app.py

from fastapi import FastAPI
import gradio as gr
import subprocess
import sys
import os
import threading
import time
import uuid
import glob
import shutil
from pathlib import Path
from apscheduler.schedulers.background import BackgroundScheduler
import signal
import uvicorn


default_command = "bigcodebench.evaluate"
is_running = False
lock = threading.Lock()
process = None

app = FastAPI()

def generate_command(
    jsonl_file, split, subset, parallel,
    min_time_limit, max_as_limit, max_data_limit, max_stack_limit,
    check_gt_only, no_gt
):
    command = [default_command]
    
    if jsonl_file is not None:
        # Copy the uploaded file to the current directory
        local_filename = os.path.basename(jsonl_file.name)
        shutil.copy(jsonl_file.name, local_filename)
        command.extend(["--samples", local_filename])
    
    command.extend(["--split", split, "--subset", subset])
    
    if parallel is not None and parallel != 0:
        command.extend(["--parallel", str(int(parallel))])
    
    command.extend([
        "--min-time-limit", str(min_time_limit),
        "--max-as-limit", str(int(max_as_limit)),
        "--max-data-limit", str(int(max_data_limit)),
        "--max-stack-limit", str(int(max_stack_limit))
    ])
    
    if check_gt_only:
        command.append("--check-gt-only")
    
    if no_gt:
        command.append("--no-gt")
    
    return " ".join(command)


def cleanup_previous_files(jsonl_file):
    if jsonl_file is not None:
        file_list = ['Dockerfile', 'app.py', 'README.md', os.path.basename(jsonl_file.name), "__pycache__"]
    else:
        file_list = ['Dockerfile', 'app.py', 'README.md', "__pycache__"]
    for file in glob.glob("*"):
        try:
            if file not in file_list:
                os.remove(file)
        except Exception as e:
            print(f"Error during cleanup of {file}: {e}")

def find_result_file():
    json_files = glob.glob("*.json")
    if json_files:
        return max(json_files, key=os.path.getmtime)
    return None

def run_bigcodebench(command):
    global is_running, process
    with lock:
        if is_running:
            yield "A command is already running. Please wait for it to finish.\n"
            return
        is_running = True

    try:
        yield f"Executing command: {command}\n"
        
        process = subprocess.Popen(command.split(), stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True, preexec_fn=os.setsid)
        
        for line in process.stdout:
            yield line
        
        process.wait()
        
        if process.returncode != 0:
            yield f"Error: Command exited with status {process.returncode}\n"
        
        yield "Evaluation completed.\n"
        
        result_file = find_result_file()
        if result_file:
            yield f"Result file found: {result_file}\n"
        else:
            yield "No result file found.\n"
    finally:
        with lock:
            is_running = False
            process = None

def kill_process():
    global process
    if process:
        os.killpg(os.getpgid(process.pid), signal.SIGTERM)
        process = None

def stream_logs(command, jsonl_file=None):
    global is_running
        
    if is_running:
        yield "A command is already running. Please wait for it to finish.\n"
        return
    
    cleanup_previous_files(jsonl_file)
    yield "Cleaned up previous files.\n"

    log_content = []
    for log_line in run_bigcodebench(command):
        log_content.append(log_line)
        yield "".join(log_content)
        
with gr.Blocks() as demo:
    gr.Markdown("# BigCodeBench Evaluator")
    
    with gr.Row():
        jsonl_file = gr.File(label="Upload JSONL file", file_types=[".jsonl"])
        split = gr.Dropdown(choices=["complete", "instruct"], label="Split", value="complete")
        subset = gr.Dropdown(choices=["hard"], label="Subset", value="hard")
    
    with gr.Row():
        parallel = gr.Number(label="Parallel (optional)", precision=0)
        min_time_limit = gr.Number(label="Min Time Limit", value=1, precision=1)
        max_as_limit = gr.Number(label="Max AS Limit", value=25*1024, precision=0)
    
    with gr.Row():
        max_data_limit = gr.Number(label="Max Data Limit", value=25*1024, precision=0)
        max_stack_limit = gr.Number(label="Max Stack Limit", value=10, precision=0)
        check_gt_only = gr.Checkbox(label="Check GT Only")
        no_gt = gr.Checkbox(label="No GT")
    
    kill_process_btn = gr.Button("Kill Process", visible=False)
    kill_process_btn.click(kill_process)

    # Add this JavaScript to handle window closing
    gr.HTML("""
    <script>
    window.addEventListener('beforeunload', function (e) {
        fetch('/kill_process', {
            method: 'POST',
            headers: {
                'Content-Type': 'application/json',
            },
            body: JSON.stringify({}),
        });
    });
    </script>
    """)
    
    command_output = gr.Textbox(label="Command", value=default_command, interactive=False)
    with gr.Row():
        submit_btn = gr.Button("Run Evaluation")
        download_btn = gr.DownloadButton(label="Download Result")
    log_output = gr.Textbox(label="Execution Logs", lines=20)
    
    input_components = [
        jsonl_file, split, subset, parallel,
        min_time_limit, max_as_limit, max_data_limit, max_stack_limit,
        check_gt_only, no_gt
    ]
    
    for component in input_components:
        component.change(generate_command, inputs=input_components, outputs=command_output)
        
    
    def start_evaluation(command, jsonl_file, subset, split):
        extra = subset + "_" if subset != "full" else ""
        if jsonl_file is not None:
            result_path = os.path.basename(jsonl_file.name).replace(".jsonl", f"_{extra}eval_results.json")
        else:
            result_path = None

        for log in stream_logs(command, jsonl_file):
            if jsonl_file is not None:
                yield log, gr.update(value=result_path, label=result_path), gr.update()
            else:
                yield log, gr.update(), gr.update()
        is_running = False
        result_file = find_result_file()
        if result_file:
            return gr.update(label="Evaluation completed. Result file found."), gr.update(value=result_file)
                    # gr.Button(visible=False)#,
                    # gr.DownloadButton(label="Download Result", value=result_file, visible=True))
        else:
            return gr.update(label="Evaluation completed. No result file found."), gr.update(value=result_path)
                    # gr.Button("Run Evaluation", visible=True),
                    # gr.DownloadButton(visible=False))
    submit_btn.click(start_evaluation,
                 inputs=[command_output, jsonl_file, subset, split],
                 outputs=[log_output, download_btn])

@app.post("/kill_process")
async def api_kill_process():
    kill_process()
    return {"status": "success"}

# demo.queue(max_size=300).launch(
#     share=True, 
#     server_name="0.0.0.0", 
#     server_port=7860,
#     additional_routes={"/kill_process": kill_process_api}
# )
app = gr.mount_gradio_app(app, demo, path="/gradio")
uvicorn.run(app, host="0.0.0.0", port=7860)
scheduler = BackgroundScheduler()