Update app.py

app.py

@@ -44,7 +44,7 @@ def render_conversation_tab(session_state, agent_graph, update_state_dict):
     # Input for new message
     with st.form(key="user_input_form"):
         user_input = st.text_area("What data pipeline do you need to create?", 
-                        placeholder="e.g., I need a sales performance dashboard showing regional performance by product for the last 2 years")
+                                placeholder="e.g., I need a sales performance dashboard showing regional performance by product for the last 2 years")
         submit_button = st.form_submit_button("Submit")
     
     if submit_button and user_input:
@@ -179,267 +179,4 @@ def render_workflow_tab(session_state):
     executor_active = current_agent == "executor_agent"
     
     # Show the workflow visualization
-    with col1:
-        if understanding_active:
-            st.markdown("### 🔍 **Understanding**")
-        else:
-            st.markdown("### 🔍 Understanding")
-        st.markdown("Extracts user intent and asks clarification questions")
-        
-        if "user_intent" in session_state.conversation and session_state.conversation["user_intent"]:
-            st.success("Completed")
-        elif understanding_active:
-            st.info("In Progress")
-        else:
-            st.warning("Not Started")
-    
-    with col2:
-        if planning_active:
-            st.markdown("### 📋 **Planning**")
-        else:
-            st.markdown("### 📋 Planning")
-        st.markdown("Creates data pipeline plan with sources and transformations")
-        
-        if "pipeline_plan" in session_state.conversation and session_state.conversation["pipeline_plan"]:
-            st.success("Completed")
-        elif planning_active:
-            st.info("In Progress")
-        elif understanding_active:
-            st.warning("Not Started")
-        else:
-            st.success("Completed")
-    
-    with col3:
-        if sql_active:
-            st.markdown("### 💻 **SQL Generation**")
-        else:
-            st.markdown("### 💻 SQL Generation")
-        st.markdown("Converts plan into executable SQL queries")
-        
-        if "sql_queries" in session_state.conversation and session_state.conversation["sql_queries"]:
-            st.success("Completed")
-        elif sql_active:
-            st.info("In Progress")
-        elif understanding_active or planning_active:
-            st.warning("Not Started")
-        else:
-            st.success("Completed")
-    
-    with col4:
-        if executor_active:
-            st.markdown("### ⚙️ **Execution**")
-        else:
-            st.markdown("### ⚙️ Execution")
-        st.markdown("Executes queries and reports results")
-        
-        if "execution_results" in session_state.conversation and session_state.conversation["execution_results"]:
-            st.success("Completed")
-        elif executor_active:
-            st.info("In Progress")
-        elif understanding_active or planning_active or sql_active:
-            st.warning("Not Started")
-        else:
-            st.success("Completed")
-    
-    # Overall confidence score
-    if "confidence_scores" in session_state.conversation and "overall" in session_state.conversation["confidence_scores"]:
-        st.markdown("### Overall Pipeline Confidence")
-        score = session_state.conversation["confidence_scores"]["overall"] * 100
-        st.progress(score / 100, text=f"{score:.1f}%")
-    
-        # Workflow decision points
-        if status == "complete":
-            if score > 80:
-                st.success("✅ High confidence - Pipeline can be deployed automatically")
-            else:
-                st.warning("⚠️ Medium confidence - Human review recommended before deployment")
-                
-    # Add human review section for pending approval status
-    if status == "pending_approval":
-        st.markdown("### 👤 Human Review Required")
-        st.info("This pipeline requires human review before deployment")
-        
-        col1, col2 = st.columns(2)
-        with col1:
-            if st.button("✅ Approve Pipeline"):
-                # Update state to approved
-                session_state.conversation["status"] = "approved"
-                # Trigger execution to continue
-                st.rerun()
-        with col2:
-            if st.button("❌ Reject Pipeline"):
-                # Update state to rejected
-                session_state.conversation["status"] = "rejected"
-                st.error("Pipeline rejected. Please provide feedback to refine the pipeline.")
-""")
-
-    # DB Explorer UI
-    db_explorer_py = "ui/db_explorer.py"
-    if not os.path.exists(db_explorer_py):
-        with open(db_explorer_py, "w") as f:
-            f.write("""
-import streamlit as st
-import pandas as pd
-
-def render_db_explorer_tab(session_state):
-    \"\"\"Render the database explorer tab in the UI.\"\"\"
-    st.subheader("Database Explorer")
-    
-    # Get tables by category
-    tables = session_state.db.get_tables()
-    
-    # Display tables by category
-    col1, col2 = st.columns(2)
-    
-    with col1:
-        st.markdown("### Raw Data Tables")
-        for table in tables["raw_tables"]:
-            with st.expander(table):
