
        cl.user_session.set("generated_sql", generated_sql)
        
        # Send the generated SQL
        await cl.Message(
            content=f"Here's the SQL query I generated:\n\n```sql\n{generated_sql}\n```",
            author="SQL Assistant"
        ).send()
        
        # Step 4: Optimize SQL
        thinking_msg.content = "🔧 Optimizing the query..."
        await thinking_msg.update()
        await cl.sleep(1)
        
        # Update state with generated SQL and optimize
        state.update(sql_state)
        optimization_state = query_validation_and_optimization(state)
        optimized_sql = optimization_state.get("optimized_sql", "No optimized SQL")
        
        # Store the optimized SQL in the user session
        cl.user_session.set("optimized_sql", optimized_sql)
        
        # Send the optimized SQL
        await cl.Message(
            content=f"Here's the optimized version of the query:\n\n```sql\n{optimized_sql}\n```",
            author="SQL Assistant"
        ).send()
        
        # Step 5: Execute query
        thinking_msg.content = "⚙️ Executing query..."
        await thinking_msg.update()
        await cl.sleep(1)
        
        # Update state with optimized SQL and execute
        state.update(optimization_state)
        execution_state = execution_agent(state)
        execution_result = execution_state.get("execution_result", {})
        
        # Format and send the results
        if isinstance(execution_result, dict) and "error" in execution_result:
            error_msg = execution_result.get("error", "Unknown error occurred")
            await cl.Message(
                content=f"❌ Error executing query: {error_msg}",
                author="SQL Assistant"
            ).send()
        elif not execution_result:
            await cl.Message(
                content="✅ Query executed successfully but returned no results.",
                author="SQL Assistant"
            ).send()
        else:
            try:
                # Convert results to DataFrame for better display
                if isinstance(execution_result[0], tuple):
                    # Try to get column names from BigQuery schema
                    try:
                        # Get the schema from the query job
                        query_job = client.query(optimized_sql)
                        schema = query_job.result().schema
                        column_names = [field.name for field in schema]
                        
                        # Use these column names for the DataFrame
                        df = pd.DataFrame(execution_result, columns=column_names)
                    except Exception:
                        # Fallback to generic column names
                        columns = [f"Column_{i}" for i in range(len(execution_result[0]))]
                        df = pd.DataFrame(execution_result, columns=columns)
                else:
                    df = pd.DataFrame(execution_result)
                
                # Display the DataFrame as a table
                await cl.Message(
                    content="✅ Query executed successfully! Here are the results:",
                    author="SQL Assistant"
                ).send()
                
                # Send the DataFrame as an element
                elements = [cl.Dataframe(data=df)]
                await cl.Message(content="", elements=elements, author="SQL Assistant").send()
                
                # Also provide a summary of the results with feedback buttons
                num_rows = len(df)
                num_cols = len(df.columns)
                
                # Ask for feedback using AskActionMessage
                res = await cl.AskActionMessage(
                    content=f"The query returned {num_rows} rows and {num_cols} columns.\n\nWas this result helpful?",
                    actions=[
                        cl.Action(name="feedback", payload={"value": "positive"}, label="👍 Good results"),
                        cl.Action(name="feedback", payload={"value": "negative"}, label="👎 Not what I wanted")
                    ],
                ).send()
                
                if res:
                    feedback_value = res.get("payload", {}).get("value")
                    
                    client = cl.user_session.get("client")
                    original_query = cl.user_session.get("original_query")
                    generated_sql = cl.user_session.get("generated_sql")
                    optimized_sql = cl.user_session.get("optimized_sql")
                    
                    if feedback_value == "positive":
                        # Handle positive feedback
                        success = save_feedback_to_bigquery(
                            client, 
                            original_query, 
                            generated_sql, 
                            optimized_sql, 
                            "positive"
                        )
                        
                        if success:
                            await cl.Message(content="Thanks for your positive feedback! I've saved it to improve future responses.", author="SQL Assistant").send()
                        else:
                            await cl.Message(content="Thanks for your feedback! (Note: There was an issue saving it to the database)", author="SQL Assistant").send()
                    
                    elif feedback_value == "negative":
                        # For negative feedback, just ask for text input
                        await cl.Message(content="I'm sorry the results weren't what you expected. Please type your feedback about what was wrong.", author="SQL Assistant").send()
                        
                        # Set flag to indicate we're awaiting detailed feedback
                        cl.user_session.set("awaiting_feedback", True)
                        
                        # Save initial negative feedback
                        save_feedback_to_bigquery(
                            client, 
                            original_query, 
                            generated_sql, 
                            optimized_sql, 
                            "negative"
                        )
                
            except Exception as e:
                await cl.Message(
                    content=f"❌ Error formatting results: {str(e)}",
                    author="SQL Assistant"
                ).send()
        
    except Exception as e:
        # Handle any errors
        thinking_msg.content = f"❌ Error: {str(e)}"
        await thinking_msg.update()
        
        await cl.Message(
            content=f"I encountered an error while processing your query: {str(e)}",
            author="SQL Assistant"
        ).send()

# Callback handlers for actions
@cl.action_callback("feedback")
async def on_feedback_action(action):
    """Handle feedback action."""
    feedback_value = action.payload.get("value")
    
    client = cl.user_session.get("client")
    original_query = cl.user_session.get("original_query")
    generated_sql = cl.user_session.get("generated_sql")
    optimized_sql = cl.user_session.get("optimized_sql")
    
    if feedback_value == "positive":
        # Handle positive feedback
        success = save_feedback_to_bigquery(
            client, 
            original_query, 
            generated_sql, 
            optimized_sql, 
            "positive"
        )
        
        if success:
                await cl.Message(content="Thanks for your positive feedback! I've saved it to improve future responses.", author="SQL Assistant").send()
        else:
                await cl.Message(content="Thanks for your feedback! (Note: There was an issue saving it to the database)", author="SQL Assistant").send()

@cl.action_callback("feedback_bad")
async def on_feedback_bad(action):
    """Handle negative feedback."""
    # Ask for more detailed feedback
    res = await cl.AskUserMessage(
        content="I'm sorry the results weren't what you expected. Could you please provide more details about what was wrong?",
        author="SQL Assistant",
        timeout=300,
        elements=[
            cl.Textarea(
                id="feedback_details",
                label="Your feedback",
                initial_value="",
                rows=3
            )
        ]
    ).send()
    
    feedback_details = "negative"
    if res and "feedback_details" in res:
        feedback_details = f"negative: {res['feedback_details']}"
    
    client = cl.user_session.get("client")
    original_query = cl.user_session.get("original_query")
    generated_sql = cl.user_session.get("generated_sql")
    optimized_sql = cl.user_session.get("optimized_sql")
    
    # Save the feedback to BigQuery
    success = save_feedback_to_bigquery(
        client, 
        original_query, 
        generated_sql, 
        optimized_sql, 
        feedback_details
    )
    
    if success:
        await cl.Message(content="Thanks for your detailed feedback! I've saved it to improve future responses.", author="SQL Assistant").send()
    else:
        await cl.Message(content="Thanks for your feedback! (Note: There was an issue saving it to the database)", author="SQL Assistant").send()

# This is needed for Chainlit to run properly
if __name__ == "__main__":
    # Note: Chainlit uses its own CLI command to run the app
    # You'll run this with: chainlit run new_app.py -w
    pass