refactor(simulator): remove special-casing for input/output blocks

The simulator now has the block's run() source code via inspect.getsource(),
so it can figure out what any block does by reading the code. No need for
special isinstance checks for AgentInputBlock/AgentOutputBlock.

autogpt_platform/backend/backend/executor/simulator.py

@@ -10,8 +10,8 @@ Special cases (no LLM simulation needed):
     (iterations capped to 1).
   - AgentExecutorBlock executes for real so it can spawn child graph executions
     (whose blocks are then simulated).
-  - AgentInputBlock (and all subclasses) and AgentOutputBlock are pure
-    passthrough -- they forward their input values directly.
+  - AgentInputBlock, AgentOutputBlock, and other simple blocks are simulated
+    using the same LLM prompt (which includes the block's run() source code).
   - MCPToolBlock uses a specialised LLM prompt grounded in the tool's schema.
 
 OrchestratorBlock and AgentExecutorBlock are handled in manager.py via
@@ -34,7 +34,6 @@ from collections.abc import AsyncGenerator
 from typing import Any
 
 from backend.blocks.agent import AgentExecutorBlock
-from backend.blocks.io import AgentInputBlock, AgentOutputBlock
 from backend.blocks.mcp.block import MCPToolBlock
 from backend.blocks.orchestrator import OrchestratorBlock
 from backend.util.clients import get_openai_client
@@ -388,32 +387,6 @@ async def simulate_block(
             yield output
         return
 
-    # Input/output blocks are pure passthrough -- they just forward their
-    # input values.  No LLM simulation needed.
-    if isinstance(block, AgentInputBlock):
-        # AgentInputBlock and all subclasses (AgentDropdownInputBlock,
-        # AgentFileInputBlock, AgentShortTextInputBlock, etc.) yield
-        # "result" with the provided value.
-        value = input_data.get("value")
-        if value is None:
-            # Dry-run with no user input: generate a sensible default so
-            # downstream blocks (e.g. OrchestratorBlock) still receive data.
-            placeholder = input_data.get("placeholder_values")
-            if placeholder and isinstance(placeholder, list) and placeholder:
-                value = placeholder[0]  # First dropdown option
-            else:
-                name = input_data.get("name", "input")
-                value = f"sample {name}"
-        yield "result", value
-        return
-
-    if isinstance(block, AgentOutputBlock):
-        # AgentOutputBlock passes through "value" as "output" (+ "name").
-        yield "output", input_data.get("value")
-        if "name" in input_data:
-            yield "name", input_data["name"]
-        return
-
     output_schema = block.output_schema.jsonschema()
     output_properties: dict[str, Any] = output_schema.get("properties", {})