Create almost_blah.py

mylab/almost_blah.py

@@ -0,0 +1,260 @@
+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="Summarize the analysis into a structured report with Introduction, Key Insights, and Analysis. DO NOT include any conclusion or summary.",
+        backstory="Markdown report excluding Conclusion and Summary.",
+        llm=llm,
+    )
+
+    # New Agent for generating ONLY the Conclusion
+    conclusion_writer = Agent(
+        role="Conclusion Specialist",
+        goal="Summarize findings into a clear and concise Conclusion/Summary 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 setup
+    crew = Crew(
+        agents=[sql_dev, data_analyst, report_writer, conclusion_writer],
+        tasks=[extract_data, analyze_data, write_report, 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 Report without Conclusion
+                report_inputs = {"query": query + " Provide a detailed analysis but DO NOT include a Conclusion."}
+                report_result = crew.kickoff(inputs=report_inputs)
+
+                # Step 2: Generate only the Conclusion
+                conclusion_inputs = {"query": query + " Now, provide only the Conclusion for this analysis."}
+                conclusion_result = crew.kickoff(inputs=conclusion_inputs)
+
+                # Directly use the outputs 
+                main_report = report_result if report_result else "⚠️ No Report Generated."
+                conclusion = conclusion_result if conclusion_result else "⚠️ No Conclusion Generated."
+
+                st.markdown("### Analysis Report:")
+                st.markdown(main_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: Insert Visual Insights
+                st.markdown("## 📊 Visual Insights")
+                for fig in visualizations:
+                    st.plotly_chart(fig, use_container_width=True)
+
+                # Step 5: Append the Conclusion
+                st.markdown("## Conclusion")
+                st.markdown(conclusion)
+
+    # 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)")
+