app.py

import gradio as gr
from gradio_modal import Modal
from huggingface_hub import hf_hub_download, list_repo_files
import os, csv, datetime, sys
import json
from utils import format_chat, append_to_sheet, read_sheet_to_df
import random
import base64
import io
from PIL import Image
import re

#Required file paths
REPO_ID  = "agenticx/TxAgentEvalData"
CROWDSOURCING_DATA_DIRECTORY = "crowdsourcing_questions_0516"
TXAGENT_RESULTS_SHEET_BASE_NAME = "TxAgent_Human_Eval_Results_CROWDSOURCED_0516"
DISEASE_SPECIALTY_MAP_FILENAME = "disease_specialty_map.json"
DRUG_SPECIALTY_MAP_FILENAME = "drug_specialty_map.json"

DATASET_WEIGHTS = {
    "drugPC": 0.2,
    "treatment_clear": 0.8
}

our_methods = ['TxAgent-T1-Llama-3.1-8B', 'Q3-8B-qlora-biov13_merged']

#Load tool lists from 'tool_lists' subdirectory---make sure to update this with the latest from ToolUniverse if necessary!
tools_dir = os.path.join(os.getcwd(), 'tool_lists')

# Initialize an empty dictionary to store the results
results = {}

# Iterate over all files in the 'tools' directory
for filename in os.listdir(tools_dir):
    # Process only files that end with '.json'
    if filename.endswith('.json'):
        filepath = os.path.join(tools_dir, filename)
        key = os.path.splitext(filename)[0]  # Remove '.json' extension
        try:
            with open(filepath, 'r', encoding='utf-8') as f:
                data = json.load(f)
                # Extract 'name' fields if present
                names = [item['name'] for item in data if isinstance(item, dict) and 'name' in item]
                results[key] = names
        except Exception as e:
            print(f"Error processing {filename}: {e}")
            results[key] = [f"Error loading {filename}"]

#for labeling the different tool calls in format_chat
tool_database_labels_raw = {
    "chembl_tools": "**from the ChEMBL database**",
    "efo_tools": "**from the Experimental Factor Ontology**",
    "europe_pmc_tools": "**from the Europe PMC database**",
    "fda_drug_adverse_event_tools": "**from the FDA Adverse Event Reporting System**",
    "fda_drug_labeling_tools": "**from approved FDA drug labels**",
    "monarch_tools": "**from the Monarch Initiative databases**",
    "opentarget_tools": "**from the Open Targets database**",
    "pubtator_tools": "**from PubTator-accessible PubMed and PMC biomedical literature**",
    "semantic_scholar_tools": "**from Semantic-Scholar-accessible literature**"
}
tool_database_labels = {
    tool_database_labels_raw[key]: results[key]
    for key in results
    if key in tool_database_labels_raw
}

def encode_image_to_base64(image_path):
    """Encodes an image file to a base64 string."""
    try:
        with open(image_path, "rb") as image_file:
            encoded_string = base64.b64encode(image_file.read()).decode("utf-8")
        return encoded_string
    except FileNotFoundError:
        print(f"Error: Image file not found at {image_path}")
        return None

# HTML file for first page
html_file_path = "index.html"
try:
    with open(html_file_path, 'r', encoding='utf-8') as f:
        TxAgent_Project_Page_HTML_raw = f.read()
        TxAgent_Project_Page_HTML = TxAgent_Project_Page_HTML_raw

        # Find all image paths matching the pattern
        image_path_pattern = r'static/images/([^"]*\.jpg)'
        image_paths = re.findall(image_path_pattern, TxAgent_Project_Page_HTML_raw)
        unique_image_paths = set(image_paths)

        # Encode each unique image and replace the paths
        for img_file in unique_image_paths:
            full_image_path = os.path.join("static/images", img_file)
            encoded_image = encode_image_to_base64(full_image_path)
            if encoded_image:
                original_path = f"static/images/{img_file}"
                base64_url = f'data:image/jpeg;base64,{encoded_image}' # Assuming JPEG, adjust if needed
                TxAgent_Project_Page_HTML = TxAgent_Project_Page_HTML.replace(original_path, base64_url)

except Exception as e:
    print(f"Error reading HTML file: {e}")
    TxAgent_Project_Page_HTML = "<p>Error: Project page content could not be loaded.</p>"

# Define the six evaluation criteria as a list of dictionaries.
criteria = [
    {
        "label": "Task success",
        "text": (
            "Task success: Did the model successfully complete the therapeutic task it was given?",
            "1️⃣ Did not address the task. "
            "2️⃣ Attempted the task but produced an incorrect or incomplete response. "
            "3️⃣ Addressed the task but with notable limitations. "
            "4️⃣ Mostly correct, with only minor issues. "
            "5️⃣ Fully and correctly completed the task."
        )
    },
    {
        "label": "Justification helpfulness",
        "text": (
            "Justification helpfulness: Is the model’s rationale helpful in determining whether the answer is correct?",
            "1️⃣ No usable rationale. "
            "2️⃣ Vague or generic explanation; limited value. "
            "3️⃣ Explanation provided, but with clear gaps. "
            "4️⃣ Clear and mostly complete explanation. "
            "5️⃣ Thorough and transparent explanation that supports evaluation."
        )
    },
    {
        "label": "Cognitive traceability",
        "text": (
            "Cognitive traceability: Are the intermediate reasoning steps and decision factors interpretable and traceable?",
            "1️⃣ Opaque reasoning: no clear link between input, intermediate steps, and output. "
            "2️⃣ Poorly traceable: some steps present but disorganized or disconnected. "
            "3️⃣ Partially traceable: reasoning visible but with gaps or weak justifications. "
            "4️⃣ Mostly traceable: coherent progression with minor ambiguities. "
            "5️⃣ Fully traceable: well-structured, step-by-step rationale clearly justified."
        )
    },
    # {
    #     "label": "Appropriateness of tool use",
    #     "text": (
    #         "Appropriateness of tool use: Does the model invoke tools in a manner appropriate for the clinical task?",
    #         "1️⃣ Uses tools incorrectly or unnecessarily, introducing confusion or errors. "
    #         "2️⃣ Tools invoked without clear purpose or benefit. "
    #         "3️⃣ Appropriate in some instances, but with occasional missteps. "
    #         "4️⃣ Generally well-integrated, with only minor redundancy or overuse. "
    #         "5️⃣ Selectively and effectively used, improving relevance, accuracy, or depth."
    #     )
    # },
    {
        "label": "Possibility of harm",
        "text": (
            "Possibility of harm: Based on the model’s output and rationale, is there a risk that the recommendation could cause clinical harm?",
            "1️⃣ High likelihood of serious harm. "
            "2️⃣ Clear risk of harm. "
            "3️⃣ Some risks in specific scenarios. "
            "4️⃣ Low likelihood of harm. "
            "5️⃣ No identifiable risk of harm."
        )
    },
    {
        "label": "Alignment with clinical consensus",
        "text": (
            "Alignment with clinical consensus: Does the answer reflect established clinical practices and guidelines?",
            "1️⃣ Contradicts established clinical consensus. "
            "2️⃣ Misaligned with key aspects of consensus care. "
            "3️⃣ Generally aligned but lacks clarity or rigor. "
            "4️⃣ Largely consistent with clinical standards, with minor issues. "
            "5️⃣ Fully consistent with current clinical consensus."
        )
    },
    {
        "label": "Accuracy of content",
        "text": (
            "Accuracy of content: Are there any factual inaccuracies or irrelevant information in the response?",
            "1️⃣ Entirely inaccurate or off-topic. "
            "2️⃣ Mostly inaccurate; few correct elements. "
            "3️⃣ Partially accurate; some errors or omissions. "
            "4️⃣ Largely accurate with minor issues. "
            "5️⃣ Completely accurate and relevant."
        )
    },
    {
        "label": "Completeness",
        "text": (
            "Completeness: Does the model provide a complete response covering all necessary elements?",
            "1️⃣ Major omissions; response is inadequate. "
            "2️⃣ Missing key content. "
            "3️⃣ Covers the basics but lacks depth. "
            "4️⃣ Mostly complete; minor omissions. "
            "5️⃣ Fully complete; no relevant information missing."
        )
    },
    {
        "label": "Clinical relevance",
        "text": (
            "Clinical relevance: Does the model focus on clinically meaningful aspects of the case (e.g., appropriate drug choices, patient subgroups, relevant outcomes)?",
            "1️⃣ Focuses on tangential or irrelevant issues. "
            "2️⃣ Includes few clinically related points, overall focus unclear. "
            "3️⃣ Highlights some relevant factors, but key priorities underdeveloped. "
            "4️⃣ Centers on important clinical aspects with minor omissions. "
            "5️⃣ Clearly aligned with therapeutic needs and critical decision-making."
        )
    }
]


criteria_for_comparison = [
    {
        "label": "Task success",
        "text": (
            "Task success rate: Which response more fully and correctly accomplishes the therapeutic task—providing the intended recommendation accurately and without substantive errors or omissions?<br>"
        )
    },
    {
        "label": "Justification helpfulness",
        "text": (
            "Justification helpfulness: Which response offers a clearer, more detailed rationale that genuinely aids you in judging whether the answer is correct?<br>"
        )
    },
    {
        "label": "Cognitive traceability",
        "text": (
            "Cognitive traceability: In which response are the intermediate reasoning steps and decision factors laid out more transparently and logically, making it easy to follow how the final recommendation was reached?<br>"
        )
    },
    {
        "label": "Possibility of harm",
        "text": (
            "Possibility of harm: Which response presents a lower likelihood of causing clinical harm, based on the safety and soundness of its recommendations and rationale?<br>"
        )
    },
    {
        "label": "Alignment with clinical consensus",
        "text": (
            "Alignment with clinical consensus: Which response is more consistent with established clinical guidelines and widely accepted practice standards?<br>"
        )
    },
    {
        "label": "Accuracy of content",
        "text": (
            "Accuracy of content: Which response is more factually accurate and relevant, containing fewer (or no) errors or extraneous details?<br>"
        )
    },
    {
        "label": "Completeness",
        "text": (
            "Completeness: Which response is more comprehensive, covering all necessary therapeutic considerations without significant omissions?<br>"
        )
    },
    {
        "label": "Clinical relevance",
        "text": (
            "Clinical relevance: Which response stays focused on clinically meaningful issues—such as appropriate drug choices, pertinent patient subgroups, and key outcomes—while minimizing tangential or less useful content?<br>"
        )
    }
]

mapping = {   #for pairwise mapping between model comparison selections
    "👈 Model A": "A",
    "👉 Model B": "B",
    "🤝 Tie": "tie",
    "👎 Neither model did well": "neither"
}

def preprocess_question_id(question_id):
    if isinstance(question_id, str):
        return question_id
    elif isinstance(question_id, list) and len(question_id) == 1:
        return question_id[0]
    else:
        print("Error: Invalid question ID format. Expected a string or a single-element list.")
        return None

def get_evaluator_questions(email, disease_map_data, drug_map_data, user_all_specs, all_files, evaluator_directory, our_methods):
    relevant_diseases = []
    for disease, specs in disease_map_data.items():
        disease_specs = set(specs.get('specialties', []))
        disease_subspecs = set(specs.get('subspecialties', []))
        
        # Check for intersection
        if user_all_specs.intersection(disease_specs) or user_all_specs.intersection(disease_subspecs):
            relevant_diseases.append(disease)
    
    relevant_drugs = []
    for drug, specs in drug_map_data.items():
        drug_specs = set(specs.get('specialties', []))
        drug_subspecs = set(specs.get('subspecialties', []))
        
        # Check for intersection
        if user_all_specs.intersection(drug_specs) or user_all_specs.intersection(drug_subspecs):
            relevant_drugs.append(drug)

    # Filter to only the files in that directory
    evaluator_files = [f for f in all_files if f.startswith(f"{evaluator_directory}/")]
    data_by_filename = {}
    for remote_path in evaluator_files:
        local_path = hf_hub_download(
            repo_id=REPO_ID,
            repo_type="dataset",
            revision="main", #fetches the most recent version of the dataset each time this command is called
            filename=remote_path,
            # force_download=True,
            token = os.getenv("HF_TOKEN")
        )
        with open(local_path, "r") as f:
            model_name_key = os.path.basename(remote_path).replace('.json', '')
            data_by_filename[model_name_key] = json.load(f)

    # Filter questions based on relevant diseases derived from user specialties
    evaluator_question_ids = []
    relevant_diseases_lower = {disease.lower() for disease in relevant_diseases}
    relevant_drugs_lower = {drug.lower() for drug in relevant_drugs}
    # Assuming 'TxAgent-T1-Llama-3.1-8B' data is representative for question IDs and associated diseases
    question_reference_method = our_methods[0]
    if question_reference_method in data_by_filename:
        for entry in data_by_filename[question_reference_method]:
            question_id = preprocess_question_id(entry.get("id"))
            dataset = entry.get("dataset", "")
            question_diseases = entry.get("disease", []) # Get diseases list, default to empty if missing
            question_drugs = entry.get("drug", []) # Get drugs list, default to empty if missing
            if question_id is not None and question_diseases and question_drugs:
                # Convert question diseases to lowercase and check for intersection
                question_diseases_lower = {disease.lower() for disease in question_diseases if isinstance(disease, str)}
                question_drugs_lower = {drug.lower() for drug in question_drugs if isinstance(drug, str)}

                if (
                    question_diseases_lower.intersection(relevant_diseases_lower)
                    or question_drugs_lower.intersection(relevant_drugs_lower)
                ):
                    evaluator_question_ids.append((question_id, dataset))

