feat: add FastAPI app and CLI entry point

app/main.py

@@ -0,0 +1,152 @@
+import argparse
+import json
+import os
+
+from fastapi import FastAPI
+from pydantic import BaseModel
+
+from app.graph import build_graph
+from app.schemas import ClinicalBrief
+from langgraph.types import Command
+
+
+class ChatRequest(BaseModel):
+    session_id: str
+    message: str
+
+
+class ChatResponse(BaseModel):
+    reply: str
+    state: str
+    brief: ClinicalBrief | None = None
+
+
+app = FastAPI(title="Clinical Intake Agent")
+
+graph, checkpointer = build_graph()
+
+
+def get_current_node(session_id: str) -> str:
+    """Get current node from checkpoint."""
+    config = {"configurable": {"thread_id": session_id}}
+    try:
+        snapshot = graph.get_state(config)
+        if snapshot and snapshot.values:
+            return snapshot.values.get("current_node", "intake")
+    except Exception:
+        pass
+    return "intake"
+
+
+def get_last_reply(session_id: str) -> str:
+    """Get last assistant reply from checkpoint."""
+    config = {"configurable": {"thread_id": session_id}}
+    try:
+        snapshot = graph.get_state(config)
+        if snapshot and snapshot.values:
+            messages = snapshot.values.get("messages", [])
+            for msg in reversed(messages):
+                if msg.get("role") == "assistant":
+                    return msg.get("content", "")
+    except Exception:
+        pass
+    return ""
+
+
+def get_brief(session_id: str) -> dict | None:
+    """Get clinical brief from checkpoint."""
+    config = {"configurable": {"thread_id": session_id}}
+    try:
+        snapshot = graph.get_state(config)
+        if snapshot and snapshot.values:
+            return snapshot.values.get("clinical_brief")
+    except Exception:
+        pass
+    return None
+
+
+@app.get("/health")
+async def health():
+    mock_mode = os.environ.get("MOCK_LLM", "false").lower() == "true"
+    return {"status": "ok", "mock_mode": mock_mode}
+
+
+@app.post("/chat", response_model=ChatResponse)
+async def chat(request: ChatRequest):
+    config = {"configurable": {"thread_id": request.session_id}}
+    
+    # Get current checkpoint state
+    snapshot = graph.get_state(config)
+    
+    # Check if graph is interrupted and waiting for input
+    if snapshot.next:
+        # First update state with the user message
+        graph.update_state(config, {"messages": [{"role": "user", "content": request.message}]})
+        # Then resume execution
+        result = graph.invoke(None, config=config)
+    else:
+        # New conversation - start fresh
+        input_state = {"messages": [{"role": "user", "content": request.message}]}
+        result = graph.invoke(input_state, config=config)
+    
+    current_node = get_current_node(request.session_id)
+    reply = get_last_reply(request.session_id)
+    brief_dict = get_brief(request.session_id)
+    
+    return ChatResponse(reply=reply, state=current_node, brief=brief_dict)
+
+
+def run_cli():
+    print("=" * 60)
+    print("Clinical Intake Agent - CLI Mode")
+    print("=" * 60)
+    print("Type your responses. The intake will end when complete.\n")
+
+    session_id = "cli_session"
+    
+    while True:
+        try:
+            user_input = input("You: ").strip()
+        except EOFError:
+            break
+
+        if not user_input:
+            continue
+
+        config = {"configurable": {"thread_id": session_id}}
+        
+        # Build input state from checkpoint or start fresh
+        snapshot = graph.get_state(config)
+        if snapshot and snapshot.values and snapshot.values.get("messages"):
+            # Continue existing conversation - only pass the new user message
+            # The Annotated reducer will append it to existing messages
+            input_state = {"messages": [{"role": "user", "content": user_input}]}
+        else:
+            input_state = {"messages": [{"role": "user", "content": user_input}]}
+        
+        result = graph.invoke(input_state, config=config)
+        
+        current_node = get_current_node(session_id)
+        reply = get_last_reply(session_id)
+        brief = get_brief(session_id)
+
+        print(f"\nAgent: {reply}\n")
+
+        if current_node == "done" and brief:
+            print("=" * 60)
+            print("CLINICAL INTAKE COMPLETE")
+            print("=" * 60)
+            print(json.dumps(brief, indent=2))
+            break
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Clinical Intake Agent")
+    parser.add_argument("--cli", action="store_true", help="Run in CLI mode")
+    args = parser.parse_args()
+
+    if args.cli:
+        run_cli()
+    else:
+        import uvicorn
+        uvicorn.run(app, host="0.0.0.0", port=7860)