Update app.py

app.py

@@ -30,7 +30,6 @@ 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")
@@ -51,9 +50,12 @@ elif model_choice == "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"):
@@ -61,16 +63,25 @@ if input_option == "Use Hugging Face Dataset":
             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!")
-                st.dataframe(st.session_state.df.head())
         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:
-        st.session_state.df = pd.read_csv(uploaded_file)
-        st.success("File uploaded successfully!")
-        st.dataframe(st.session_state.df.head())
+        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:
@@ -100,6 +111,7 @@ if st.session_state.df is not None:
         """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.",
@@ -117,11 +129,19 @@ if st.session_state.df is not None:
 
     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.",
+        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.",
@@ -130,56 +150,116 @@ if st.session_state.df is not None:
 
     analyze_data = Task(
         description="Analyze the extracted data for query: {query}.",
-        expected_output="Analysis text summarizing findings.",
+        expected_output="Key Insights and Analysis without any Introduction or Conclusion.",
         agent=data_analyst,
         context=[extract_data],
     )
 
     write_report = Task(
-        description="Summarize the analysis into an executive report.",
-        expected_output="Markdown report of insights.",
+        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],
     )
 
-    crew = Crew(
+    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,
     )
 
-    # UI: Tabs for Query Results and General Insights
+    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..."):
-                inputs = {"query": query}
-                result = crew.kickoff(inputs=inputs)
-                st.markdown("### Analysis Report:")
-                st.markdown(result)
+                # 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)
 
-                # Query-Specific Visualization
-                if "salary" in query.lower():
-                    fig = px.box(st.session_state.df, x="job_title", y="salary_in_usd", title="Salary Distribution by Job Title")
-                    st.plotly_chart(fig)
+                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")
+                st.markdown(conclusion_result if conclusion_result else "⚠️ No Conclusion Generated.")
+
+    # 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")
+        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)")
+    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)")
+
+