import chainlit as cl
import pandas as pd
import time
from typing import Dict, Any

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

@cl.on_chat_start
async def on_chat_start():
    """Initialize the chat session."""
    # Initialize BigQuery client
    client = init_bigquery_connection()
    
    # Store the client in the user session
    cl.user_session.set("client", client)
    
    # Send a welcome message
    await cl.Message(
        content="👋 Welcome to the Natural Language to SQL Query Assistant! Ask me any question about your e-commerce data.",
        author="SQL Assistant"
    ).send()
    
    # Add some example questions without using actions
    await cl.Message(
        content="Here are some example questions you can ask:",
        author="SQL Assistant"
    ).send()
    
    examples = [
        "What are the top 5 products by revenue?",
        "How many orders were placed in the last month?",
        "Which customers spent the most in 2023?",
        "What is the average order value by product category?"
    ]
    
    # Display all examples in a single message
    examples_text = "\n\n".join([f"• {example}" for example in examples])
    examples_text += "\n\n(You can copy and paste any of these examples to try them out)"
    
    await cl.Message(
        content=examples_text,
        author="SQL Assistant"
    ).send()

@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