app.py

import gradio as gr
import json
import os
from tqdm import tqdm
import pandas as pd
import numpy as np
from collections import Counter
import time
from zipfile import ZipFile
from openai import AzureOpenAI
from openai._exceptions import RateLimitError, BadRequestError

client = AzureOpenAI(
    api_key=os.environ.get("AZURE_OPENAI_API_KEY"),
    api_version=os.environ.get("AZURE_OPENAI_API_VERSION"),
    azure_endpoint=os.getenv("AZURE_OPENAI_API_ENDPOINT"),
)
deployment_id = os.environ.get("AZURE_OPENAI_DEP_ID")
gpt_model = deployment_id


prompt = """Compare the ground truth and prediction from AI models, to give a correctness score for the prediction. <AND> in the ground truth means it is totally right only when all elements in the ground truth are present in the prediction, and <OR> means it is totally right when any one element in the ground truth is present in the prediction. The correctness score is 0.0 (totally wrong), 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, or 1.0 (totally right). Just complete the last space of the correctness score.

Question | Ground truth | Prediction | Correctness
--- | --- | --- | ---
What is x in the equation? | -1 <AND> -5 | x = 3 | 0.0
What is x in the equation? | -1 <AND> -5 | x = -1 | 0.5
What is x in the equation? | -1 <AND> -5 | x = -5 | 0.5
What is x in the equation? | -1 <AND> -5 | x = -5 or 5 | 0.5
What is x in the equation? | -1 <AND> -5 | x = -1 or x = -5 | 1.0
Can you explain this meme? | This meme is poking fun at the fact that the names of the countries Iceland and Greenland are misleading. Despite its name, Iceland is known for its beautiful green landscapes, while Greenland is mostly covered in ice and snow. The meme is saying that the person has trust issues because the names of these countries do not accurately represent their landscapes. | The meme talks about Iceland and Greenland. It's pointing out that despite their names, Iceland is not very icy and Greenland isn't very green. | 0.4
Can you explain this meme? | This meme is poking fun at the fact that the names of the countries Iceland and Greenland are misleading. Despite its name, Iceland is known for its beautiful green landscapes, while Greenland is mostly covered in ice and snow. The meme is saying that the person has trust issues because the names of these countries do not accurately represent their landscapes. | The meme is using humor to point out the misleading nature of Iceland's and Greenland's names. Iceland, despite its name, has lush green landscapes while Greenland is mostly covered in ice and snow. The text 'This is why I have trust issues' is a playful way to suggest that these contradictions can lead to distrust or confusion. The humor in this meme is derived from the unexpected contrast between the names of the countries and their actual physical characteristics. | 1.0
"""



def grade(file_obj, progress=gr.Progress()):
    # load metadata
    # Download mm-vet.zip and `unzip mm-vet.zip` and change the path below
    mmvet_path = "mm-vet"
    use_sub_set = False
    decimal_places = 1 # number of decimal places to round to


    if use_sub_set:
        bard_set_file = os.path.join(mmvet_path, "bard_set.json")
        with open(bard_set_file, 'r') as f:
            sub_set = json.load(f)
        sub_set_name = 'bardset'
        sub_set_name = sub_set_name + '_'
    else:
        sub_set = None
        sub_set_name = ''

    mmvet_metadata = os.path.join(mmvet_path, "mm-vet.json")
    with open(mmvet_metadata, 'r') as f:
        data = json.load(f)


    counter = Counter()
    cap_set_list = []
    cap_set_counter = []
    len_data = 0
    for id, value in data.items():
        if sub_set is not None and id not in sub_set:
            continue
        question = value["question"]
        answer = value["answer"]
        cap = value["capability"]
        cap = set(cap)
        counter.update(cap)
        if cap not in cap_set_list:
            cap_set_list.append(cap)
            cap_set_counter.append(1)
        else:
            cap_set_counter[cap_set_list.index(cap)] += 1
        