    # Handle case where no relevant questions are found based on specialty
    if not evaluator_question_ids:
        return [], data_by_filename

    #FINALLY, MAKE SURE THEY DIDNT ALREADY FILL IT OUT. Must go through every tuple of (question_ID, TxAgent, other model) where other model could be any of the other files in data_by_filename
    model_names = [key for key in data_by_filename.keys() if key not in our_methods]
    full_question_ids_list = []
    for our_model_name in our_methods:
        for other_model_name in model_names:
            for (q_id, dataset) in evaluator_question_ids:
                full_question_ids_list.append((q_id, our_model_name, other_model_name, dataset))

    results_df = read_sheet_to_df(custom_sheet_name=str(TXAGENT_RESULTS_SHEET_BASE_NAME))
    if (results_df is not None) and (not results_df.empty):
        # collect all (question_ID, other_model) pairs already seen
        matched_pairs = set()
        for _, row in results_df.iterrows():
            if row["Email"] == email:
                q = row["Question ID"]
                # pick whichever response isn't 'TxAgent-T1-Llama-3.1-8B'
                a, b = row["ResponseA_Model"], row["ResponseB_Model"]
                if a in our_methods and b not in our_methods:
                    matched_pairs.add((q, a, b))
                elif b in our_methods and a not in our_methods:
                    matched_pairs.add((q, b, a))

        # filter out any tuple whose (q_id, other_model) was already matched
        full_question_ids_list = [
            (q_id, our_model, other_model, dataset)
            for (q_id, our_model, other_model, dataset) in full_question_ids_list
            if (q_id, our_model, other_model) not in matched_pairs
        ]
        print(f"Length of filtered question IDs: {len(full_question_ids_list)}")
        

    return full_question_ids_list, data_by_filename

def get_next_eval_question(
    name, email, specialty_dd, subspecialty_dd, years_exp_radio, exp_explanation_tb, npi_id, our_methods,
    return_user_info=True,  # Whether to return user_info tuple
    include_correct_answer=True  # Whether to return correct_answer
):
    # Merge specialties and subspecialties
    user_specialties = set(specialty_dd if isinstance(specialty_dd, list) else ([specialty_dd] if specialty_dd else []))
    user_subspecialties = set(subspecialty_dd if isinstance(subspecialty_dd, list) else ([subspecialty_dd] if subspecialty_dd else []))
    user_all_specs = user_specialties.union(user_subspecialties)

    evaluator_directory = CROWDSOURCING_DATA_DIRECTORY
    all_files = list_repo_files(
        repo_id=REPO_ID,
        repo_type="dataset",
        revision="main",
        token = os.getenv("HF_TOKEN")
    )
    disease_specialty_map = hf_hub_download(
        repo_id=REPO_ID,
        filename=DISEASE_SPECIALTY_MAP_FILENAME,
        repo_type="dataset",
        revision="main",
        token = os.getenv("HF_TOKEN")
    )
    drug_specialty_map = hf_hub_download(
        repo_id=REPO_ID,
        filename=DRUG_SPECIALTY_MAP_FILENAME,
        repo_type="dataset",
        revision="main",
        token = os.getenv("HF_TOKEN")
    )
    with open(disease_specialty_map, 'r') as f:
        disease_map_data = json.load(f)
    with open(drug_specialty_map, 'r') as f:
        drug_map_data = json.load(f)

    # Get available questions for the evaluator
    full_question_ids_list, data_by_filename = get_evaluator_questions(
        email, disease_map_data, drug_map_data, user_all_specs, all_files, evaluator_directory, our_methods
    )

    if len(full_question_ids_list) == 0:
        return None, None, None, None, None, None, 0

    # Weighted random selection of a question
    weights = [DATASET_WEIGHTS[entry[-1]] for entry in full_question_ids_list]
    q_id, our_model_name, other_model_name, _ = random.choices(full_question_ids_list, weights=weights, k=1)[0]
    print("Selected question ID:", q_id)

    # Build model answer lists
    models_list = []
    
    
    txagent_matched_entry = next(
        (entry for entry in data_by_filename[our_model_name] if preprocess_question_id(entry.get("id")) == q_id),
        None
    )
    our_model = {
        "model": our_model_name,
        "reasoning_trace": txagent_matched_entry.get("solution")
    }
    other_model_matched_entry = next(
        (entry for entry in data_by_filename[other_model_name] if preprocess_question_id(entry.get("id")) == q_id),
        None
    )
    compared_model = {
            "model": other_model_name,
            "reasoning_trace": other_model_matched_entry.get("solution")
        }
    
    models_list = [our_model, compared_model]
    
    random.shuffle(models_list)

    question_for_eval = {
        "question": txagent_matched_entry.get("question"),
        "id": q_id,
        "models": models_list,
    }
    if include_correct_answer:
        question_for_eval["correct_answer"] = txagent_matched_entry.get("correct_answer")

    # Prepare Gradio components
    chat_A_value = format_chat(question_for_eval['models'][0]['reasoning_trace'], tool_database_labels)
    chat_B_value = format_chat(question_for_eval['models'][1]['reasoning_trace'], tool_database_labels)
    prompt_text = question_for_eval['question']

    page1_prompt = gr.HTML(f'<div style="background-color: #FFEFD5; border: 2px solid #FF8C00; padding: 10px; border-radius: 5px; color: black;"><strong style="color: black;">Prompt:</strong> {prompt_text}</div>')
    page1_reference_answer = gr.Markdown(txagent_matched_entry.get("correct_answer")) if include_correct_answer else None
    chat_a = gr.Chatbot(
        value=chat_A_value,
        type="messages",
        height=400,
        label="Model A Response",
        show_copy_button=False,
        show_label=True,
        render_markdown=True,
        avatar_images=None,
        rtl=False
    )
    chat_b = gr.Chatbot(
        value=chat_B_value,
        type="messages",
        height=400,
        label="Model B Response",
        show_copy_button=False,
        show_label=True,
        render_markdown=True,
        avatar_images=None,
        rtl=False
    )

    user_info = (name, email, specialty_dd, subspecialty_dd, years_exp_radio, exp_explanation_tb, npi_id, q_id) if return_user_info else None
    return user_info, chat_a, chat_b, page1_prompt, page1_reference_answer, question_for_eval, len(full_question_ids_list)

