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import requests
import time
import random
import copy
import traceback
import pandas as pd
from PIL import Image
from typing import List, Dict, Tuple, Any
from common_utils import encode_image_to_base64
from collections import defaultdict
try:
from latex2sympy2 import latex2sympy
except ImportError:
print('Warning: latex2sympy2 not installed. Install with: pip install latex2sympy2')
latex2sympy = None
FAIL_MSG = 'Failed to obtain answer via API.'
def is_equal(asw: str, gt_asw: str) -> bool:
"""Check if two answers are equal."""
if not isinstance(asw, str) or not isinstance(gt_asw, str):
print('Warning: input is not string')
print(asw, gt_asw)
asw = str(asw).lower().strip()
gt_asw = str(gt_asw).lower().strip()
if gt_asw == asw:
return True
try:
a = eval(gt_asw)
b = eval(asw)
if abs(a - b) < 1e-6:
return True
except:
pass
if latex2sympy is not None:
try:
a = latex2sympy(gt_asw)
b = latex2sympy(asw)
if abs(eval(str(a)) - eval(str(b))) < 1e-6:
return True
if abs(a - b) < 1e-6:
return True
except:
pass
return False
def get_gpt4_ICE():
"""Get in-context examples for GPT-4 answer extraction."""
example_1 = """
Hint: Please answer the question and provide the final answer at the end.\n
Question: Which number is missing?\n
Model response: The number missing in the sequence is 14.\n
Extracted answer: 14
"""
example_2 = """
Hint: Please answer the question and provide the final answer at the end.\n
Question: What is the fraction of females facing the camera?\n
Model response: The fraction of females facing the camera is 0.6,
which means that six out of ten females in the group are facing the camera.\n
Extracted answer: 0.6
"""
example_3 = """
Hint: Please answer the question and provide the final answer at the end.\n
Question: How much money does Luca need to buy a sour apple candy and a butter-scotch candy? (Unit: $)\n
Model response: Luca needs $1.45 to buy a sour apple candy and a butterscotch candy.\n
Extracted answer: 1.45
"""
example_4 = """
Hint: Please answer the question and provide the final answer at the end.\n
Question: Between which two years does the line graph saw its maximum peak?\n
Model response: The line graph saw its maximum peak between 2007 and 2008.\n
Extracted answer: [2007, 2008]
"""
example_5 = """
Hint: Please answer the question and provide the correct option letter, e.g., A, B, C, D, at the end.\n
Question: What fraction of the shape is blue?\n
Choices: (A) 3/11 (B) 8/11 (C) 6/11 (D) 3/5\n
Model response: The correct answer is (B) 8/11.\n
Extracted answer: B
"""
return [example_1, example_2, example_3, example_4, example_5]
def build_mathv_gpt4_prompt(line):
"""Build the prompt for GPT-4 to extract answer from model response."""
task_description = """
Please read the following example.
Then extract the answer from the model response and type it at the end of the prompt.\n
"""
question = line['question']
prediction = str(line['prediction'])
prompt = task_description
examples = get_gpt4_ICE()
for example in examples:
prompt += example + '\n'
prompt += question + '\n'
prompt += 'Model response: ' + prediction + '\n'
prompt += 'Extracted answer: '
return prompt
def list_to_dict(lst):
"""Convert list to dictionary with uppercase letters as keys."""
return {chr(65 + i): val for i, val in enumerate(lst)}
def can_infer_option(answer, choices):
"""Rule-based extraction of answer option."""
if FAIL_MSG in answer:
return False
reject_to_answer = [
"Sorry, I can't help with images of people yet.",
"I can't process this file.",
"I'm sorry, but without the image provided",
'Cannot determine the answer'
]
for err in reject_to_answer:
if err in answer:
return 'Z'
def count_choice(splits, choices, prefix='', suffix=''):
cnt = 0
for c in choices:
if prefix + c + suffix in splits:
cnt += 1
return cnt
answer_mod = copy.copy(answer)
chars = '.()[],:;!*#{}'
for c in chars:
answer_mod = answer_mod.replace(c, ' ')
splits = [x.strip() for x in answer_mod.split()]
count = count_choice(splits, choices)
if count == 1:
for ch in choices:
if 'A' in splits and len(splits) > 3:
return False
if ch in splits:
return ch
elif count == 0 and count_choice(splits, {'Z', ''}) == 1:
return 'Z'
return False
def can_infer_text(answer, choices):
"""Extract answer by matching text content."""
answer = answer.lower()
assert isinstance(choices, dict)
for k in choices:
choices[k] = str(choices[k]).lower()
cands = []
for k in choices:
if choices[k] in answer:
cands.append(k)
if len(cands) == 1:
return cands[0]
return False
def can_infer(answer, choices):
"""Combined approach to infer answer choice."""
answer = str(answer)
copt = can_infer_option(answer, choices)
return copt if copt else can_infer_text(answer, choices)
def post_check(line, prefetch=False):
"""Check if the prediction matches the answer."""
res = None
ans = line['answer']
response = line['prediction'] if prefetch else line['res']
try:
if len(eval(line['choices'])) > 0:
ans = line['answer']
choices = list_to_dict(eval(line['choices']))
res = can_infer(response, choices)
if prefetch:
return res
else:
res = str(response)
ans = str(ans)
except ValueError:
pass
if is_equal(res, ans):
return res if prefetch else True
else:
return False
class OpenAIWrapper:
"""Wrapper for OpenAI API."""
def __init__(self, model, api_base, api_key, timeout=60, retry=5, wait=5):
self.model = model
self.api_base = api_base
self.api_key = api_key
self.timeout = timeout
self.retry = retry
self.wait = wait
self.fail_msg = FAIL_MSG
def generate(self, prompt, temperature=0):
"""Generate a response from the API."""
