mylab/dummy_funcs.py

import streamlit as st
import pandas as pd
import sqlite3
import tempfile
from fpdf import FPDF
import os
import re
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
from difflib import get_close_matches
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())

# Function to create TXT file
def create_text_report_with_viz_temp(report, conclusion, visualizations):
    content = f"### Analysis Report\n\n{report}\n\n### Visualizations\n"

    for i, fig in enumerate(visualizations, start=1):
        fig_title = fig.layout.title.text if fig.layout.title.text else f"Visualization {i}"
        x_axis = fig.layout.xaxis.title.text if fig.layout.xaxis.title.text else "X-axis"
        y_axis = fig.layout.yaxis.title.text if fig.layout.yaxis.title.text else "Y-axis"

        content += f"\n{i}. {fig_title}\n"
        content += f"   - X-axis: {x_axis}\n"
        content += f"   - Y-axis: {y_axis}\n"

        if fig.data:
            trace_types = set(trace.type for trace in fig.data)
            content += f"   - Chart Type(s): {', '.join(trace_types)}\n"
        else:
            content += "   - No data available in this visualization.\n"

    content += f"\n\n\n{conclusion}"

    with tempfile.NamedTemporaryFile(delete=False, suffix=".txt", mode='w', encoding='utf-8') as temp_txt:
        temp_txt.write(content)
        return temp_txt.name


# Function to create PDF with report text and visualizations
def create_pdf_report_with_viz(report, conclusion, visualizations):
    pdf = FPDF()
    pdf.set_auto_page_break(auto=True, margin=15)
    pdf.add_page()
    pdf.set_font("Arial", size=12)

    # Title
    pdf.set_font("Arial", style="B", size=18)
    pdf.cell(0, 10, "📊 Analysis Report", ln=True, align="C")
    pdf.ln(10)

    # Report Content
    pdf.set_font("Arial", style="B", size=14)
    pdf.cell(0, 10, "Analysis", ln=True)
    pdf.set_font("Arial", size=12)
    pdf.multi_cell(0, 10, report)

    pdf.ln(10)
    pdf.set_font("Arial", style="B", size=14)
    pdf.cell(0, 10, "Conclusion", ln=True)
    pdf.set_font("Arial", size=12)
    pdf.multi_cell(0, 10, conclusion)

    # Add Visualizations
    pdf.add_page()
    pdf.set_font("Arial", style="B", size=16)
    pdf.cell(0, 10, "📈 Visualizations", ln=True)
    pdf.ln(5)

    with tempfile.TemporaryDirectory() as temp_dir:
        for i, fig in enumerate(visualizations, start=1):
            fig_title = fig.layout.title.text if fig.layout.title.text else f"Visualization {i}"
            x_axis = fig.layout.xaxis.title.text if fig.layout.xaxis.title.text else "X-axis"
            y_axis = fig.layout.yaxis.title.text if fig.layout.yaxis.title.text else "Y-axis"

            # Save each visualization as a PNG image
            img_path = os.path.join(temp_dir, f"viz_{i}.png")
            fig.write_image(img_path)

            # Insert Title and Description
            pdf.set_font("Arial", style="B", size=14)
            pdf.multi_cell(0, 10, f"{i}. {fig_title}")
            pdf.set_font("Arial", size=12)
            pdf.multi_cell(0, 10, f"X-axis: {x_axis} | Y-axis: {y_axis}")
            pdf.ln(3)

            # Embed Visualization
            pdf.image(img_path, w=170)
            pdf.ln(10)

        # Save PDF
        temp_pdf = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
        pdf.output(temp_pdf.name)

    return temp_pdf

def escape_markdown(text):
    # Ensure text is a string
    text = str(text)
    # Escape Markdown characters: *, _, `, ~
    escape_chars = r"(\*|_|`|~)"
    return re.sub(escape_chars, r"\\\1", text)


