| import os |
| import gradio as gr |
| import requests |
| import pandas as pd |
| from langchain_core.messages import HumanMessage |
| from agent import build_graph |
|
|
| |
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" |
|
|
| class BasicAgent: |
| """A langgraph agent.""" |
| def __init__(self): |
| print("BasicAgent initialized.") |
| self.graph = build_graph() |
|
|
| def __call__(self, question: str) -> str: |
| print(f"Agent received question (first 50 chars): {question[:50]}...") |
| messages = [HumanMessage(content=question)] |
| result = self.graph.invoke({"messages": messages}) |
| answer = result['messages'][-1].content |
| return answer |
|
|
| def run_and_submit_all(profile: gr.OAuthProfile | None): |
| space_id = os.getenv("SPACE_ID") |
| if not profile: |
| return "Please Login to Hugging Face with the button.", None |
| username = profile.username |
| api_url = DEFAULT_API_URL |
| questions_url = f"{api_url}/questions" |
| submit_url = f"{api_url}/submit" |
|
|
| try: |
| agent = BasicAgent() |
| except Exception as e: |
| return f"Error initializing agent: {e}", None |
|
|
| agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" |
| try: |
| resp_q = requests.get(questions_url, timeout=15) |
| resp_q.raise_for_status() |
| questions = resp_q.json() |
| except Exception as e: |
| return f"Error fetching questions: {e}", None |
|
|
| results_log = [] |
| answers = [] |
| for item in questions: |
| task_id = item.get("task_id") |
| q = item.get("question") |
| if not task_id or q is None: |
| continue |
| try: |
| ans = agent(q) |
| answers.append({"task_id": task_id, "submitted_answer": ans}) |
| results_log.append({"Task ID": task_id, "Question": q, "Submitted Answer": ans}) |
| except Exception as e: |
| results_log.append({"Task ID": task_id, "Question": q, "Submitted Answer": f"ERROR: {e}"}) |
|
|
| if not answers: |
| return "Agent did not produce any answers to submit.", pd.DataFrame(results_log) |
|
|
| payload = {"username": username.strip(), "agent_code": agent_code, "answers": answers} |
| try: |
| resp_s = requests.post(submit_url, json=payload, timeout=60) |
| resp_s.raise_for_status() |
| data = resp_s.json() |
| status = ( |
| f"Submission Successful!\n" |
| f"User: {data.get('username')}\n" |
| f"Score: {data.get('score', 'N/A')}% " |
| f"({data.get('correct_count', '?')}/{data.get('total_attempted', '?')})\n" |
| f"{data.get('message', '')}" |
| ) |
| return status, pd.DataFrame(results_log) |
| except Exception as e: |
| return f"Submission Failed: {e}", pd.DataFrame(results_log) |
|
|
| with gr.Blocks() as demo: |
| gr.Markdown("# Basic Agent Evaluation Runner") |
| gr.Markdown(""" |
| 1. Clone this space and customize your agent logic. |
| 2. Log in with the button below. |
| 3. Click **Run Evaluation & Submit All Answers**. |
| """) |
| gr.LoginButton() |
| run_btn = gr.Button("Run Evaluation & Submit All Answers") |
| status_out = gr.Textbox(label="Run Status / Submission Result", lines=5) |
| results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True) |
| run_btn.click(fn=run_and_submit_all, outputs=[status_out, results_table]) |
|
|
| if __name__ == "__main__": |
| demo.launch(debug=True, share=False) |
|
|