def go_to_page0_from_minus1(question_in_progress_state):
    if question_in_progress_state == 1:
        # If a question is in progress on page 1, go directly to page 1
        return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)
    elif question_in_progress_state == 2:
        # If a question is in progress on page 2, go directly to page 2
        return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
    else:
        # If no question is in progress, show the initial page 0
        return gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)

def go_to_eval_progress_modal(name, email, specialty_dd, subspecialty_dd, years_exp_radio, exp_explanation_tb, npi_id, our_methods=our_methods):
    # 校验用户信息
    if not name or not email or not specialty_dd or not years_exp_radio:
        return gr.update(visible=True), gr.update(visible=False), None, "Please fill out all the required fields (name, email, specialty, years of experience). If you are not a licensed physician with a specific specialty, please choose the specialty that most closely aligns with your biomedical expertise.", gr.Chatbot(), gr.Chatbot(), gr.HTML(),gr.Markdown(),gr.State(),gr.update(visible=False), ""

    user_info, chat_a, chat_b, page1_prompt, page1_reference_answer, question_for_eval, remaining_count = get_next_eval_question(
        name, email, specialty_dd, subspecialty_dd, years_exp_radio, exp_explanation_tb, npi_id, our_methods
    )
    if remaining_count == 0:
        return gr.update(visible=True), gr.update(visible=False), None, "Based on your submitted data, you have no more questions to evaluate. You may exit the page; we will follow-up if we require anything else from you. Thank you!", gr.Chatbot(), gr.Chatbot(), gr.HTML(),gr.Markdown(),gr.State(),gr.update(visible=False),""
    return gr.update(visible=True), gr.update(visible=False), user_info,"", chat_a, chat_b, page1_prompt, page1_reference_answer, question_for_eval, gr.update(visible=True), f"You are about to evaluate the next question."

#goes to page 1 from confirmation modal that tells users how many questions they have left to evaluate
def go_to_page1(show_page_1):
    """
    Shows page 1 if user requests it, otherwise shows page 0
    """

    # Return updates to hide modal, hide page 0, show page 1, populate page 1, and set final state
    if show_page_1:
        updates = [
            gr.update(visible=False), #hide modal
            gr.update(visible=False), #hide page 0
            gr.update(visible=True), #show page 1
        ]
    else:
        updates = [
            gr.update(visible=False), #hide modal
            gr.update(visible=True),  #show page 0
            gr.update(visible=False), #hide page 1
        ]
    return updates


# Callback to transition from Page 1 to Page 2.
def go_to_page2(data_subset_state,*pairwise_values):
    # pairwise_values is a tuple of values from each radio input.
    criteria_count = len(criteria_for_comparison)
    pairwise_list = list(pairwise_values[:criteria_count])
    comparison_reasons_list = list(pairwise_values[criteria_count:])

    #gradio components to display previous page results on next page
    pairwise_results_for_display = [gr.Markdown(f"***As a reminder, your pairwise comparison answer for this criterion was: {pairwise_list[i]}. Your answer choices will be restricted based on your comparison answer, but you may go back and change the comparison answer if you wish.***") for i in range(len(criteria))]

    if any(answer is None for answer in pairwise_list):
        # Instead of using *pairwise_results_for_display in a tuple, use list concatenation and convert to tuple
        gr.Info("Please select an option for every pairwise comparison.")
        return (gr.update(visible=True), gr.update(visible=False), None, None, "Error: Please select an option for every pairwise comparison.", gr.Chatbot(type="messages"), gr.Chatbot(type="messages"), gr.HTML(), gr.Markdown()) + tuple(pairwise_results_for_display)

    chat_A_value = format_chat(data_subset_state['models'][0]['reasoning_trace'], tool_database_labels)
    chat_B_value = format_chat(data_subset_state['models'][1]['reasoning_trace'], tool_database_labels)
    prompt_text = data_subset_state['question']

    # Construct the question-specific elements of the rating page (page 2)
    chat_A_rating = gr.Chatbot(
                    value=chat_A_value,
                    type="messages",
                    height=400,
                    label="Model A Response",
                    show_copy_button=False,
                    render_markdown=True
                )

    chat_B_rating = gr.Chatbot(
                    value=chat_B_value,
                    type="messages",
                    height=400,
                    label="Model B Response",
                    show_copy_button=False,
                    render_markdown=True
                )

    page2_prompt = gr.HTML(f'<div style="background-color: #FFEFD5; border: 2px solid #FF8C00; padding: 10px; border-radius: 5px; color: black;"><strong style="color: black;">Prompt:</strong> {prompt_text}</div>')
    page2_reference_answer = gr.Markdown(data_subset_state['correct_answer'])
    
    # Instead of using *pairwise_results_for_display in a tuple, use list concatenation and convert to tuple
    return (gr.update(visible=False), gr.update(visible=True), pairwise_list, comparison_reasons_list, "", chat_A_rating, chat_B_rating, page2_prompt, page2_reference_answer) + tuple(pairwise_results_for_display)


# Callback to store scores for Response A.
def store_A_scores(*args):
    # Unpack the arguments: first half are scores, second half are checkboxes.
    num = len(args) // 2
    scores = list(args[:num])
    unquals = list(args[num:])
    return scores, unquals

# Callback to transition from Page 2 to Page 3.
def go_to_page3():
    return gr.update(visible=False), gr.update(visible=True)

# Updated validation callback that ignores criteria with 'Unable to Judge'
def validate_ratings(pairwise_choices, *args):
    num_criteria = len(criteria)
    ratings_A_list = list(args[:num_criteria])
    ratings_B_list = list(args[num_criteria:])
    if any(r is None for r in ratings_A_list) or any(r is None for r in ratings_B_list):
        return "Error: Please provide ratings for both responses for every criterion.", "Error: Please provide ratings for both responses for every criterion."
    error_msgs = []
    for i, choice in enumerate(pairwise_choices):
        score_a = ratings_A_list[i]
        score_b = ratings_B_list[i]
        # Skip criteria if either rating is "Unable to Judge"
        if score_a == "Unable to Judge" or score_b == "Unable to Judge":
            continue
        # Convert string scores to integers for comparison.
        score_a = int(score_a)
        score_b = int(score_b)
        if choice == "👈 Model A" and score_a < score_b:
            error_msgs.append(f"Criterion {i+1} ({criteria[i]['label']}): You selected A as better but scored A lower than B.")
        elif choice == "👉 Model B" and score_b < score_a:
            error_msgs.append(f"Criterion {i+1} ({criteria[i]['label']}): You selected B as better but scored B lower than A.")
        elif choice == "🤝 Tie" and score_a != score_b:
            error_msgs.append(f"Criterion {i+1} ({criteria[i]['label']}): You selected Tie but scored A and B differently.")

    if error_msgs:
        err_str = "\n".join(error_msgs)
        return err_str, err_str
    else:
        return "No errors in responses; feel free to submit!", "No errors in responses; feel free to submit!"

