| from typing import Any, Dict, List, Optional, TypedDict, Annotated |
| import operator |
| from langchain_core.messages import HumanMessage, AIMessage, SystemMessage, ToolMessage |
| from langchain_openai import ChatOpenAI |
| from langchain_core.tools import tool |
| from langgraph.prebuilt import ToolNode, tools_condition |
| from langgraph.graph import StateGraph, START, END |
| import os |
| import requests |
| import json |
| from dotenv import load_dotenv |
|
|
| load_dotenv() |
|
|
| OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") |
| llm = ChatOpenAI(model="gpt-4o", temperature=0, api_key=OPENAI_API_KEY) |
|
|
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" |
|
|
| class AgentState(TypedDict): |
| question: str |
| answer: str |
| task_id: str |
| log: Annotated[List[str], operator.add] |
|
|
| def assistant(state: AgentState) -> AgentState: |
| messages = [ |
| SystemMessage(content="You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: <your answer here>. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string."), |
| HumanMessage(content=state["question"]) |
| ] |
| response = llm.invoke(messages) |
| return {"answer": response.content, "log": [f"Assistant response: {response.content}"]} |
|
|
| |
| def get_all_questions(): |
| """Fetch all questions from the API""" |
| try: |
| response = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15) |
| response.raise_for_status() |
| return response.json() |
| except requests.exceptions.RequestException as e: |
| print(f"Error fetching questions: {e}") |
| return [] |
|
|
| def get_random_question(): |
| """Fetch a random question from the API""" |
| try: |
| response = requests.get(f"{DEFAULT_API_URL}/random-question", timeout=15) |
| response.raise_for_status() |
| return response.json() |
| except requests.exceptions.RequestException as e: |
| print(f"Error fetching random question: {e}") |
| return None |
|
|
| def get_file_for_task(task_id: str): |
| """Download file associated with a task ID""" |
| try: |
| response = requests.get(f"{DEFAULT_API_URL}/files/{task_id}", timeout=30) |
| response.raise_for_status() |
| return response.content |
| except requests.exceptions.RequestException as e: |
| print(f"Error fetching file for task {task_id}: {e}") |
| return None |
|
|
| def submit_answers(username: str, agent_code: str, answers: List[Dict]): |
| """Submit answers to the API""" |
| submission_data = { |
| "username": username, |
| "agent_code": agent_code, |
| "answers": answers |
| } |
| try: |
| response = requests.post(f"{DEFAULT_API_URL}/submit", json=submission_data, timeout=60) |
| response.raise_for_status() |
| return response.json() |
| except requests.exceptions.RequestException as e: |
| print(f"Error submitting answers: {e}") |
| return None |
|
|
| |
| graph = StateGraph(AgentState) |
| graph.add_node("assistant", assistant) |
| graph.add_edge(START, "assistant") |
| graph.add_edge("assistant", END) |
| app = graph.compile() |
|
|
| def run_agent(question: str, task_id: str): |
| """Run the agent on a single question""" |
| state = {"question": question, "task_id": task_id, "log": []} |
| return app.invoke(state) |
|
|
| def run_agent_on_all_questions(): |
| """Run the agent on all questions from the API""" |
| print("Fetching all questions...") |
| questions = get_all_questions() |
| |
| if not questions: |
| print("No questions found or error occurred") |
| return |
| |
| print(f"Found {len(questions)} questions") |
| results = [] |
| |
| for i, question_data in enumerate(questions): |
| task_id = question_data.get("task_id") |
| question_text = question_data.get("question") |
| |
| if not task_id or not question_text: |
| print(f"Skipping malformed question {i}") |
| continue |
| |
| print(f"\nProcessing question {i+1}/{len(questions)}") |
| print(f"Task ID: {task_id}") |
| print(f"Question: {question_text[:100]}...") |
| |
| |
| result = run_agent(question_text, task_id) |
| |
| results.append({ |
| "task_id": task_id, |
| "question": question_text, |
| "answer": result["answer"], |
| "log": result["log"] |
| }) |
| |
| print(f"Answer: {result['answer']}") |
| |
| return results |
|
|
| def demo_single_question(): |
| """Demo with a single random question""" |
| print("Fetching a random question...") |
| question_data = get_random_question() |
| |
| if not question_data: |
| print("Could not fetch random question") |
| return |
| |
| task_id = question_data.get("task_id") |
| question_text = question_data.get("question") |
| |
| print(f"Task ID: {task_id}") |
| print(f"Question: {question_text}") |
| |
| |
| result = run_agent(question_text, task_id) |
| |
| print(f"\nAnswer: {result['answer']}") |
| print(f"Log: {result['log']}") |
| |
| return result |
|
|
| if __name__ == "__main__": |
| |
| |
| |
| |
| |
| |
| |
| print("=== Running on All Questions ===") |
| results = run_agent_on_all_questions() |
| |
| |
| if results: |
| with open('agent_results.json', 'w') as f: |
| json.dump(results, f, indent=2) |
| print(f"\nResults saved to agent_results.json") |
| |
| |
| print("=== Manual Test ===") |
| manual_question = "What is the capital of France?" |
| manual_task_id = "test-123" |
| manual_result = run_agent(manual_question, manual_task_id) |
| print(f"Question: {manual_question}") |
| print(f"Answer: {manual_result['answer']}") |