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import os
import gradio as gr
import requests
import pandas as pd
from agent import build_graph
from langchain_core.messages import HumanMessage
import re
def extract_answer(text: str) -> str:
"""
Clean and extract the final answer from agent output.
Removes prefixes like 'FINAL ANSWER:', trims punctuation,
and normalizes separators.
"""
# 提取 final answer 后内容
match = re.search(r"(final\s*answer|answer\s*is)[::]?\s*(.+)", text, re.IGNORECASE)
answer = match.group(2) if match else text
# 清理格式
answer = answer.strip().lstrip(":").strip() # ✅ 去掉前导冒号和空格
answer = answer.rstrip('.').strip()
# 多项格式化
if ',' in answer:
answer = ",".join(part.strip() for part in answer.split(','))
if ';' in answer:
answer = "; ".join(part.strip() for part in answer.split(';'))
return answer
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
# --- Basic Agent Definition ---
class BasicAgent:
def __init__(self, provider: str = "openai"):
print(f"Initializing LangGraph Agent with provider: {provider}")
self.graph = build_graph(provider=provider)
def __call__(self, question: str) -> str:
print(f"Running LangGraph Agent on question: {question[:50]}...")
try:
messages = [HumanMessage(content=question)]
result = self.graph.invoke({"messages": messages})
outputs = result["messages"]
for m in reversed(outputs):
if m.type == "ai":
raw_answer = m.content
clean = extract_answer(raw_answer)
print(f"Extracted clean answer: {clean}")
return clean
return ""
except Exception as e:
print(f"LangGraph Agent error: {e}")
return f"Error: {str(e)}"
def run_and_submit_all(username: str):
if not username:
return "❌ Please enter your Hugging Face username.", None
space_id = os.getenv("SPACE_ID")
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" if space_id else "N/A"
print(f"Fetching questions from: {questions_url}")
try:
response = requests.get(questions_url, timeout=15)
response.raise_for_status()
questions_data = response.json()
if not questions_data:
return "Fetched questions list is empty or invalid format.", None
print(f"Fetched {len(questions_data)} questions.")
except requests.exceptions.RequestException as e:
return f"Error fetching questions: {e}", None
results_log = []
answers_payload = []
print(f"Running agent on {len(questions_data)} questions...")
for item in questions_data:
task_id = item.get("task_id")
question_text = item.get("question")
if not task_id or question_text is None:
continue
try:
submitted_answer = agent(question_text)
answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
except Exception as e:
results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
if not answers_payload:
return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
submission_data = {
"username": username.strip(),
"agent_code": agent_code,
"answers": answers_payload
}
print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
try:
response = requests.post(submit_url, json=submission_data, timeout=60)
response.raise_for_status()
result_data = response.json()
final_status = (
f"✅ Submission Successful!\n"
f"User: {result_data.get('username')}\n"
f"Overall Score: {result_data.get('score', 'N/A')}% "
f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
f"Message: {result_data.get('message', 'No message received.')}"
)
results_df = pd.DataFrame(results_log)
return final_status, results_df
except Exception as e:
return f"❌ Submission Failed: {e}", pd.DataFrame(results_log)
# --- Gradio Interface ---
with gr.Blocks() as demo:
gr.Markdown("# Basic Agent Evaluation Runner")
gr.Markdown(
"""
**Instructions:**
1. Please enter your Hugging Face username below manually.
2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see your score.
---
"""
)
username_box = gr.Textbox(label="Your Hugging Face Username (for submission)", placeholder="e.g. johndoe")
run_button = gr.Button("Run Evaluation & Submit All Answers")
status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
run_button.click(
fn=run_and_submit_all,
inputs=[username_box],
outputs=[status_output, results_table]
)
if __name__ == "__main__":
print("\n" + "-"*30 + " App Starting " + "-"*30)
space_host_startup = os.getenv("SPACE_HOST")
space_id_startup = os.getenv("SPACE_ID")
if space_host_startup:
print(f"✅ SPACE_HOST found: {space_host_startup}")
print(f" Runtime URL should be: https://{space_host_startup}.hf.space")
else:
print("ℹ️ SPACE_HOST not found (running locally?).")
if space_id_startup:
print(f"✅ SPACE_ID found: {space_id_startup}")
print(f" Repo URL: https://huggingface.co/spaces/{space_id_startup}")
else:
print("ℹ️ SPACE_ID not found. Repo URL cannot be determined.")
print("-"*(60 + len(" App Starting ")) + "\n")
print("Launching Gradio Interface for Basic Agent Evaluation...")
demo.launch(debug=True, share=False)
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