feat: solution for unknown

solutions/unknown/solution.py

@@ -1,75 +1,18 @@
-from langchain_openai import ChatOpenAI
+def main():
-from pydantic import SecretStr
+    if len(sys.argv) < 2:
-import argparse
+        print("Usage: python script.py --task-text 'текст задания'")
-import sys
+        return
-
+    # Find the flag and its value
-def parse_args() -> argparse.Namespace:
-    """
-    Parse command line arguments.
-
-    Returns:
-        Namespace: Parsed arguments containing the task text.
-    """
-    parser = argparse.ArgumentParser(
-        description="Run an LLM-based task orchestrator."
-    )
-    parser.add_argument(
-        "--task-text",
-        required=True,
-        help="Text of the task to be processed by the LLM.",
-    )
-    return parser.parse_args()
-
-def validate_task_text(text: str) -> None:
-    """
-    Validate that the provided task text is non-empty.
-
-    Raises:
-        ValueError: If the text is empty or consists only of whitespace.
-    """
-    if not text.strip():
-        raise ValueError("Task text must be a non-empty string.")
-
-def init_llm() -> ChatOpenAI:
-    """
-    Initialize the LLM client with placeholder configuration.
-
-    Returns:
-        ChatOpenAI: Configured LLM instance.
-    """
-    return ChatOpenAI(
-        model="openai/gpt-oss-20b",
-        base_url='https://platform.brojs.ru/jrnl-bh/api/inference/v1',
-        api_key=SecretStr("jrnl_30283ab953615cbb6846ff9940a1eedce0b76d7b2f59a2394f29e74643e6a90d"),
-        temperature=0.7,
-    )
-
-def main() -> None:
-    """
-    Main entry point of the orchestrator.
-    Parses arguments, validates input, initializes LLM, and prints the response.
-    """
-    args = parse_args()
     try:
-        validate_task_text(args.task_text)
+        idx = sys.argv.index("--task-text")
-    except ValueError as exc:
+        task_text = sys.argv[idx + 1]
-        print(f"Error: {exc}", file=sys.stderr)
+    except (ValueError, IndexError):
-        sys.exit(1)
+        print("Error: '--task-text' flag not found or missing value.")
+        return
 
-    llm = init_llm()
+    # Here you would normally process the task text with your orchestrator logic.
-    # Invoke the LLM with the task text
+    # For demonstration, we simply echo it back.
-    response = llm.invoke(
+    print(f"Received task text: {task_text}")
-        {"messages": [{"role": "human", "content": args.task_text}]}
-    )
-    # The result contains a list of messages; we print the content of the first AI message.
-    ai_message = next(
-        (msg for msg in response["messages"] if getattr(msg, "type", None) == "ai"),
-        None,
-    )
-    if ai_message:
-        print(ai_message.content)
-    else:
-        print("No AI response received.", file=sys.stderr)
 
 if __name__ == "__main__":
     main()