app.py

import gradio as gr
from Sample import sample_random_entry
from Config import TOPICS
import pandas as pd
import os
from threading import Lock

lock = Lock()
info_dict = {}

def append_to_csv(output_path, row_data, header_names):
    # Acquire the lock before accessing the file
    with lock:
        # Check if file exists and is not empty
        if os.path.exists(output_path) and os.path.getsize(output_path) > 0:
            print(f"Appending to {output_path}")
            # File exists and is not empty, append without headers
            df = pd.DataFrame([row_data])
            df.to_csv(output_path, mode='a', header=False, index=False)
        else:
            print(f"Writing to {output_path}")
            # File does not exist or is empty, write with headers
            df = pd.DataFrame([row_data], columns=header_names)
            df.to_csv(output_path, mode='w', header=True, index=False)

def sample_and_display(topic):
    # If a topic is selected, use it to sample a new entry
    global info_dict
    display_dict, info_dict = sample_random_entry(topic=topic) if topic else sample_random_entry()
    question_text = display_dict['qa']
    evaluation_card_text = display_dict['card']
    model_name = ''  # Clear the model name
    completion_text = ''  # Clear the completion text
    return question_text, evaluation_card_text, model_name, completion_text

def evaluate_guess(reasoning, correctness, confidence, topic):
    global info_dict
    # Here your logic will go to evaluate the guess
    # Placeholder for the correct logic to determine the correct answer
    correct_answer = 'Correctly' if info_dict['correctness'] else 'Incorrectly'
    evaluation_response = "Correct" if correctness == correct_answer else "Incorrect"
    
    # Assuming info_dict is updated by sample_and_display function
    actual_model = info_dict.get('model', 'Unknown Model')
    actual_completion = info_dict.get('completion', 'No completion available.')
    
    # Update the completion text
    completion_text = f"Completion: {actual_completion}\n\nChoice: {chr(info_dict.get('verdict', 0) + 65)}"

    question_index = info_dict.get('index', -1)
    question_topic = topic
    output_path = f'responses/mmlu/{question_topic}/response.csv'
    entry = dict()

    entry['index'] = question_index
    entry['model'] = actual_model
    entry['reasoning'] = reasoning
    entry['correctness'] = correctness == correct_answer
    entry['confidence'] = confidence

    header_names = ['index', 'model', 'reasoning', 'correctness', 'confidence']  # Add other headers as necessary   

    append_to_csv(output_path, entry, header_names)


    return evaluation_response, actual_model, completion_text

# Initial sampling
initial_topic = TOPICS['mmlu'][0]  # Assuming TOPICS is a list of topics
question_text, evaluation_card_text, model_name, completion_text = sample_and_display(initial_topic)

with gr.Blocks() as app:
    topic = gr.Dropdown(choices=TOPICS['mmlu'], label="Select Topic", value=initial_topic)
    with gr.Row():
        with gr.Column(scale=2):
            evaluation_card = gr.Textbox(value=evaluation_card_text, label="Evaluation Card", interactive=False)
            model = gr.Textbox(value=model_name, label="Model", interactive=False)
            completion = gr.Textbox(value=completion_text, label="Model's Completion", interactive=False)
        with gr.Column(scale=1):
            question = gr.Textbox(value=question_text, label="Question", interactive=False)
            reasoning = gr.Textbox(lines=5, placeholder="Your reasoning (optional)")
            correctness = gr.Radio(choices=["Correct", "Incorrect"], label="I believe the model will answer this question")
            confidence = gr.Slider(minimum=0, maximum=10, step=1, label="Confidence")
            output_text = gr.Text(label="Evaluation Output")
            submit_button = gr.Button("Submit")
            next_button = gr.Button("Next Entry")
    
    submit_button.click(fn=evaluate_guess, inputs=[reasoning, correctness, confidence, topic], outputs=[output_text, model, completion])
    next_button.click(fn=sample_and_display, inputs=[topic], outputs=[question, evaluation_card, model, completion])

app.launch()