Update app.py

app.py

@@ -149,75 +149,3 @@ if st.session_state.df is not None:
         verbose=True,
     )
 
-# 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("execute_sql")
-    def execute_sql(sql_query: str) -> str:
-        return QuerySQLDataBaseTool(db=db).invoke(sql_query)
-
-    sql_dev = Agent("SQL Developer", "Optimize SQL queries for data extraction.", llm, tools=[execute_sql])
-    data_analyst = Agent("Data Analyst", "Analyze data and generate insights.", llm)
-    report_writer = Agent("Report Writer", "Summarize analysis into reports.", llm)
-
-    extract_data = Task("Extract data for: {query}.", "Query results.", sql_dev)
-    analyze_data = Task("Analyze extracted data for: {query}.", "Insights.", data_analyst, context=[extract_data])
-    write_report = Task("Generate a report for: {query}.", "Summary report.", report_writer, context=[analyze_data])
-
-    crew = Crew([sql_dev, data_analyst, report_writer], [extract_data, analyze_data, write_report], Process.sequential)
-
-    # Tabs for UI
-    tab1, tab2 = st.tabs(["🔍 Query Specific Insights + Viz", "📊 Full Data Insights"])
-
-    #  Tab 1: Query-Specific Analysis + viz
-    with tab1:
-        query = st.text_area("Enter Query:", placeholder="e.g., 'What is the average salary for senior employees?'")
-        if st.button("Submit Query"):
-            with st.spinner("Processing query..."):
-                result = crew.kickoff(inputs={"query": query})
-                st.subheader("📝 Analysis Report")
-                st.markdown(result)
-
-                # Query-Specific Visualization
-                if "salary" in query.lower():
-                    fig = px.box(st.session_state.df, x="job_title", y="salary_in_usd", title="Salary by Job Title")
-                    st.plotly_chart(fig)
-
-    # Tab 2: Full Insights
-    with tab2:
-        st.subheader("📊 Comprehensive Data Visualizations")
-
-        # Salary by Job Title
-        fig1 = px.box(st.session_state.df, x="job_title", y="salary_in_usd", title="Salary Distribution by Job Title")
-        st.plotly_chart(fig1)
-
-        # Average Salary by Experience Level
-        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)
-
-        # Average Salary by Company Size
-        fig3 = px.bar(st.session_state.df.groupby("company_size")["salary_in_usd"].mean().reset_index(),
-                      x="company_size", y="salary_in_usd", title="Average Salary by Company Size")
-        st.plotly_chart(fig3)
-
-        # Download CSV Report
-        if st.button("📥 Download CSV Summary"):
-            summary = st.session_state.df.groupby("job_title")["salary_in_usd"].mean().reset_index()
-            summary.to_csv("summary.csv", index=False)
-            with open("summary.csv", "rb") as file:
-                st.download_button("Download CSV", file, "summary.csv", "text/csv")
-
-    temp_dir.cleanup()
-else:
-    st.info("Upload a dataset to continue.")
-
-with st.sidebar:
-    # st.markdown("---")
-    st.markdown("### Reference:")
-    st.markdown("[1. SQL Agents w CrewAI & Llama 3 - Plaban Nayak](https://github.com/plaban1981/Agents/blob/main/SQL_Agents_with_CrewAI_and_Llama_3.ipynb)")