# Synonym mapping for flexible query understanding
COLUMN_SYNONYMS = {
    "job_title": ["job title", "job role", "role", "designation", "position", "job responsibility", "occupation"],
    "experience_level": ["experience level", "seniority", "experience", "career stage", "years of experience"],
    "employment_type": ["employment type", "job type", "contract type", "employment status"],
    "salary_in_usd": ["salary", "income", "earnings", "pay", "wage", "compensation"],
    "remote_ratio": ["remote work", "work from home", "remote ratio", "remote", "telecommute"],
    "company_size": ["company size", "organization size", "business size", "firm size"],
    "employee_residence": ["country", "residence", "location", "employee location", "home country"],
    "company_location": ["company location", "office location", "company country", "headquarters", "location", "located", "area"],
}


# Fuzzy matcher for mapping query terms to dataset columns
def fuzzy_match_columns(query):
    query = query.lower()
    all_synonyms = {synonym: col for col, synonyms in COLUMN_SYNONYMS.items() for synonym in synonyms}
    words = query.replace("and", "").replace("vs", "").replace("by", "").split()

    matched_columns = []
    for word in words:
        matches = get_close_matches(word, all_synonyms.keys(), n=1, cutoff=0.6)
        matched_columns.extend([all_synonyms[match] for match in matches])

    return list(dict.fromkeys(matched_columns))

# Ask LLM to suggest relevant columns if fuzzy matching fails
def ask_llm_for_columns(query, llm, df):
    columns = ', '.join(df.columns)
    prompt = f"""
    Analyze this user query and suggest the most relevant dataset columns for visualization.
    Query: "{query}"
    Available Columns: {columns}
    Respond in this JSON format:
    {{
      "x_axis": "column_name",
      "y_axis": "column_name",
      "group_by": "optional_column_name"
    }}
    """

    response = llm.generate(prompt)
    try:
        suggestion = json.loads(response)
        return suggestion
    except json.JSONDecodeError:
        st.error("⚠️ Failed to interpret AI response. Please refine your query.")
        return None

# Add min, max, and average salary annotations to the chart
def add_stats_to_figure(fig, df, y_axis):
    min_salary = df[y_axis].min()
    max_salary = df[y_axis].max()
    avg_salary = df[y_axis].mean()

    fig.add_annotation(
        text=f"Min: ${min_salary:,.2f} | Max: ${max_salary:,.2f} | Avg: ${avg_salary:,.2f}",
        xref="paper", yref="paper",
        x=0.5, y=1.1,
        showarrow=False,
        font=dict(size=12, color="black"),
        bgcolor="rgba(255, 255, 255, 0.7)"
    )
    return fig

# Unified Visualization Generator with Fuzzy Matching and LLM Fallback
def generate_visual_from_query(query, df, llm=None):
    try:
        # Step 1: Attempt Fuzzy Matching
        matched_columns = fuzzy_match_columns(query)

        # Step 2: Fallback to LLM if no columns are matched
        if not matched_columns and llm:
            st.info("🤖 No match found. Asking AI for suggestions...")
            suggestion = ask_llm_for_columns(query, llm, df)
            if suggestion:
                matched_columns = [suggestion.get("x_axis"), suggestion.get("group_by")]

        # Step 3: Process Matched Columns
        if len(matched_columns) >= 2:
            x_axis, group_by = matched_columns[0], matched_columns[1]
        elif len(matched_columns) == 1:
            x_axis, group_by = matched_columns[0], None
        else:
            st.warning("❓ No matching columns found. Try rephrasing your query.")
            return None

        # Step 4: Visualization Generation

        # Distribution Plot
        if "distribution" in query:
            fig = px.box(df, x=x_axis, y="salary_in_usd", color=group_by,
                         title=f"Salary Distribution by {x_axis.replace('_', ' ').title()}"
                               + (f" and {group_by.replace('_', ' ').title()}" if group_by else ""))
            return add_stats_to_figure(fig, df, "salary_in_usd")