        len_data += 1

    sorted_list = counter.most_common()
    columns = [k for k, v in sorted_list]
    columns.append("total")
    columns.append("std")
    columns.append('runs')
    df = pd.DataFrame(columns=columns)


    cap_set_sorted_indices = np.argsort(-np.array(cap_set_counter))
    new_cap_set_list = []
    new_cap_set_counter = []
    for index in cap_set_sorted_indices:
        new_cap_set_list.append(cap_set_list[index])
        new_cap_set_counter.append(cap_set_counter[index])

    cap_set_list = new_cap_set_list
    cap_set_counter = new_cap_set_counter
    cap_set_names = ["_".join(list(cap_set)) for cap_set in cap_set_list]

    columns2 = cap_set_names
    columns2.append("total")
    columns2.append("std")
    columns2.append('runs')
    df2 = pd.DataFrame(columns=columns2)


    ###### change your model name ######
    model = file_obj.name.split("/")[-1][:-5]
    # result_path = "results"
    num_run = 1 # we set 5 in the paper
    # model_results_file = os.path.join(result_path, f"{model}.json")
    model_results_file = file_obj.name

    # grade results for each sample to svae
    grade_file = f'{model}_{gpt_model}-grade-{num_run}runs.json'
    # grade_file = os.path.join(result_path, grade_file)

    # score results regarding capabilities/capability integration to save
    cap_score_file = f'{model}_{sub_set_name}{gpt_model}-cap-score-{num_run}runs.csv'
    # cap_score_file = os.path.join(result_path, cap_score_file)
    cap_int_score_file = f'{model}_{sub_set_name}{gpt_model}-cap-int-score-{num_run}runs.csv'
    # cap_int_score_file = os.path.join(result_path, cap_int_score_file)



    with open(model_results_file) as f:
        results = json.load(f)
    if os.path.exists(grade_file):
        with open(grade_file, 'r') as f:
            grade_results = json.load(f)
    else:
        grade_results = {}


    def need_more_runs():
        need_more_runs = False
        if len(grade_results) > 0:
            for k, v in grade_results.items():
                if len(v['score']) < num_run:
                    need_more_runs = True
                    break
        return need_more_runs or len(grade_results) < len_data


    while need_more_runs():
        for j in range(num_run):
            print(f'eval run {j}')
            for id, line in progress.tqdm(data.items(), desc="Grading"):
                if sub_set is not None and id not in sub_set:
                    continue
                if id in grade_results and len(grade_results[id]['score']) >= (j + 1):
                    continue

                model_pred = results[id]
                
                question = prompt + '\n' + ' | '.join([line['question'], line['answer'].replace("<AND>", " <AND> ").replace("<OR>", " <OR> "), model_pred, ""])
                messages = [
                {"role": "user", "content": question},
                ]

                if id not in grade_results:
                    sample_grade = {'model': [], 'content': [], 'score': []}
                else:
                    sample_grade = grade_results[id]

                
                grade_sample_run_complete = False
                temperature = 0.0

                num_sleep = 0
                while not grade_sample_run_complete:
                    try:
                        response = client.chat.completions.create(
                            model=gpt_model,
                            max_tokens=3,
                            temperature=temperature,
                            messages=messages)
                        content = response.choices[0].message.content
                        flag = True
                        try_time = 1
                        while flag:
                            try:
                                content = content.split(' ')[0].strip()
                                score = float(content)
                                if score > 1.0 or score < 0.0:
                                    assert False
                                flag = False
                            except:
                                question = prompt + '\n' + ' | '.join([line['question'], line['answer'].replace("<AND>", " <AND> ").replace("<OR>", " <OR> "), model_pred, ""]) + "\nPredict the correctness of the answer (digit): "
                                messages = [
                                {"role": "user", "content": question},
                                ]
                                response = client.chat.completions.create(
                                    model=gpt_model,
                                    max_tokens=3,
                                    temperature=temperature,
                                    messages=messages)
                                content = response.choices[0].message.content
                                try_time += 1
                                temperature += 0.5
                                print(f"{id} try {try_time} times")
                                print(content)
                                if try_time > 5:
                                    score = 0.0
                                    flag = False
                        grade_sample_run_complete = True
                        response_model = response.model
                    except BadRequestError as e:
                        content = "BadRequestError"
                        score = 0.0
                        flag = False
                        print(id, "BadRequestError")
                        response_model = gpt_model
                        break
                    # except RateLimitError as e:
                    except:
                        # gpt4 may have token rate limit
                        num_sleep += 1
                        if num_sleep > 12:
                            score = 0.0
                            grade_sample_run_complete = True
                            content = "RateLimitError"
                            num_sleep = 0
                            continue
                        print("sleep 5s")
                        time.sleep(5)
                        response_model = gpt_model
                        

