app.py

import gradio as gr
import random
from threading import Thread
from queue import Queue

# Import our new modules
import config
import backend

# --- HELPER FUNCTIONS (Unchanged) ---
def get_random_question(domain):
    data_conf = config.DATASET_CONFIG[domain]
    dataset = data_conf["dataset"]
    
    if not dataset:
        return "Failed to load dataset.", "N/A"
        
    random_index = random.randint(0, len(dataset) - 1)
    sample = dataset[random_index]
    
    if domain == "Math":
        question = sample[data_conf["question_col"]]
        answer = sample[data_conf["answer_col"]]
    elif domain == "Bio":
        instruction = sample[data_conf["instruction_col"]]
        bio_input = sample[data_conf["input_col"]]
        answer = sample[data_conf["answer_col"]]
        if bio_input and bio_input.strip():
            question = f"**Instruction:**\n{instruction}\n\n**Input:**\n{bio_input}"
        else:
            question = instruction
            
    return question, answer

def update_domain_settings(domain):
    models = list(config.ALL_MODELS[domain].keys())
    def_base = next((m for m in models if "Base" in m), models[0])
    def_ft = next((m for m in models if "Finetuned" in m), models[0])
    
    q, a = get_random_question(domain)
    return [
        gr.Dropdown(choices=models, value=def_base),
        gr.Dropdown(choices=models, value=def_ft),
        gr.Textbox(value=q),
        a,
        gr.Markdown(visible=False)
    ]

def load_next_question(domain):
    q, a = get_random_question(domain)
    return [gr.Textbox(value=q), a, gr.Markdown(visible=False, value="")]

def reveal_answer(hidden_answer):
    return gr.Markdown(value=f"**Ground Truth Answer:**\n\n{hidden_answer}", visible=True)

# --- CORE LOGIC (REBUILT FOR TRUE PARALLEL STREAMING) ---

def stream_to_queue(model_id, prompt, lane, queue, key):
    """

    A worker function that runs in a thread.

    It calls the streaming API and puts tokens into the queue.

    """
    try:
        # call_modal_api is a generator
        for token in backend.call_modal_api(model_id, prompt, lane):
            queue.put((key, token))
    except Exception as e:
        queue.put((key, f"\n\nTHREAD ERROR: {e}"))
    finally:
        # When the stream is done, put a 'None' sentinel
        queue.put((key, None))

def run_comparison(domain, question, model_1_name, model_2_name):
    # 1. Get IDs
    id_1 = config.ALL_MODELS[domain].get(model_1_name)
    id_2 = config.ALL_MODELS[domain].get(model_2_name)
    
    # 2. Ask the Smart Router
    lane_for_m1, lane_for_m2 = backend.router.get_routing_plan(id_1, id_2)
    
    # 3. Create the Queue and Threads
    q = Queue()
    
    Thread(
        target=stream_to_queue, 
        args=(id_1, question, lane_for_m1, q, 'm1')
    ).start()
    
    Thread(
        target=stream_to_queue, 
        args=(id_2, question, lane_for_m2, q, 'm2')
    ).start()

    # 4. Listen to the Queue
    text1 = ""
    text2 = ""
    m1_done = False
    m2_done = False
    
    # Clear boxes and start
    yield "", "", gr.Markdown(visible=False)

    while not (m1_done and m2_done):
        # Wait for the next token from *either* thread
        try:
            key, token = q.get()
        except Exception as e:
            # This should ideally not happen
            print(f"Queue error: {e}")
            continue

        # Check for the 'None' sentinel
        if token is None:
            if key == 'm1':
                m1_done = True
            elif key == 'm2':
                m2_done = True
        else:
            # Append the new token
            if key == 'm1':
                text1 += token
            elif key == 'm2':
                text2 += token
        
        # Yield the updated full text
        yield text1, text2, gr.Markdown(visible=False)


# --- UI BUILD (Unchanged) ---
initial_question, initial_answer = get_random_question("Math")

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """

        # 🔬 LLM Finetuning Arena

        ### Comparing Finetuned vs. Base Models on Specialized Tasks

        """
    )
    
    hidden_answer_state = gr.State(value=initial_answer)
    
    with gr.Row():
        domain_radio = gr.Radio(
            ["Math", "Bio"], label="1. Select Domain", value="Math"
        )
    
    with gr.Row():
        question_box = gr.Textbox(
            label="2. Question Prompt (Editable)", 
            value=initial_question, lines=5, scale=4
        )
        next_btn = gr.Button("Load Random Question 🔄", scale=1, min_width=100)
        
    with gr.Row():
        model_1_dd = gr.Dropdown(
            label="3. Select Model 1 (Left)", 
            choices=list(config.ALL_MODELS["Math"].keys()),
            value=next((m for m in config.ALL_MODELS["Math"] if "Base" in m))
        )
        model_2_dd = gr.Dropdown(
            label="4. Select Model 2 (Right)", 
            choices=list(config.ALL_MODELS["Math"].keys()),
            value=next((m for m in config.ALL_MODELS["Math"] if "Finetuned" in m))
        )
        
    with gr.Row():
        run_btn = gr.Button("🚀 Run Comparison", variant="primary", scale=3)
        show_answer_btn = gr.Button("Show Ground Truth Answer", scale=1)

    answer_display_box = gr.Markdown(label="Ground Truth Answer", visible=False)
    
    gr.Markdown("---")
    
    with gr.Row():
        output_1_box = gr.Markdown(label="Output: Model 1")
        output_2_box = gr.Markdown(label="Output: Model 2")

    # --- EVENTS (Unchanged) ---
    domain_radio.change(
        fn=update_domain_settings,
        inputs=[domain_radio],
        outputs=[model_1_dd, model_2_dd, question_box, hidden_answer_state, answer_display_box]
    )
    
    next_btn.click(
        fn=load_next_question,
        inputs=[domain_radio],
        outputs=[question_box, hidden_answer_state, answer_display_box]
    )
    
    show_answer_btn.click(
        fn=reveal_answer,
        inputs=[hidden_answer_state],
        outputs=[answer_display_box]
    )
    
    run_btn.click(
        fn=run_comparison,
        inputs=[domain_radio, question_box, model_1_dd, model_2_dd],
        outputs=[output_1_box, output_2_box, answer_display_box]
    )

if __name__ == "__main__":
    if not config.MY_AUTH_TOKEN:
        print("⚠️ WARNING: ARENA_AUTH_TOKEN is not set.")
    demo.launch()