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import os
import gradio as gr
import requests
from transformers import AutoModelForCausalLM, AutoTokenizer
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
MODEL_NAME = "meta-llama/Llama-4-Scout-17B-16E-Instruct"
# --- Basic Agent Using Llama 4 ---
class BasicAgent:
def __init__(self, hf_token: str):
print("Initializing Llama 4 Agent...")
self.tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_auth_token=hf_token)
self.model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, use_auth_token=hf_token)
def __call__(self, question: str) -> str:
question = question.encode('utf-8', errors='ignore').decode('utf-8')
inputs = self.tokenizer(question, return_tensors="pt")
outputs = self.model.generate(**inputs, max_new_tokens=50)
answer = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
# 只回覆答案，不包含 prompt
if question in answer:
answer = answer.replace(question, '').strip()
return answer
# --- Function to run and submit ---
def run_and_submit_all(profile: gr.OAuthProfile | None):
hf_token = os.getenv('HF_TOKEN')
if not hf_token:
return "HF_TOKEN not set!", None
if not profile:
return "Please login to Hugging Face.", None
username = profile.username
api_url = DEFAULT_API_URL
questions_url = f"{api_url}/questions"
submit_url = f"{api_url}/submit"
# 1. Create agent
try:
agent = BasicAgent(hf_token)
except Exception as e:
return f"Error initializing agent: {e}", None
# 2. Fetch questions
try:
response = requests.get(questions_url, timeout=15)
response.raise_for_status()
questions_data = response.json()
if not questions_data:
return "No questions fetched.", None
except Exception as e:
return f"Error fetching questions: {e}", None
# 3. Run agent on questions
answers_payload = []
results_log = []
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 "No answers generated.", results_log
# 4. Prepare submission
space_id = os.getenv('SPACE_ID', 'YOUR_SPACE_NAME')
agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
submission_data = {"username": username, "agent_code": agent_code, "answers": answers_payload}
# 5. Submit
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', '')}")
return final_status, results_log
except Exception as e:
return f"Submission Failed: {e}", results_log |