app.py

import gradio as gr
from Sample import *

display_dict, info_dict = sample_random_entry(n=1)

# Extract the question text
question_text = display_dict['qa']

def markdown_to_html(markdown_text):
    # You would use a Markdown library to convert markdown to HTML here
    # Since this code runs in an environment without extra libraries, this is a placeholder
    html = markdown_text.replace("\n", "<br>")  # Simple replacement, not a real markdown conversion
    return html

completion_text = ''

def evaluate_guess(reasoning, correctness, confidence):
    
    # Placeholder for comparison logic
    # You'll need to access the correct answer from `info_dict` or a similar structure
    correct_answer = "Correctly"  # Placeholder for the actual logic to determine this
    evaluation_response = "Correct" if correctness == correct_answer else "Incorrect"

    actual_model = info_dict['model']
    actual_completion = info_dict['completion']
    
    # Update the completion text
    completion_text = f"Completion: {actual_completion}\n\nChoice: {chr(info_dict['verdict'] + 65)}"
    model = actual_model
    # Return the evaluation response and the completion text to update the interface
    return evaluation_response, model, completion_text
    

print(display_dict['card'])


with gr.Blocks() as app:
    with gr.Row():
        with gr.Column(scale=2):  # This column is wider
            # Use a Textbox to display the evaluation card content
            evaluation_card = gr.Textbox(value=display_dict['card'], label="Evaluation Card", interactive=False)
            model = gr.Textbox(value="", label="Model", placeholder='An anonymous Model', interactive=False)
            completion = gr.Textbox(value="", label="Model's Completion", interactive=False)
            
        with gr.Column(scale=1):
            # Display the sampled question in a Textbox
            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")  # Create an output text component
            submit_button = gr.Button("Submit")

            # This textbox will be used to display the model's completion
      

    # When the button is clicked, it will update the content of the completion textbox
    submit_button.click(fn=evaluate_guess, inputs=[reasoning, correctness, confidence], outputs=[output_text, model, completion])

app.launch()