File size: 5,652 Bytes
10e9b7d eccf8e4 3c4371f 10e9b7d 8cb674b 3db6293 e80aab9 8cb674b 31243f4 8cb674b 31243f4 8cb674b 3c4371f 7e4a06b 8cb674b 3c4371f 7e4a06b 8cb674b 3c4371f 7e4a06b 8cb674b 31243f4 e80aab9 8cb674b 31243f4 8cb674b 31243f4 8cb674b 36ed51a 8cb674b c1fd3d2 3c4371f 8cb674b eccf8e4 8cb674b 7d65c66 8cb674b 31243f4 8cb674b 7d65c66 8cb674b e80aab9 8cb674b 7d65c66 8cb674b 7d65c66 8cb674b 31243f4 8cb674b 31243f4 8cb674b 31243f4 8cb674b 31243f4 8cb674b 31243f4 7d65c66 31243f4 8cb674b 31243f4 8cb674b e80aab9 8cb674b e80aab9 8cb674b e80aab9 8cb674b 31243f4 8cb674b e80aab9 8cb674b e80aab9 8cb674b 31243f4 8cb674b 31243f4 8cb674b 7d65c66 8cb674b 31243f4 e80aab9 8cb674b e80aab9 8cb674b e80aab9 e514fd7 8cb674b e514fd7 8cb674b e514fd7 8cb674b e80aab9 7e4a06b e80aab9 8cb674b e80aab9 8cb674b e80aab9 31243f4 8cb674b e80aab9 7d65c66 8cb674b 7d65c66 8cb674b 3c4371f 8cb674b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 | import os
import gradio as gr
import requests
import pandas as pd
from smolagents import (
CodeAgent,
DuckDuckGoSearchTool,
InferenceClientModel
)
# -----------------------------
# Constants
# -----------------------------
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
# -----------------------------
# Smart Agent
# -----------------------------
class BasicAgent:
def __init__(self):
print("Initializing Smart Agent...")
# Web Search Tool
search_tool = DuckDuckGoSearchTool()
# Free Hugging Face Model
model = InferenceClientModel(
model_id="meta-llama/Llama-3.1-8B-Instruct"
)
# Main Agent
self.agent = CodeAgent(
tools=[search_tool],
model=model,
add_base_tools=True,
max_steps=5
)
def __call__(self, question: str) -> str:
print(f"Question: {question}")
prompt = f"""
You are a GAIA benchmark assistant.
IMPORTANT RULES:
- Return ONLY the final answer
- Do NOT explain your reasoning
- Do NOT write 'FINAL ANSWER'
- Keep answers short and exact
- If the answer is a number, return only the number
- If the answer is text, return only the text
Question:
{question}
"""
try:
response = self.agent.run(prompt)
answer = str(response).strip()
print(f"Agent answer: {answer}")
return answer
except Exception as e:
print(f"Error while solving question: {e}")
return "Error"
# -----------------------------
# Main Evaluation Function
# -----------------------------
def run_and_submit_all(profile: gr.OAuthProfile | None):
space_id = os.getenv("SPACE_ID")
if profile:
username = f"{profile.username}"
print(f"User logged in: {username}")
else:
return "Please login with Hugging Face first.", None
api_url = DEFAULT_API_URL
questions_url = f"{api_url}/questions"
submit_url = f"{api_url}/submit"
# -----------------------------
# Create Agent
# -----------------------------
try:
agent = BasicAgent()
except Exception as e:
return f"Error initializing agent: {e}", None
# -----------------------------
# Space Code URL
# -----------------------------
agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
print(agent_code)
# -----------------------------
# Fetch Questions
# -----------------------------
try:
response = requests.get(
questions_url,
timeout=30
)
response.raise_for_status()
questions_data = response.json()
print(f"Fetched {len(questions_data)} questions")
except Exception as e:
return f"Error fetching questions: {e}", None
# -----------------------------
# Run Agent
# -----------------------------
results_log = []
answers_payload = []
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"ERROR: {e}"
})
# -----------------------------
# Submit Answers
# -----------------------------
submission_data = {
"username": username.strip(),
"agent_code": agent_code,
"answers": answers_payload
}
try:
response = requests.post(
submit_url,
json=submission_data,
timeout=120
)
response.raise_for_status()
result_data = response.json()
final_status = (
f"Submission Successful!\n\n"
f"User: {result_data.get('username')}\n"
f"Score: {result_data.get('score')}%\n"
f"Correct: {result_data.get('correct_count')}/"
f"{result_data.get('total_attempted')}\n\n"
f"Message: {result_data.get('message')}"
)
results_df = pd.DataFrame(results_log)
return final_status, results_df
except Exception as e:
results_df = pd.DataFrame(results_log)
return f"Submission Failed: {e}", results_df
# -----------------------------
# Gradio UI
# -----------------------------
with gr.Blocks() as demo:
gr.Markdown("# GAIA Agent Evaluation")
gr.Markdown(
"""
Login with Hugging Face and run your AI agent on GAIA questions.
"""
)
gr.LoginButton()
run_button = gr.Button(
"Run Evaluation & Submit All Answers"
)
status_output = gr.Textbox(
label="Status",
lines=8
)
results_table = gr.DataFrame(
label="Agent Results"
)
run_button.click(
fn=run_and_submit_all,
outputs=[
status_output,
results_table
]
)
# -----------------------------
# Launch App
# -----------------------------
if __name__ == "__main__":
print("Starting GAIA Agent App...")
demo.launch(
debug=True,
share=False
) |