                if len(sample_grade['model']) >= j + 1:
                    sample_grade['model'][j] = response_model
                    sample_grade['content'][j] = content
                    sample_grade['score'][j] = score
                else:
                    sample_grade['model'].append(response_model)
                    sample_grade['content'].append(content)
                    sample_grade['score'].append(score)
                grade_results[id] = sample_grade

                with open(grade_file, 'w') as f:
                    json.dump(grade_results, f, indent=4)


    assert not need_more_runs()
    cap_socres = {k: [0.0]*num_run for k in columns[:-2]}
    counter['total'] = len_data

    cap_socres2 = {k: [0.0]*num_run for k in columns2[:-2]}
    counter2 = {columns2[i]:cap_set_counter[i] for i in range(len(cap_set_counter))}
    counter2['total'] = len_data

    for k, v in grade_results.items():
        if sub_set is not None and k not in sub_set:
            continue
        for i in range(num_run):
            score = v['score'][i]
            caps = set(data[k]['capability'])
            for c in caps:
                cap_socres[c][i] += score
            
            cap_socres['total'][i] += score

            index = cap_set_list.index(caps)
            cap_socres2[cap_set_names[index]][i] += score
            cap_socres2['total'][i] += score

    for k, v in cap_socres.items():
        cap_socres[k] = np.array(v) / counter[k] *100


    std = round(cap_socres['total'].std(), decimal_places)
    total_copy = cap_socres['total'].copy()
    runs = str(list(np.round(total_copy, decimal_places)))

    for k, v in cap_socres.items():
        cap_socres[k] = round(v.mean(), decimal_places)

    cap_socres['std'] = std
    cap_socres['runs'] = runs
    df.loc[model] = cap_socres


    for k, v in cap_socres2.items():
        cap_socres2[k] = round(np.mean(np.array(v) / counter2[k] *100), decimal_places)
    cap_socres2['std'] = std
    cap_socres2['runs'] = runs
    df2.loc[model] = cap_socres2

    df.to_csv(cap_score_file)
    df2.to_csv(cap_int_score_file)

    files = [cap_score_file, cap_int_score_file, grade_file]
    zip_file = f"results.zip"
    with ZipFile(zip_file, "w") as zipObj:
        for idx, file in enumerate(files):
            zipObj.write(file, file)
    for file in files:
        os.remove(file)
    return zip_file



# demo = gr.Interface(
#     fn=grade, 
#     inputs=gr.File(file_types=[".json"]), 
#     outputs="file")


model_result_example = "https://raw.githubusercontent.com/ImAnonymousUser/MM-Vet/main/results/llava_llama2_13b_chat.json"

markdown = f"""
# MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities

We offer MM-Vet LLM-based (GPT-4) evaluator to grade open-ended outputs from your models.

Plese upload your json file of your model results containing `{{v1_0: ..., v1_1: ..., }}`like [this json file]({model_result_example}).

The grading results will be downloaded as a zip file.
"""


with gr.Blocks() as demo:
    gr.Markdown(markdown)
    with gr.Row():
        inp = gr.File(file_types=[".json"])
        out = gr.File(file_types=[".zip"])
    inp.change(grade, inp, out)


if __name__ == "__main__":
    demo.queue().launch()