app.py

import os 
import streamlit as st
import pandas as pd
import sqlite3
import logging
import ast  # For parsing string representations of lists

from langchain_community.chat_models import ChatOpenAI
from langchain_community.agent_toolkits.sql.base import create_sql_agent
from langchain_community.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
from langchain_community.utilities import SQLDatabase
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

# Initialize logging
logging.basicConfig(level=logging.INFO)

# Initialize conversation history
if 'history' not in st.session_state:
    st.session_state.history = []

# OpenAI API key
openai_api_key = os.getenv("OPENAI_API_KEY")

# Check if the API key is set
if not openai_api_key:
    st.error("OpenAI API key is not set. Please set the OPENAI_API_KEY environment variable.")
    st.stop()

# Step 1: Upload CSV data file (or use default)
st.title("Enhanced Natural Language to SQL Query App")
st.write("Upload a CSV file to get started, or use the default dataset.")

csv_file = st.file_uploader("Upload your CSV file", type=["csv"])
if csv_file is None:
    data = pd.read_csv("default_data.csv")  # Ensure this file exists
    st.write("Using default_data.csv file.")
    table_name = "default_table"
else:
    data = pd.read_csv(csv_file)
    table_name = csv_file.name.split('.')[0]
    st.write(f"Data Preview ({csv_file.name}):")
    st.dataframe(data.head())

# Step 2: Load CSV data into SQLite database
db_file = 'my_database.db'
conn = sqlite3.connect(db_file)
data.to_sql(table_name, conn, index=False, if_exists='replace')
conn.close()

# Create SQLDatabase instance
db = SQLDatabase.from_uri(f"sqlite:///{db_file}", include_tables=[table_name])

# Initialize the LLM
llm = ChatOpenAI(temperature=0, openai_api_key=openai_api_key)

# Initialize the SQL Agent
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
agent_executor = create_sql_agent(
    llm=llm,
    toolkit=toolkit,
    verbose=True,
    agent_executor_kwargs={"return_intermediate_steps": True}
)

# Step 4: Define the callback function
def process_input():
    user_prompt = st.session_state['user_input']

    if user_prompt:
        try:
            # Append user message to history
            st.session_state.history.append({"role": "user", "content": user_prompt})

            # Use the agent to get the response WITHOUT 'return_intermediate_steps'
            with st.spinner("Processing..."):
                response = agent_executor(user_prompt)  # Removed 'return_intermediate_steps' here

            # Extract the final answer and the data from intermediate steps
            final_answer = response['output']
            intermediate_steps = response['intermediate_steps']

            # Initialize an empty list for SQL result
            sql_result = []

            # Find the SQL query result
            for step in intermediate_steps:
                if step[0].tool == 'sql_db_query':
                    # The result is a string representation of a list
                    sql_result = ast.literal_eval(step[1])
                    break

            # Convert the result to a DataFrame for better formatting
            if sql_result:
                df_result = pd.DataFrame(sql_result)
                sql_result_formatted = df_result.to_markdown(index=False)
            else:
                sql_result_formatted = "No results found."

            # Include the data in the final answer
            assistant_response = f"{final_answer}\n\n**Query Result:**\n{sql_result_formatted}"

            # Append the assistant's response to the history
            st.session_state.history.append({"role": "assistant", "content": assistant_response})

            # Generate insights based on the response
            insights_template = """
            You are an expert data analyst. Based on the user's question and the response provided below, generate a concise analysis that includes key data insights and actionable recommendations. Limit the response to a maximum of 150 words.

            User's Question: {question}

            Response:
            {response}

            Concise Analysis:
            """
            insights_prompt = PromptTemplate(template=insights_template, input_variables=['question', 'response'])
            insights_chain = LLMChain(llm=llm, prompt=insights_prompt)

            insights = insights_chain.run({'question': user_prompt, 'response': assistant_response})

            # Append the assistant's insights to the history
            st.session_state.history.append({"role": "assistant", "content": insights})
        except Exception as e:
            logging.error(f"An error occurred: {e}")
            assistant_response = f"Error: {e}"
            st.session_state.history.append({"role": "assistant", "content": assistant_response})

        # Reset user input
        st.session_state['user_input'] = ''

 


# Step 5: Display conversation history
for message in st.session_state.history:
    if message['role'] == 'user':
        st.markdown(f"**User:** {message['content']}")
    elif message['role'] == 'assistant':
        st.markdown(f"**Assistant:** {message['content']}")

# Input field
st.text_input("Enter your message:", key='user_input', on_change=process_input)