app.py

import streamlit as st
import pandas as pd
import sqlite3
import os
import json
from pathlib import Path
import plotly.express as px
from datetime import datetime, timezone
from crewai import Agent, Crew, Process, Task
from crewai.tools import tool
from langchain_groq import ChatGroq
from langchain_openai import ChatOpenAI
from langchain.schema.output import LLMResult
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

st.title("SQL-RAG Using CrewAI 🚀")
st.write("Analyze datasets using natural language queries powered by SQL and CrewAI.")

# Initialize LLM
llm = None

# Model Selection
model_choice = st.radio("Select LLM", ["GPT-4o", "llama-3.3-70b"], index=0, horizontal=True)

# API Key Validation and LLM Initialization
groq_api_key = os.getenv("GROQ_API_KEY")
openai_api_key = os.getenv("OPENAI_API_KEY")

if model_choice == "llama-3.3-70b":
    if not groq_api_key:
        st.error("Groq API key is missing. Please set the GROQ_API_KEY environment variable.")
        llm = None
    else:
        llm = ChatGroq(groq_api_key=groq_api_key, model="groq/llama-3.3-70b-versatile")
elif model_choice == "GPT-4o":
    if not openai_api_key:
        st.error("OpenAI API key is missing. Please set the OPENAI_API_KEY environment variable.")
        llm = None
    else:
        llm = ChatOpenAI(api_key=openai_api_key, model="gpt-4o")

# Initialize session state for data persistence
if "df" not in st.session_state:
    st.session_state.df = None
if "show_preview" not in st.session_state:
    st.session_state.show_preview = False  

# Dataset Input
input_option = st.radio("Select Dataset Input:", ["Use Hugging Face Dataset", "Upload CSV File"])

if input_option == "Use Hugging Face Dataset":
    dataset_name = st.text_input("Enter Hugging Face Dataset Name:", value="Einstellung/demo-salaries")
    if st.button("Load Dataset"):
        try:
            with st.spinner("Loading dataset..."):
                dataset = load_dataset(dataset_name, split="train")
                st.session_state.df = pd.DataFrame(dataset)
                st.session_state.show_preview = True  # Show preview after loading
                st.success(f"Dataset '{dataset_name}' loaded successfully!")
        except Exception as e:
            st.error(f"Error: {e}")

elif input_option == "Upload CSV File":
    uploaded_file = st.file_uploader("Upload CSV File:", type=["csv"])
    if uploaded_file:
        try:
            st.session_state.df = pd.read_csv(uploaded_file)
            st.session_state.show_preview = True  # Show preview after loading
            st.success("File uploaded successfully!")
        except Exception as e:
            st.error(f"Error loading file: {e}")

# Show Dataset Preview Only After Loading
if st.session_state.df is not None and st.session_state.show_preview:
    st.subheader("📂 Dataset Preview")
    st.dataframe(st.session_state.df.head())

# SQL-RAG Analysis
if st.session_state.df is not None:
    temp_dir = tempfile.TemporaryDirectory()
    db_path = os.path.join(temp_dir.name, "data.db")
    connection = sqlite3.connect(db_path)
    st.session_state.df.to_sql("salaries", connection, if_exists="replace", index=False)
    db = SQLDatabase.from_uri(f"sqlite:///{db_path}")

    @tool("list_tables")
    def list_tables() -> str:
        """List all tables in the database."""
        return ListSQLDatabaseTool(db=db).invoke("")

    @tool("tables_schema")
    def tables_schema(tables: str) -> str:
        """Get the schema and sample rows for the specified tables."""
        return InfoSQLDatabaseTool(db=db).invoke(tables)

    @tool("execute_sql")
    def execute_sql(sql_query: str) -> str:
        """Execute a SQL query against the database and return the results."""
        return QuerySQLDataBaseTool(db=db).invoke(sql_query)

    @tool("check_sql")
    def check_sql(sql_query: str) -> str:
        """Validate the SQL query syntax and structure before execution."""
        return QuerySQLCheckerTool(db=db, llm=llm).invoke({"query": sql_query})

    # Agent for SQL data extraction
    sql_dev = Agent(
        role="Senior Database Developer",
        goal="Extract data using optimized SQL queries.",
        backstory="An expert in writing optimized SQL queries for complex databases.",
        llm=llm,
        tools=[list_tables, tables_schema, execute_sql, check_sql],
    )

    # Agent for data analysis
    data_analyst = Agent(
        role="Senior Data Analyst",
        goal="Analyze the data and produce insights.",
        backstory="A seasoned analyst who identifies trends and patterns in datasets.",
        llm=llm,
    )