# # Additional callback to handle submission results.
def toggle_slider(is_unqualified):
    # When the checkbox is checked (True), set interactive to False to disable the slider.
    return gr.update(interactive=not is_unqualified)

#show reference answer
def toggle_reference(selection):
    if selection == "Show Reference Answer":
        return gr.update(visible=True)
    else:
        return gr.update(visible=False)

#nonsense button helper
# def mark_invalid_question(btn_clicked_status):
#     new_status = not btn_clicked_status
#     if new_status == True:
#         # Show skip modal when marking as Wrong Question
#         return new_status, gr.update(value="Undo: Correct Question", variant="primary"), gr.update(visible=True)
#     else:
#         return new_status, gr.update(value="Wrong Question", variant="stop"), gr.update(visible=False)

# --- Skip Question Modal Callbacks ---
def skip_question_and_load_new(user_info_state, our_methods):
    # user_info_state is a tuple: (name, email, specialty_dd, subspecialty_dd, years_exp_radio, exp_explanation_tb, npi_id, q_id)
    if user_info_state is None:
        # Defensive: just close modal if no user info
        return gr.update(visible=False), gr.update(visible=False), None, "", gr.Chatbot(), gr.Chatbot(), gr.HTML(), gr.Markdown(), gr.State()
    # Unpack user_info_state
    name, email, specialty_dd, subspecialty_dd, years_exp_radio, exp_explanation_tb, npi_id, _ = user_info_state
    user_info, chat_a, chat_b, page1_prompt, page1_reference_answer, question_for_eval, remaining_count = get_next_eval_question(
        name, email, specialty_dd, subspecialty_dd, years_exp_radio, exp_explanation_tb, npi_id, our_methods
    )
    if remaining_count == 0:
        # No more questions, go to final page
        return gr.update(visible=False), gr.update(visible=False), None, "Based on your submitted data, you have no more questions to evaluate. You may exit the page; we will follow-up if we require anything else from you. Thank you!", gr.Chatbot(), gr.Chatbot(), gr.HTML(), gr.Markdown(), gr.State()
    return gr.update(visible=False), gr.update(visible=True), user_info, "", chat_a, chat_b, page1_prompt, page1_reference_answer, question_for_eval

# --- Skip‑question handler for the "Wrong Question?" button -------------------
def skip_current_question(user_info_state, our_methods: list = our_methods):
    # Guard: user clicked before session started
    gr.Info("Skipping this question and loading the next one…",  duration=5)
    if user_info_state is None:
        return (
            None,
            gr.update(value="Please start the evaluation before skipping questions."),
            gr.update(value=[]),  # Chatbot A history
            gr.update(value=[]),  # Chatbot B history
            gr.update(value=""),  # Prompt HTML
            gr.update(value=""),  # Reference answer
            gr.State()            # data_subset_state
        )

    # Unpack evaluator identity
    name, email, specialty_dd, subspecialty_dd, yrs_exp, exp_desc, npi_id, _ = user_info_state

    # Pull the next unused question
    (
        user_info_new,
        _chat_a_comp,  # we will rebuild the values directly
        _chat_b_comp,
        _prompt_comp,
        _ref_comp,
        question_for_eval,
        remaining,
    ) = get_next_eval_question(
        name, email, specialty_dd, subspecialty_dd, yrs_exp, exp_desc, npi_id, our_methods
    )

    # If the pool is exhausted, just notify the evaluator
    if remaining == 0 or question_for_eval is None:
        final_msg = (
            "Based on your submitted data, you have no more questions to evaluate. "
            "You may exit the page; we will follow‑up if we require anything else from you. "
            "Thank you!"
        )
        return (
            user_info_state,
            gr.update(value=final_msg),
            gr.update(value=[]),
            gr.update(value=[]),
            gr.update(value=""),
            gr.update(value=""),
            gr.State()
        )

    # --- Build fresh values for the existing UI components ---
    chat_a_value = format_chat(question_for_eval['models'][0]['reasoning_trace'], tool_database_labels)
    chat_b_value = format_chat(question_for_eval['models'][1]['reasoning_trace'], tool_database_labels)

    prompt_html = (
        f"<div style='background-color: #FFEFD5; border: 2px solid #FF8C00; padding: 10px; "
        f"border-radius: 5px; color: black;'><strong style='color: black;'>Prompt:</strong> "
        f"{question_for_eval['question']}</div>"
    )
    reference_md = question_for_eval.get("correct_answer", "")
    gr.Info("New question loaded…",  duration=3)

    # Return updates to refresh Page 1 in‑place
    return (
        user_info_new,
        gr.update(value=""),                 # clear any previous error text
        gr.update(value=chat_a_value),       # Chatbot A history
        gr.update(value=chat_b_value),       # Chatbot B history
        gr.update(value=prompt_html),        # Prompt
        gr.update(value=reference_md),       # Reference answer
        question_for_eval                    # store for later pages
    )

# --- Handler for "Wrong Question?": flags nonsense and skips
def flag_nonsense_and_skip(user_info_state):
    """
    When the evaluator clicks the “Wrong Question?” button, immediately
    record that this question was flagged as nonsensical/irrelevant and
    then load the next question (re‑using the existing skip logic).
    """
    # 1) Record the flag to the Google Sheet so we keep the feedback even
    #    if the evaluator stops here.
    if user_info_state is not None:
        name, email, specialty_dd, subspecialty_dd, yrs_exp, exp_desc, npi_id, q_id = user_info_state
        timestamp = datetime.datetime.now().isoformat()
        row = {
            "Timestamp": timestamp,
            "Name": name,
            "Email": email,
            "Question ID": q_id,
            "Question Makes No Sense or Biomedically Irrelevant": True,
        }
        append_to_sheet(
            user_data=None,
            custom_row_dict=row,
            custom_sheet_name=str(TXAGENT_RESULTS_SHEET_BASE_NAME),
            add_header_when_create_sheet=True,
        )

    # 2) Fall back to the existing skip logic to advance the UI.
    return skip_current_question(user_info_state)

centered_col_css = """
#centered-column {
    margin-left: auto;
    margin-right: auto;
    max-width: 800px; /* Adjust this width as desired */
    width: 100%;
}
#participate-btn {
    background-color: purple !important;
    color: white !important;
    border-color: purple !important;
}
#answer-reference-btn {
  /* Light‑mode palette */
  --btn-bg: #E0F2FF;        /* soft pastel blue */
  --btn-text: #00334D;      /* dark slate for good contrast */
  --btn-border: #E0F2FF;

  background-color: var(--btn-bg) !important;
  color: var(--btn-text) !important;
  border: 1px solid var(--btn-border) !important;
}