-                sample = session_state.db.get_table_sample(table, 3)
-                st.dataframe(pd.DataFrame(sample))
-        
-        st.markdown("### Staging Tables")
-        for table in tables["staging_tables"]:
-            with st.expander(table):
-                sample = session_state.db.get_table_sample(table, 3)
-                st.dataframe(pd.DataFrame(sample))
-    
-    with col2:
-        st.markdown("### Analytics Ready Data")
-        for table in tables["ard_tables"]:
-            with st.expander(table):
-                sample = session_state.db.get_table_sample(table, 3)
-                st.dataframe(pd.DataFrame(sample))
-        
-        st.markdown("### Data Products")
-        for table in tables["data_products"]:
-            with st.expander(table):
-                sample = session_state.db.get_table_sample(table, 3)
-                st.dataframe(pd.DataFrame(sample))
-    
-    # SQL Query Executor
-    st.markdown("### Query Explorer")
-    with st.form(key="sql_form"):
-        sql_query = st.text_area("Enter SQL Query", height=100, 
-                                placeholder="SELECT * FROM ARD_SALES_PERFORMANCE WHERE region = 'North' LIMIT 5")
-        run_sql = st.form_submit_button("Run Query")
-    
-    if run_sql and sql_query:
-        with st.spinner("Executing query..."):
-            result = session_state.db.execute_query(sql_query)
-            
-            if "error" in result:
-                st.error(f"Error executing query: {result['error']}")
-            elif "data" in result:
-                st.dataframe(pd.DataFrame(result["data"]))
-                st.success(f"Query returned {len(result['data'])} rows")
-            elif "tables" in result:
-                st.write(result["tables"])
-            elif "schema" in result:
-                st.write(f"Schema for {result['table']}:")
-                st.dataframe(pd.DataFrame(result["schema"]))
-""")
-
-# Now import UI modules after ensuring they exist
-ensure_ui_files_exist()
-
-# Import UI modules
-from ui.conversation import render_conversation_tab
-from ui.pipeline import render_pipeline_tab
-from ui.agent_workflow import render_workflow_tab
-from ui.db_explorer import render_db_explorer_tab
-
-# Import agent state
-from agents.state import AgentState
-
-# Load environment variables from .env file if it exists
-load_dotenv()
-
-def main():
-    """Main application entry point."""
-    # Set page configuration
-    st.set_page_config(page_title="Pharmaceutical Data Agent", page_icon="💊", layout="wide")
-    
-    # Sidebar for configuration
-    st.sidebar.title("Configuration")
-    
-    # API key input (in a real app, use more secure methods)
-    api_key = st.sidebar.text_input(
-        "Anthropic API Key", 
-        value=os.getenv("ANTHROPIC_API_KEY", ""),
-        type="password"
-    )
-    
-    if not api_key:
-        st.warning("Please enter your Anthropic API Key in the sidebar to continue.")
-        return
-    
-    # Set the API key as an environment variable
-    os.environ["ANTHROPIC_API_KEY"] = api_key
-    
-    # Initialize Anthropic client using default settings
-    anthropic_client = Anthropic()  # Uses API key from environment variable
-    
-    # Initialize database with synthetic data
-    if "db" not in st.session_state:
-        st.session_state.db = SyntheticDatabase()
-    
-    # Initialize agent graph
-    if "agent_graph" not in st.session_state or "update_state_dict" not in st.session_state:
-        st.session_state.agent_graph, st.session_state.update_state_dict = create_agent_graph(
-            anthropic_client, 
-            st.session_state.db
-        )
-    
-    # Initialize conversation state
-    if "conversation" not in st.session_state:
-        st.session_state.conversation = {
-            "messages": [],
-            "user_intent": {},
-            "data_context": {},
-            "pipeline_plan": {},
-            "sql_queries": [],
-            "execution_results": {},
-            "confidence_scores": {},
-            "status": "planning",
-            "current_agent": "understanding_agent"
-        }
-    
-    # Initialize agent state for new runs
-    if "agent_state" not in st.session_state:
-    st.session_state.agent_state = AgentState(
-        messages=[],  # Start with an empty list of messages
-        user_intent={},
-        data_context={},
-        pipeline_plan={},
-        sql_queries=[],
-        execution_results={},
-        confidence_scores={},
-        status="planning",
-        current_agent="understanding_agent"
-    )
-    
-    # Main content area
-    st.title("Pharmaceutical Data Management Agent")
-    
-    # Tabs for different views
-    tab1, tab2, tab3, tab4 = st.tabs(["Conversation", "Pipeline Details", "Agent Workflow", "Database Explorer"])
-    
-    with tab1:
-        render_conversation_tab(
-            st.session_state, 
-            st.session_state.agent_graph,
-            st.session_state.update_state_dict
-        )
-    
-    with tab2:
-        render_pipeline_tab(st.session_state)
-    
-    with tab3:
-        render_workflow_tab(st.session_state)
-    
-    with tab4:
-        render_db_explorer_tab(st.session_state)
-
-if __name__ == "__main__":
-    main()
+    with col1: