Support --task-file argument for main.py (#358)

* Update main.py

* Update main.py

* Update opendevin/main.py

Co-authored-by: Robert Brennan <accounts@rbren.io>

* Update main.py

* Update main.py

* Update main.py

* Update main.py

---------

Co-authored-by: Robert Brennan <accounts@rbren.io>

opendevin/main.py

@@ -1,56 +1,52 @@
 import asyncio
 import argparse
-
+import sys
 from typing import Type
 
-import agenthub # noqa F401 (we import this to get the agents registered)
-from opendevin import config
+import agenthub  # noqa F401 (we import this to get the agents registered)
+from opendevin import config  
 from opendevin.agent import Agent
 from opendevin.controller import AgentController
 from opendevin.llm.llm import LLM
 
-if __name__ == "__main__":
+def read_task_from_file(file_path: str) -> str:
+    """Read task from the specified file."""
+    with open(file_path, 'r', encoding='utf-8') as file:
+        return file.read()
+
+def read_task_from_stdin() -> str:
+    """Read task from stdin."""
+    return sys.stdin.read()
+
+async def main():
+    """Main coroutine to run the agent controller with task input flexibility."""
     parser = argparse.ArgumentParser(description="Run an agent with a specific task")
-    parser.add_argument(
-        "-d",
-        "--directory",
-        required=True,
-        type=str,
-        help="The working directory for the agent",
-    )
-    parser.add_argument(
-        "-t",
-        "--task",
-        required=True,
-        type=str,
-        help="The task for the agent to perform",
-    )
-    parser.add_argument(
-        "-c",
-        "--agent-cls",
-        default="LangchainsAgent",
-        type=str,
-        help="The agent class to use",
-    )
-    parser.add_argument(
-        "-m",
-        "--model-name",
-        default=config.get_or_default("LLM_MODEL", "gpt-4-0125-preview"),
-        type=str,
-        help="The (litellm) model name to use",
-    )
-    parser.add_argument(
-        "-i",
-        "--max-iterations",
-        default=100,
-        type=int,
-        help="The maximum number of iterations to run the agent",
-    )
+    parser.add_argument("-d", "--directory", required=True, type=str, help="The working directory for the agent")
+    parser.add_argument("-t", "--task", type=str, default="", help="The task for the agent to perform")
+    parser.add_argument("-f", "--file", type=str, help="Path to a file containing the task. Overrides -t if both are provided.")
+    parser.add_argument("-c", "--agent-cls", default="LangchainsAgent", type=str, help="The agent class to use")
+    parser.add_argument("-m", "--model-name", default=config.get_or_default("LLM_MODEL", "gpt-4-0125-preview"), type=str, help="The (litellm) model name to use")
+    parser.add_argument("-i", "--max-iterations", default=100, type=int, help="The maximum number of iterations to run the agent")
     args = parser.parse_args()
 
-    print(f"Running agent {args.agent_cls} (model: {args.model_name}, directory: {args.directory}) with task: \"{args.task}\"")
+    # Determine the task source
+    if args.file:
+        task = read_task_from_file(args.file)
+    elif not sys.stdin.isatty():
+        task = read_task_from_stdin()
+    else:
+        task = args.task
+
+    if not task:
+        raise ValueError("No task provided. Please specify a task through -t, -f.")
+
+    print(f"Running agent {args.agent_cls} (model: {args.model_name}, directory: {args.directory}) with task: \"{task}\"")
     llm = LLM(args.model_name)
     AgentCls: Type[Agent] = Agent.get_cls(args.agent_cls)
     agent = AgentCls(llm=llm)
     controller = AgentController(agent, workdir=args.directory, max_iterations=args.max_iterations)
-    asyncio.run(controller.start_loop(args.task))
+
+    await controller.start_loop(task)
+
+if __name__ == "__main__":
+    asyncio.run(main())