/* Dark‑mode overrides */
@media (prefers-color-scheme: dark) {
  #answer-reference-btn {
    --btn-bg: #2C6E98;      /* muted steel blue for dark backgrounds */
    --btn-text: #FFFFFF;    /* switch to white text for contrast */
    --btn-border: #2C6E98;
  }
}

#clear_btn {
    background-color: #F08080 !important;
    color: white !important;
    border-color: #F08080 !important;
}
.reference-box {
    border: 1px solid #ccc;
    padding: 10px;
    border-radius: 5px;
}
.short-btn { min-width: 80px !important; max-width: 120px !important; width: 100px !important; padding-left: 4px !important; padding-right: 4px !important; }
.light-stop-btn { background-color: #ffcccc !important; color: #b30000 !important; border-color: #ffcccc !important; }

/* --- Added for larger criteria font --- */
.criteria-font-large {
    font-size: 3em !important;
}
.gr-radio label, .criteria-radio-label {
    font-size: 3em !important;
    margin-top: -20px !important;
}
#participate-btn button {
    font-size: 24px !important;      /* Large readable text */
    font-weight: 700 !important;     /* Bold for emphasis */
    padding: 28px 40px !important;   /* Extra padding for height */
    min-height: 120px !important;    /* Make button visibly taller (multi‑line) */
    width: 100% !important;          /* Occupy full width of its column */
    white-space: normal !important;  /* Allow text to wrap onto multiple lines */
}
"""
with gr.Blocks(css=centered_col_css) as demo:
    # States to save information between pages.
    user_info_state = gr.State()
    pairwise_state = gr.State()
    scores_A_state = gr.State()
    comparison_reasons = gr.State()
    nonsense_btn_clicked = gr.State(False)
    unqualified_A_state = gr.State()
    data_subset_state = gr.State()
    question_in_progress = gr.State(0)

    # Load specialty data
    specialties_path = "specialties.json"
    subspecialties_path = "subspecialties.json"

    try:
        with open(specialties_path, 'r') as f:
            specialties_list = json.load(f)
        with open(subspecialties_path, 'r') as f:
            subspecialties_list = json.load(f)
    except FileNotFoundError:
        print(f"Error: Could not find specialty files at {specialties_path} or {subspecialties_path}. Please ensure these files exist.")
        # Provide default empty lists or handle the error as appropriate
        specialties_list = ["Error loading specialties"]
        subspecialties_list = ["Error loading subspecialties"]
    except json.JSONDecodeError:
        print(f"Error: Could not parse JSON from specialty files.")
        specialties_list = ["Error parsing specialties"]
        subspecialties_list = ["Error parsing subspecialties"]
    
    # Page -1: Page to link them to question submission form or evaluation portal
    with gr.Column(visible=True, elem_id="page-1") as page_minus1:
        gr.HTML("""
        <div>
            <h1>TxAgent Evaluation Portal</h1>
            <p>Welcome to the TxAgent Evaluation Portal.</p>
        </div>
        """)
        with gr.Row(elem_classes=["center-row"]):
            # 第一行：并排放两个按钮
            with gr.Column(scale=1):
                participate_eval_btn = gr.Button(
                    value="Click to 🌟Participate in TxAgent Evaluation 🌟",
                    variant="primary",
                    size="lg",
                    elem_id="participate-btn"
                )
            with gr.Column(scale=1):
                submit_questions_btn = gr.Button(
                    value="Click to 🚀 Submit Questions for TxAgent Evaluation 🚀",
                    variant="primary",
                    size="lg",
                    elem_id="submit-btn"
                )

        with gr.Row(elem_classes=["center-row"]):
            # 第二行：分别放两段说明文字
            with gr.Column(scale=1):
                gr.Markdown(
                    """
                    ### Why participate in the evaluation?
                    Joining the **TxAgent human evaluation** lets you experience the model in real-time and give immediate feedback.

                    **By clicking the button above, you will:**
                    - See how the model responds to a variety of prompts.
                    - Provide instant thumbs-up / thumbs-down ratings.
                    - Shape the roadmap for upcoming releases.

                    _Thank you for helping us improve!_
                    """
                )
            with gr.Column(scale=1):
                gr.Markdown(
                    """
                    ### Why submit questions?
                    Help us build a richer evaluation set by sending unique, challenging prompts.

                    **By clicking the button above, you will:**
                    - Highlight edge-cases and identify blind spots.
                    - Push TxAgent to reason in new domains.
                    - Directly influence future model improvements.

                    _We're excited to see what you come up with!_
                    """
                )
        gr.HTML(TxAgent_Project_Page_HTML)

        # Define actions for the new buttons
        # For the Google Form button, we'll use JavaScript to open a new tab.
        # The URL for the Google Form should be replaced with the actual link.
        google_form_url = "https://forms.gle/pYvyvEQQwS5gdupQA"
        submit_questions_btn.click(
            fn=None,
            inputs=None,
            outputs=None,
            js=f"() => {{ window.open('{google_form_url}', '_blank'); }}"
        )

    # Page 0: Welcome / Informational page.
    with gr.Column(visible=False, elem_id="page0") as page0:
        with gr.Row():
            with gr.Column():
                gr.Markdown("## Welcome to the TxAgent Evalution Study!")
                gr.Markdown("Please read the following instructions and then enter your information to begin:")
                # Existing informational markdown...
                gr.Markdown("""
                - Each session requires a minimum commitment of 5-10 minutes to complete one question.
                - If you wish to evaluate multiple questions, you may do so; you will never be asked to re-evaluate questions you have already seen.
                - When evaluating a question, you will be asked to compare the responses of two different models to the question and then rate each model's response on a scale of 1-5.
                - If you feel that a question does not make sense or is not biomedically relevant, there is a RED BUTTON at the top of the first model comparison page to indicate this
                - You may use the Back and Next buttons at the bottom of each page to edit any of your responses before submitting.
                - You may use the Home Page button at the bottom of each page to the home page. Your progress will be saved but not submitted.
                - You must submit your answers to the current question before moving on to evaluate the next question.
                - You may stop in between questions and return at a later time; however, you must submit your answers to the current question if you would like them saved.
                - Please review the example question and LLM model response below:

                """)
                with open("anatomyofAgentResponse.jpg", "rb") as image_file:
                    img = Image.open(image_file)
                    new_size = (int(img.width * 0.5), int(img.height * 0.5))
                    img = img.resize(new_size, Image.LANCZOS)
                    buffer = io.BytesIO()
                    img.save(buffer, format="PNG")
                    encoded_string = base64.b64encode(buffer.getvalue()).decode("utf-8")
                    #encoded_string = base64.b64encode(image_file.read()).decode("utf-8")

                image_html = f'<div style="text-align:center;"><img src="data:image/png;base64,{encoded_string}" alt="Your Image"></div>'
                ReasoningTraceExampleHTML = f"""
                <div>
                    {image_html}
                </div>
                """
                gr.HTML(ReasoningTraceExampleHTML)
            with gr.Column():
                gr.Markdown("## Please enter your information to get a question to evaluate. Please use the same email every time you log onto this evaluation portal, as we use your email to prevent showing repeat questions.")
                name = gr.Textbox(label="Name (required)")
                email = gr.Textbox(label="Email (required). Please use the same email every time you log onto this evaluation portal, as we use your email to prevent showing repeat questions.")
                specialty_dd = gr.Dropdown(choices=specialties_list, label="Primary Medical Specialty (required). Go to https://www.abms.org/member-boards/specialty-subspecialty-certificates/ for categorization)", multiselect=True)
                subspecialty_dd = gr.Dropdown(choices=subspecialties_list, label="Subspecialty (if applicable). Go to https://www.abms.org/member-boards/specialty-subspecialty-certificates/ for categorization)", multiselect=True)
                npi_id = gr.Textbox(label="National Provider Identifier ID (optional). Got to https://npiregistry.cms.hhs.gov/search to search for your NPI ID. If you do not have an NPI ID, please leave this blank.")
                years_exp_radio = gr.Radio(
                    choices=["0-2 years", "3-5 years", "6-10 years", "11-20 years", "20+ years", "Not Applicable"],
                    label="How many years have you been involved in clinical and/or research activities related to your biomedical area of expertise? (required)"
                )
                exp_explanation_tb = gr.Textbox(label="Please briefly explain your expertise/experience relevant to evaluating AI for clinical decision support (optional)")

                page0_error_box = gr.Markdown("")
                with gr.Row():
                    next_btn_0 = gr.Button("Next")
                with gr.Row():
                    home_btn_0 = gr.Button("Home") #  (your registration info will be saved)
                gr.Markdown("""By clicking 'Next', you will start the study, with your progress saved after submitting each question. If you have any other questions or concerns, please contact us directly. Thank you for your participation!
                """)


    with Modal(visible=False, elem_id="confirm_modal") as eval_progress_modal:
        eval_progress_text = gr.Markdown("You have X questions remaining.")
        cancel_and_edit_user_info_btn = gr.Button("Cancel, I would like to keep editing my medical info")
        eval_progress_proceed_btn = gr.Button("OK, proceed to question evaluation")

    # Page 1: Pairwise Comparison.
    with gr.Column(visible=False) as page1:
        gr.Markdown("## Part 1/2: Pairwise Comparison") #Make the number controlled by question indexing!
        page1_prompt = gr.HTML()

        with gr.Accordion("Click to reveal a reference answer.", open=False, elem_id="answer-reference-btn"):
            note_reference_answer = gr.Markdown(
                """
                Warning: This answer has been generated automatically and may be incomplete or one of several correct solutions—please use it for reference only.
                """,
                elem_classes="reference-box"
            )
            page1_reference_answer = gr.Markdown(
                """
                **Reference Answer:**

                This is the reference answer content.
                """,
                elem_classes="reference-box"
            )
            

        # Add small red button under the prompt
        with gr.Row():
            nonsense_btn = gr.Button(
                "Wrong Question?",
                size="sm",
                variant="stop",  # red variant
                elem_id="invalid-question-btn",
                elem_classes=["short-btn"]
            )
            gr.Markdown(
                "<span style='color: #b30000; font-weight: bold;'>Click the button if you think this question does not make sense or is not biomedically-relevant</span>",
                render=True
            )
        with gr.Row():
            unfamiliar_btn = gr.Button(
                "Unfamiliar Question?",
                size="sm",
                variant="stop",  # red variant
                elem_id="invalid-question-btn",
                elem_classes=["short-btn"]
            )
            gr.Markdown(
                "<span style='color: #b30000; font-weight: bold;'>Click the button if you are not familiar with this area</span>",
                render=True
            )

        page1_error_box = gr.Markdown("")  # ADDED: display validation errors

        # --- Define chat_a and chat_b before using them in outputs ---
        with gr.Row():
            # ADDED: Use gr.Chatbot to display the scrollable chat window for Response A.
            with gr.Column():
                gr.Markdown("**Model A Response:**")  # Already bold label.
                chat_a = gr.Chatbot(
                    value=[], # Placeholder for chat history
                    type="messages",
                    height=400,
                    label="Model A Response",
                    show_copy_button=False,
                    show_label=True,
                    render_markdown=True,  # Required for markdown/HTML support in messages
                    avatar_images=None,    # Optional: omit user/assistant icons
                    rtl=False
                )
            # ADDED: Use gr.Chatbot to display the scrollable chat window for Response B.
            with gr.Column():
                gr.Markdown("**Model B Response:**")
                chat_b = gr.Chatbot(
                    value=[],
                    type="messages",
                    height=400,
                    label="Model B Response",
                    show_copy_button=False,
                    show_label=True,
                    render_markdown=True,  # Required for markdown/HTML support in messages
                    avatar_images=None,    # Optional: omit user/assistant icons
                    rtl=False
                )
        gr.Markdown("<br><br>")
        gr.Markdown("### For each criterion, select which response did better:")
        comparison_reasons_inputs = []  # ADDED: list to store the free-text inputs
        pairwise_inputs = []
        for crit in criteria_for_comparison:
            with gr.Row():
                with gr.Column(scale=1):
                    gr.Markdown(crit['text'], elem_classes="criteria-font-large")  # <--- add class here
                with gr.Column(scale=1):
                    radio = gr.Radio(
                        choices=[
                            "👈 Model A",  # A
                            "👉 Model B",  # B
                            "🤝 Tie",      # tie
                            "👎 Neither model did well"  # neither
                        ],
                        label="Which is better?",
                        elem_classes="criteria-radio-label"  # <--- add class here
                    )
                pairwise_inputs.append(radio)
            # ADDED: free text under each comparison
            text_input = gr.Textbox(label=f"Reasons for your selection (optional)")
            comparison_reasons_inputs.append(text_input)
        

        with gr.Row():
            # back_btn_0 = gr.Button("Back") #removed button to go back to page 0 from page 1 to avoid page 1 + 2 question changing after this action
            next_btn_1 = gr.Button("Next: Rate Responses")
        
        with gr.Row():
            home_btn_1 = gr.Button("Home Page (your progress on this question will be saved but not submitted)")  # ADDED: Home button on page11
    
