import streamlit as st
import pandas as pd
import sqlite3
import os
import json
from pathlib import Path
from datetime import datetime, timezone
from crewai import Agent, Crew, Process, Task
from crewai_tools import tool
from langchain_groq import ChatGroq
from langchain.schema.output import LLMResult
from langchain_core.callbacks.base import BaseCallbackHandler
from langchain_community.tools.sql_database.tool import (
    InfoSQLDatabaseTool,
    ListSQLDatabaseTool,
    QuerySQLCheckerTool,
    QuerySQLDataBaseTool,
)
from langchain_community.utilities.sql_database import SQLDatabase
from datasets import load_dataset
import tempfile

# Setup API key
os.environ["GROQ_API_KEY"] = st.secrets.get("GROQ_API_KEY", "")

# Callback handler for logging
class LLMCallbackHandler(BaseCallbackHandler):
    def __init__(self, log_path: Path):
        self.log_path = log_path

    def on_llm_start(self, serialized, prompts, **kwargs):
        with self.log_path.open("a", encoding="utf-8") as file:
            file.write(json.dumps({"event": "llm_start", "text": prompts[0], "timestamp": datetime.now().isoformat()}) + "\n")

    def on_llm_end(self, response: LLMResult, **kwargs):
        generation = response.generations[-1][-1].message.content
        with self.log_path.open("a", encoding="utf-8") as file:
            file.write(json.dumps({"event": "llm_end", "text": generation, "timestamp": datetime.now().isoformat()}) + "\n")

# LLM Setup
llm = ChatGroq(
    temperature=0,
    model_name="mixtral-8x7b-32768",
    callbacks=[LLMCallbackHandler(Path("prompts.jsonl"))],
)

st.title("SQL-RAG using CrewAI 🚀")
st.write("Analyze and summarize data using natural language queries with SQL-based retrieval.")

# File upload or Hugging Face dataset input
option = st.radio("Choose your input method:", ["Upload a CSV file", "Enter Hugging Face dataset name"])

if option == "Upload a CSV file":
    uploaded_file = st.file_uploader("Upload your dataset (CSV format)", type=["csv"])
    if uploaded_file:
        df = pd.read_csv(uploaded_file)
        st.success("File uploaded successfully!")
else:
    dataset_name = st.text_input("Enter Hugging Face dataset name:", placeholder="e.g., imdb, ag_news")
    if dataset_name:
        try:
            dataset = load_dataset(dataset_name, split="train")
            df = pd.DataFrame(dataset)
            st.success(f"Dataset '{dataset_name}' loaded successfully!")
        except Exception as e:
            st.error(f"Error loading Hugging Face dataset: {e}")
            df = None

if 'df' in locals() and not df.empty:
    st.write("### Dataset Preview:")
    st.dataframe(df.head())

    # Create a temporary SQLite database
    temp_dir = tempfile.TemporaryDirectory()
    db_path = os.path.join(temp_dir.name, "data.db")
    connection = sqlite3.connect(db_path)
    df.to_sql("data_table", connection, if_exists="replace", index=False)
    db = SQLDatabase.from_uri(f"sqlite:///{db_path}")

    # Tools
    @tool("list_tables")
    def list_tables() -> str:
        return ListSQLDatabaseTool(db=db).invoke("")

    @tool("tables_schema")
    def tables_schema(tables: str) -> str:
        return InfoSQLDatabaseTool(db=db).invoke(tables)

    @tool("execute_sql")
    def execute_sql(sql_query: str) -> str:
        return QuerySQLDataBaseTool(db=db).invoke(sql_query)

    @tool("check_sql")
    def check_sql(sql_query: str) -> str:
        return QuerySQLCheckerTool(db=db, llm=llm).invoke({"query": sql_query})

    # Agents
    sql_dev = Agent(
        role="Database Developer",
        goal="Extract data from the database.",
        llm=llm,
        tools=[list_tables, tables_schema, execute_sql, check_sql],
        allow_delegation=False,
    )

    data_analyst = Agent(
        role="Data Analyst",
        goal="Analyze and provide insights.",
        llm=llm,
        allow_delegation=False,
    )

    report_writer = Agent(
        role="Report Editor",
        goal="Summarize the analysis.",
        llm=llm,
        allow_delegation=False,
    )

    # Tasks
    extract_data = Task(
        description="Extract data required for the query: {query}.",
        expected_output="Database result for the query",
        agent=sql_dev,
    )

    analyze_data = Task(
        description="Analyze the data for: {query}.",
        expected_output="Detailed analysis text",
        agent=data_analyst,
        context=[extract_data],
    )

    write_report = Task(
        description="Summarize the analysis into a short report.",
        expected_output="Markdown report",
        agent=report_writer,
        context=[analyze_data],
    )

    crew = Crew(
        agents=[sql_dev, data_analyst, report_writer],
        tasks=[extract_data, analyze_data, write_report],
        process=Process.sequential,
        verbose=2,
        memory=False,
    )

    query = st.text_input("Enter your query:", placeholder="e.g., 'What are the top 5 highest salaries?'")
    if query:
        with st.spinner("Processing your query..."):
            inputs = {"query": query}
            result = crew.kickoff(inputs=inputs)
            st.markdown("### Analysis Report:")
            st.markdown(result)

    temp_dir.cleanup()
else:
    st.warning("Please upload a valid file or provide a correct Hugging Face dataset name.")