Delete app/main.py

app/main.py

@@ -1,180 +0,0 @@
-import gradio as gr
-from gradio_client import Client
-from langgraph.graph import StateGraph, START, END
-from typing import TypedDict, Optional
-import io
-from PIL import Image
-import os 
-
-#OPEN QUESTION: SHOULD WE PASS ALL PARAMS FROM THE ORCHESTRATOR TO THE NODES INSTEAD OF SETTING IN EACH MODULE?
-HF_TOKEN = os.environ.get("HF_TOKEN")
-# Define the state schema
-class GraphState(TypedDict):
-    query: str
-    context: str
-    result: str
-    # Add orchestrator-level parameters (addressing your open question)
-    reports_filter: str
-    sources_filter: str
-    subtype_filter: str
-    year_filter: str
-
-# node 2: retriever
-def retrieve_node(state: GraphState) -> GraphState:
-    client = Client("giz/chatfed_retriever", hf_token=HF_TOKEN)  # HF repo name
-    context = client.predict(
-        query=state["query"],
-        reports_filter=state.get("reports_filter", ""),
-        sources_filter=state.get("sources_filter", ""),
-        subtype_filter=state.get("subtype_filter", ""),
-        year_filter=state.get("year_filter", ""),
-        api_name="/retrieve"
-    )
-    return {"context": context}
-
-# node 3: generator
-def generate_node(state: GraphState) -> GraphState:
-    client = Client("giz/chatfed_generator", hf_token=HF_TOKEN)
-    result = client.predict(
-        query=state["query"],
-        context=state["context"],
-        api_name="/generate"
-    )
-    return {"result": result}
-
-# build the graph
-workflow = StateGraph(GraphState)
-
-# Add nodes
-workflow.add_node("retrieve", retrieve_node)
-workflow.add_node("generate", generate_node)
-
-# Add edges
-workflow.add_edge(START, "retrieve")
-workflow.add_edge("retrieve", "generate")
-workflow.add_edge("generate", END)
-
-# Compile the graph
-graph = workflow.compile()
-
-# Single tool for processing queries
-def process_query(
-    query: str,
-    reports_filter: str = "",
-    sources_filter: str = "",
-    subtype_filter: str = "",
-    year_filter: str = ""
-) -> str:
-    """
-    Execute the ChatFed orchestration pipeline to process a user query.
-    
-    This function orchestrates a two-step workflow:
-    1. Retrieve relevant context using the ChatFed retriever service with optional filters
-    2. Generate a response using the ChatFed generator service with the retrieved context
-    
-    Args:
-        query (str): The user's input query/question to be processed
-        reports_filter (str, optional): Filter for specific report types. Defaults to "".
-        sources_filter (str, optional): Filter for specific data sources. Defaults to "".
-        subtype_filter (str, optional): Filter for document subtypes. Defaults to "".
-        year_filter (str, optional): Filter for specific years. Defaults to "".
-        
-    Returns:
-        str: The generated response from the ChatFed generator service
-    """
-    initial_state = {
-        "query": query, 
-        "context": "", 
-        "result": "",
-        "reports_filter": reports_filter or "",
-        "sources_filter": sources_filter or "",
-        "subtype_filter": subtype_filter or "",
-        "year_filter": year_filter or ""
-    }
-    final_state = graph.invoke(initial_state)
-    return final_state["result"]
-
-# Simple testing interface
-ui = gr.Interface(
-    fn=process_query,
-    inputs=gr.Textbox(lines=2, placeholder="Enter query here"),
-    outputs="text",
-    flagging_mode="never"
-)
-
-# Add a function to generate the graph visualization
-def get_graph_visualization():
-    """Generate and return the LangGraph workflow visualization as a PIL Image."""
-    # Generate the graph as PNG bytes
-    graph_png_bytes = graph.get_graph().draw_mermaid_png()
-    
-    # Convert bytes to PIL Image for Gradio display
-    graph_image = Image.open(io.BytesIO(graph_png_bytes))
-    return graph_image
-
-
-# Guidance for ChatUI - can be removed later. Questionable whether front end even necessary. Maybe nice to show the graph.
-with gr.Blocks(title="ChatFed Orchestrator") as demo:
-    gr.Markdown("# ChatFed Orchestrator")
-    gr.Markdown("This LangGraph server exposes MCP endpoints for the ChatUI module to call (which triggers the graph).")
-    
-    with gr.Row():
-        # Left column - Graph visualization
-        with gr.Column(scale=1):
-            gr.Markdown("**Workflow Visualization**")
-            graph_display = gr.Image(
-                value=get_graph_visualization(), 
-                label="LangGraph Workflow",
-                interactive=False,
-                height=300
-            )
-            
-            # Add a refresh button for the graph
-            refresh_graph_btn = gr.Button("🔄 Refresh Graph", size="sm")
-            refresh_graph_btn.click(
-                fn=get_graph_visualization,
-                outputs=graph_display
-            )
-        
-        # Right column - Interface and documentation  
-        with gr.Column(scale=2):
-            gr.Markdown("**Available MCP Tools:**")
-            
-            with gr.Accordion("MCP Endpoint Information", open=True):
-                gr.Markdown(f"""
-                **MCP Server Endpoint:** https://giz-chatfed-orchestrator.hf.space/gradio_api/mcp/sse
-                
-                **For ChatUI Integration:**
-                ```python
-                from gradio_client import Client
-                
-                # Connect to orchestrator
-                orchestrator_client = Client("https://giz-chatfed-orchestrator.hf.space")
-                
-                # Basic usage (no filters)
-                response = orchestrator_client.predict(
-                    query="query",
-                    api_name="/process_query"
-                )
-                
-                # Advanced usage with any combination of filters
-                response = orchestrator_client.predict(
-                    query="query",
-                    reports_filter="annual_reports",
-                    sources_filter="internal", 
-                    year_filter="2024",
-                    api_name="/process_query"
-                )
-                ```
-                """)
-
-    with gr.Accordion("Quick Testing Interface", open=True):
-        ui.render()
-
-if __name__ == "__main__":
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        mcp_server=True,
-        show_error=True
-    )