        # Average Salary Plot
        elif "average" in query or "mean" in query:
            grouped_df = df.groupby([x_axis] + ([group_by] if group_by else []))["salary_in_usd"].mean().reset_index()
            fig = px.bar(grouped_df, x=x_axis, y="salary_in_usd", color=group_by,
                         title=f"Average Salary by {x_axis.replace('_', ' ').title()}"
                               + (f" and {group_by.replace('_', ' ').title()}" if group_by else ""))
            return add_stats_to_figure(fig, df, "salary_in_usd")

        # Salary Trends Over Time
        elif "trend" in query and "work_year" in df.columns:
            grouped_df = df.groupby(["work_year", x_axis])["salary_in_usd"].mean().reset_index()
            fig = px.line(grouped_df, x="work_year", y="salary_in_usd", color=x_axis,
                          title=f"Salary Trend Over Years by {x_axis.replace('_', ' ').title()}")
            return add_stats_to_figure(fig, df, "salary_in_usd")

        # Remote Work Impact
        elif "remote" in query:
            grouped_df = df.groupby(["remote_ratio"] + ([group_by] if group_by else []))["salary_in_usd"].mean().reset_index()
            fig = px.bar(grouped_df, x="remote_ratio", y="salary_in_usd", color=group_by,
                         title="Remote Work Impact on Salary")
            return add_stats_to_figure(fig, df, "salary_in_usd")

        # No Specific Match
        else:
            st.warning("⚠️ No suitable visualization to display!")
            return None

    except Exception as e:
        st.error(f"Error generating visualization: {e}")
        return None


# 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})

    # Agents for SQL data extraction and analysis
    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],
    )

    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,
    )

    report_writer = Agent(
        role="Technical Report Writer",
        goal="Write a structured report with Introduction and Key Insights. DO NOT include any Conclusion or Summary.",
        backstory="Specializes in detailed analytical reports without conclusions.",
        llm=llm,
    )

    conclusion_writer = Agent(
        role="Conclusion Specialist",
        goal="Summarize findings into a clear and concise 3-5 line Conclusion highlighting only the most important insights.",
        backstory="An expert in crafting impactful and clear conclusions.",
        llm=llm,
    )

    # Define tasks for report and conclusion
    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="Key Insights and Analysis without any Introduction or Conclusion.",
        agent=data_analyst,
        context=[extract_data],
    )

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

    write_conclusion = Task(
        description="Summarize the key findings in 3-5 impactful lines, highlighting the maximum, minimum, and average salaries."
        "Emphasize significant insights on salary distribution and influential compensation trends for strategic decision-making.",
        expected_output="Markdown-formatted Conclusion section with key insights and statistics.",
        agent=conclusion_writer,
        context=[analyze_data],
    )



    # Separate Crews 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 Visualizations
    tab1, tab2 = st.tabs(["🔍 Query Insights + Viz", "📊 Full Data Viz"])

    # 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 analysis report
                report_inputs = {"query": query + " Provide detailed analysis but DO NOT include Conclusion."}
                report_result = crew_report.kickoff(inputs=report_inputs)

                # Step 2: Generate only the concise conclusion
                conclusion_inputs = {"query": query + " Provide ONLY the most important insights in 3-5 concise lines."}
                conclusion_result = crew_conclusion.kickoff(inputs=conclusion_inputs)

                # Step 3: Display the report
                #st.markdown("### Analysis Report:")
                st.markdown(report_result if report_result else "⚠️ No Report Generated.")

                # Step 4: Generate 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 5: Insert Visual Insights
                st.markdown("### Visual Insights")
                for fig in visualizations:
                    st.plotly_chart(fig, use_container_width=True)

                # Step 6: Display Concise Conclusion
                #st.markdown("#### Conclusion")

                safe_conclusion = escape_markdown(conclusion_result if conclusion_result else "⚠️ No Conclusion Generated.")
                st.markdown(safe_conclusion)

    # Full Data Visualization Tab
    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)")