headers = {'Content-Type': 'application/json', 'Authorization': f'Bearer {self.api_key}'}
payload = {
"model": self.model,
"messages": [{"role": "user", "content": prompt}],
"max_tokens": 4096,
"temperature": temperature
}
for i in range(self.retry):
try:
response = requests.post(
self.api_base,
headers=headers,
json=payload,
timeout=self.timeout
)
if response.status_code == 200:
resp_json = response.json()
return resp_json['choices'][0]['message']['content'].strip()
time.sleep(self.wait)
except Exception as e:
print(f"API error: {e}")
time.sleep(self.wait)
return self.fail_msg
class DashScopeWrapper:
"""Wrapper for DashScope API."""
def __init__(self, model, api_base, api_key, timeout=60, retry=5, wait=5):
self.model = model
self.api_base = api_base
self.api_key = api_key
self.timeout = timeout
self.retry = retry
self.wait = wait
self.fail_msg = FAIL_MSG
def generate(self, prompt, temperature=0):
"""Generate a response from the API."""
headers = {'Content-Type': 'application/json', 'Authorization': f'Bearer {self.api_key}'}
payload = {
"model": self.model,
"messages": [{"role": "user", "content": prompt}],
"max_completion_tokens": 4096,
"n": 1,
"temperature": temperature,
"stream": False
}
for i in range(self.retry):
try:
response = requests.post(
self.api_base,
headers=headers,
json=payload,
timeout=self.timeout
)
if response.status_code == 200:
resp_json = response.json()
# Check finish reason
for output in resp_json['choices']:
if output['finish_reason'] not in ['stop', 'function_call']:
print(f"DashScope finished with error: {resp_json}")
time.sleep(self.wait)
continue
return resp_json['choices'][0]['message']['content']
else:
print(f"DashScope API error: HTTP {response.status_code}")
try:
error_content = response.json()
print(f"Error details: {error_content}")
except:
print(f"Raw error content: {response.content.decode('utf-8', errors='replace')}")
time.sleep(self.wait)
except Exception as e:
print(f"DashScope error: {e}")
time.sleep(self.wait)
return self.fail_msg
def build_judge(model, api_type):
"""Build a judge model for evaluation."""
if api_type == 'mit':
api_key = os.environ.get('MIT_SPIDER_TOKEN', '')
api_base = os.environ.get('MIT_SPIDER_URL', '')
return OpenAIWrapper(model, api_base, api_key)
elif api_type == 'dash':
api_key = os.environ.get('CHATGPT_DASHSCOPE_API_KEY', '')
api_base = os.environ.get('DASHSCOPE_API_BASE', '')
return DashScopeWrapper(model, api_base, api_key)
else:
raise ValueError(f"Unsupported API type: {api_type}")
def MATH_V_auxeval(args):
"""Auxiliary evaluation for MathVision - extract answer from model response."""
model, line = args
prompt = build_mathv_gpt4_prompt(line)
log = ''
retry = 5
# Try rule-based extraction first
if post_check(line, prefetch=True):
res = post_check(line, prefetch=True)
log += 'Prefetch succeed.\n'
extract_flag = True
if not res or res == 'Z':
extract_flag = False
log += f'Rule extract failed with ans: {res}'
else:
log += f'Rule extract success with ans: {res}'
return dict(log=log, res=res, extract_model='rule', extract_flag=extract_flag)
# Use model-based extraction
for i in range(retry):
prediction = line['prediction']
res = model.generate(prompt, temperature=i * 0.5)
if FAIL_MSG in res:
log += f'Try {i}: output is {prediction}, failed to parse.\n'
else:
log += f'{model.model} extract Succeed.\n'
return dict(log=log, res=res, extract_model=model.model, extract_flag=True)
log += f'All {retry} retries failed.\n {model.model} response:{res}'
return dict(log=log, res='', extract_model=model.model, extract_flag=False)
def MATH_V_acc(result_file):
"""Calculate accuracy for MathVision results."""
data = pd.read_excel(result_file) if result_file.endswith('.xlsx') else pd.read_csv(result_file)
tot = defaultdict(lambda: 0)
fetch = defaultdict(lambda: 0)
hit = defaultdict(lambda: 0)
lt = len(data)
extract_counts = {}
for i in range(lt):
item = data.iloc[i]
cate = item['category']
tot['Overall'] += 1
tot[cate] += 1
if 'Prefetch succeed' in item['log']:
fetch['Overall'] += 1
fetch[cate] += 1
if post_check(item, prefetch=False):
hit['Overall'] += 1
hit[cate] += 1
# Statistics of answers extracted by rule and gpt
extract_model = item['extract_model']
extract_flag = item['extract_flag']
if extract_model in extract_counts:
extract_counts[extract_model][1] += 1
else:
extract_counts[extract_model] = [0, 1] # succeed, total
if extract_flag:
extract_counts[extract_model][0] += 1
res = defaultdict(list)
for k in tot.keys():
res['Subject'].append(k)
res['tot'].append(tot[k])
res['prefetch'].append(fetch[k])
res['hit'].append(hit[k])
res['prefetch_rate'].append(fetch[k] / tot[k] * 100)
res['acc'].append(hit[k] / tot[k] * 100)
if k == 'Overall':
for model_key in extract_counts:
res[model_key+'_success'].append(extract_counts[model_key][0])
res[model_key+'_all'].append(extract_counts[model_key][1])
else:
for model_key in extract_counts:
res[model_key+'_success'].append(0)
res[model_key+'_all'].append(0)
res = pd.DataFrame(res).sort_values('Subject', ignore_index=True)
return res
def eval_single_sample(args):
"""Evaluate a single sample."""
return MATH_V_auxeval(args)
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