    # Agent for generating the main report (without Conclusion)
    report_writer = Agent(
        role="Technical Report Writer",
        goal="Write a clear, structured report containing ONLY Key Insights and Analysis. NO Introduction, Summary, or Conclusion.",
        backstory="An expert in crafting data-driven reports with clear insights.",
        llm=llm,
    )

    # New Agent for generating ONLY the Conclusion
    conclusion_writer = Agent(
        role="Conclusion Specialist",
        goal="Summarize findings into a clear and concise Conclusion section.",
        backstory="An expert in crafting well-structured and insightful conclusions.",
        llm=llm,
    )

    # Tasks for each agent
    extract_data = Task(
        description="Extract data based on the query: {query}.",
        expected_output="Database results matching the query.",
        agent=sql_dev,
    )

    analyze_data = Task(
        description="Analyze the extracted data for query: {query}.",
        expected_output="Provide ONLY Key Insights and Analysis. Exclude Introduction and Conclusion.",
        agent=data_analyst,
        context=[extract_data],
    )

    write_report = Task(
        description="Write the report with ONLY Key Insights and Analysis. DO NOT include Introduction or Conclusion.",
        expected_output="Markdown report excluding Introduction and Conclusion.",
        agent=report_writer,
        context=[analyze_data],
    )

    write_conclusion = Task(
        description="Summarize the findings into a concise Conclusion.",
        expected_output="Markdown-formatted Conclusion section.",
        agent=conclusion_writer,
        context=[analyze_data],
    )

    # Crew with separate tasks for report and conclusion
    crew_report = Crew(
        agents=[sql_dev, data_analyst, report_writer],
        tasks=[extract_data, analyze_data, write_report],
        process=Process.sequential,
        verbose=True,
    )

    crew_conclusion = Crew(
        agents=[data_analyst, conclusion_writer],
        tasks=[write_conclusion],
        process=Process.sequential,
        verbose=True,
    )

    # Tabs for Query Results and General Insights
    tab1, tab2 = st.tabs(["🔍 Query Insights + Viz", "📊 Full Data Viz"])

    # Tab 1: Query-Insights + Visualization
    with tab1:
        query = st.text_area("Enter Query:", value="Provide insights into the salary of a Principal Data Scientist.")
        if st.button("Submit Query"):
            with st.spinner("Processing query..."):
                # Step 1: Generate the main report (without Conclusion)
                report_inputs = {"query": query}
                report_result = crew_report.kickoff(inputs=report_inputs)

                # Step 2: Generate ONLY the Conclusion
                conclusion_inputs = {"query": query}
                conclusion_result = crew_conclusion.kickoff(inputs=conclusion_inputs)

                st.markdown("### Analysis Report:")

                # Step 3: Generate relevant visualizations
                visualizations = []

                fig_salary = px.box(st.session_state.df, x="job_title", y="salary_in_usd",
                                    title="Salary Distribution by Job Title")
                visualizations.append(fig_salary)

                fig_experience = px.bar(
                    st.session_state.df.groupby("experience_level")["salary_in_usd"].mean().reset_index(),
                    x="experience_level", y="salary_in_usd",
                    title="Average Salary by Experience Level"
                )
                visualizations.append(fig_experience)

                fig_employment = px.box(st.session_state.df, x="employment_type", y="salary_in_usd",
                                        title="Salary Distribution by Employment Type")
                visualizations.append(fig_employment)

                # Step 4: Display the main report
                st.markdown(report_result)

                # Step 5: Insert Visual Insights
                st.markdown("## 📊 Visual Insights")
                for fig in visualizations:
                    st.plotly_chart(fig, use_container_width=True)

                # Step 6: Append the Conclusion
                st.markdown("## Conclusion")
                st.markdown(conclusion_result)

    # Tab 2: Full Data Visualization
    with tab2:
        st.subheader("📊 Comprehensive Data Visualizations")

        fig1 = px.histogram(st.session_state.df, x="job_title", title="Job Title Frequency")
        st.plotly_chart(fig1)

        fig2 = px.bar(
            st.session_state.df.groupby("experience_level")["salary_in_usd"].mean().reset_index(),
            x="experience_level", y="salary_in_usd",
            title="Average Salary by Experience Level"
        )
        st.plotly_chart(fig2)

        fig3 = px.box(st.session_state.df, x="employment_type", y="salary_in_usd",
                      title="Salary Distribution by Employment Type")
        st.plotly_chart(fig3)

    temp_dir.cleanup()
else:
    st.info("Please load a dataset to proceed.")


# Sidebar Reference
with st.sidebar:
    st.header("📚 Reference:")
    st.markdown("[SQL Agents w CrewAI & Llama 3 - Plaban Nayak](https://github.com/plaban1981/Agents/blob/main/SQL_Agents_with_CrewAI_and_Llama_3.ipynb)")