Delete mylab/visual_insights_section.py

mylab/visual_insights_section.py

@@ -1,252 +0,0 @@
-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})
-
-    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="Summarize the insights into a clear report.",
-        backstory="An expert in summarizing data insights into readable reports.",
-        llm=llm,
-    )
-
-    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="Analysis text summarizing findings.",
-        agent=data_analyst,
-        context=[extract_data],
-    )
-
-    write_report = Task(
-        description="Summarize the analysis into an executive report.",
-        expected_output="Markdown report of insights.",
-        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=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..."):
-                inputs = {"query": query}
-                result = crew.kickoff(inputs=inputs)
-
-                st.markdown("### Analysis Report:")
-
-                # Collect all generated visualizations
-                visualizations = []
-
-                # Salary Visualization
-                if "salary" in query.lower():
-                    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)
-
-                # Experience Level Visualization
-                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)
-
-                # Employment Type Visualization
-                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)
-
-                # Insert Visual Insights before Conclusion
-                insert_section = "## Conclusion"
-                if insert_section in result:
-                    parts = result.split(insert_section)
-                    st.markdown(parts[0])  # Show content before Conclusion
-
-                    # Insert Visual Insights Section BEFORE Conclusion
-                    st.markdown("## 📊 Visual Insights")
-                    for fig in visualizations:
-                        st.plotly_chart(fig, use_container_width=True)
-
-                    # Show the Conclusion after Visual Insights
-                    st.markdown(insert_section + parts[1])
-                else:
-                    st.markdown(result)
-                    st.markdown("## 📊 Visual Insights")
-                    for fig in visualizations:
-                        st.plotly_chart(fig, use_container_width=True)
-
-    # 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)
-
-        if "company_size" in st.session_state.df.columns:
-            fig4 = px.box(st.session_state.df, x="company_size", y="salary_in_usd",
-                          title="Salary Distribution by Company Size")
-            st.plotly_chart(fig4)
-
-        if "region" in st.session_state.df.columns:
-            fig5 = px.box(st.session_state.df, x="region", y="salary_in_usd",
-                          title="Salary Distribution by Region")
-            st.plotly_chart(fig5)
-
-    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)")