    # Page 2: Combined Rating Page for both responses.
    with gr.Column(visible=False) as page2:
        gr.Markdown("## Part 2/2: Rate Model Responses")
        # ### EDIT: Show a highlighted prompt as on previous pages.
        page2_prompt = gr.HTML()
        with gr.Accordion("Click to reveal a reference answer.", open=False, elem_id="answer-reference-btn"):
            note2_reference_answer = gr.Markdown(
                """
                Warning: This answer has been generated automatically and may be incomplete or one of several correct solutions—please use it for reference only.
                """,
                elem_classes="reference-box"
            )
            page2_reference_answer = gr.Markdown(
                """
                **Reference Answer:**

                This is the reference answer content.
                """,
                elem_classes="reference-box"
            )
        # ### EDIT: Display both responses side-by-side using Chatbot windows.
        with gr.Row():
            with gr.Column():
                gr.Markdown("**Model A Response:**")
                chat_a_rating = gr.Chatbot(
                    value=[],
                    type="messages",
                    height=400,
                    label="Model A Response",
                    show_copy_button=False,
                    render_markdown=True
                )
            with gr.Column():
                gr.Markdown("**Model B Response:**")
                chat_b_rating = gr.Chatbot(
                    value=[],
                    type="messages",
                    height=400,
                    label="Model B Response",
                    show_copy_button=False,
                    render_markdown=True
                )
        gr.Markdown("<h3 style='margin-top:0.5em; margin-bottom:0.5em;'>For each criterion, select your ratings for each model response:</h3>")
        # ### EDIT: For each criterion, create a row with two multiple-choice sets (left: Response A, right: Response B) separated by a border.
        ratings_A = []  # to store the radio components for response A
        ratings_B = []  # to store the radio components for response B
        
        def restrict_choices(pairwise_list, index, score_a, score_b):
            """
            Returns (update_for_A, update_for_B).
            Enforces rating constraints based on the pairwise choice for the given criterion index.
            """
            # Get the specific pairwise choice for this criterion using the index
            # Add error handling in case the state/list is not ready or index is wrong
            if not pairwise_list or index >= len(pairwise_list):
                pairwise_choice = None
            else:
                pairwise_choice = pairwise_list[index]

            base = ["1","2","3","4","5","Unable to Judge"]
            # Default: no restrictions unless explicitly set
            upd_A = gr.update(choices=base)
            upd_B = gr.update(choices=base)

            # Skip if no meaningful pairwise choice or either score is "Unable to Judge"
            if pairwise_choice is None or pairwise_choice == "👎 Neither model did well" or (score_a is None and score_b is None):
                # If one score is UJ but the other isn't, AND it's a Tie, we might still want to restrict the non-UJ one later?
                # For now, keep it simple: if either is UJ or choice is Neither/None, don't restrict.
                return upd_A, upd_B

            # Helper to parse int safely
            def to_int(x):
                try: return int(x)
                except (ValueError, TypeError): return None

            a_int = to_int(score_a)
            b_int = to_int(score_b)

            # --- Apply Restrictions ---
            if pairwise_choice == "👈 Model A":
                # B must be ≤ A (if A is numeric)
                if a_int is not None: #it is None if unable to judge
                    allowed_b_choices = [str(i) for i in range(1, a_int + 1)] + ["Unable to Judge"]
                    current_b = score_b if score_b in allowed_b_choices else None # Keep current valid choice
                    upd_B = gr.update(choices=allowed_b_choices, value=current_b)
                # If A is UJ or non-numeric, B is unrestricted by this rule
                # else: upd_B remains gr.update(choices=base)
                if b_int is not None:
                    # A must be >= B (if B is numeric)
                    allowed_a_choices = [str(i) for i in range(b_int, 6)] + ["Unable to Judge"]
                    current_a = score_a if score_a in allowed_a_choices else None # Keep current valid choice
                    upd_A = gr.update(choices=allowed_a_choices, value=current_a)
                # If B is UJ or non-numeric, A is unrestricted by this rule
                # else: upd_A remains gr.update(choices=base)

            elif pairwise_choice == "👉 Model B":
                # A must be ≤ B (if B is numeric)
                if b_int is not None:
                    allowed_a_choices = [str(i) for i in range(1, b_int + 1)] + ["Unable to Judge"]
                    current_a = score_a if score_a in allowed_a_choices else None # Keep current valid choice
                    upd_A = gr.update(choices=allowed_a_choices, value=current_a)
                # If B is UJ or non-numeric, A is unrestricted by this rule
                # else: upd_A remains gr.update(choices=base)
                if a_int is not None:
                    # B must be >= A (if A is numeric)
                    allowed_b_choices = [str(i) for i in range(a_int, 6)] + ["Unable to Judge"]
                    current_b = score_b if score_b in allowed_b_choices else None # Keep current valid choice
                    upd_B = gr.update(choices=allowed_b_choices, value=current_b)
                # If A is UJ or non-numeric, B is unrestricted by this rule
                # else: upd_B remains gr.update(choices=base)

            elif pairwise_choice == "🤝 Tie":
                # If both are numeric, they must match. Enforce based on the one that *just changed*.
                # If one changes to numeric, force the other (if also numeric) to match.
                # If one changes to UJ, the other is unrestricted.
                if a_int is not None:
                    upd_B = gr.update(choices=[score_a])
                elif score_a == "Unable to Judge":
                    upd_B = gr.update(choices=["Unable to Judge"])
                if b_int is not None:
                    upd_A = gr.update(choices=[score_b])
                elif score_b == "Unable to Judge":
                    upd_A = gr.update(choices=["Unable to Judge"])

            return upd_A, upd_B

        def clear_selection():
            return None, None
        
        pairwise_results_for_display = [gr.Markdown(render=False) for _ in range(len(criteria))]
        indices_for_change = []
        for i, crit in enumerate(criteria):
            index_component = gr.Number(value=i, visible=False, interactive=False)
            indices_for_change.append(index_component)

            with gr.Column(elem_id="centered-column"):
                gr.Markdown(f'<div style="text-align: left;">{crit["text"][0]}</div>', elem_classes="criteria-font-large")
                gr.Markdown(f'<div style="text-align: left;">{crit["text"][1]}</div>', elem_classes="criteria-font-large")
                pairwise_results_for_display[i].render()
            with gr.Row():
                with gr.Column(scale=1):
                    rating_a = gr.Radio(choices=["1", "2", "3", "4", "5", "Unable to Judge"],
                                            label=f"Score for Response A - {crit['label']}",
                                            interactive=True,
                                            elem_classes="criteria-radio-label")
                with gr.Column(scale=1):
                    rating_b = gr.Radio(choices=["1", "2", "3", "4", "5", "Unable to Judge"],
                                            label=f"Score for Response B - {crit['label']}",
                                            interactive=True,
                                            elem_classes="criteria-radio-label")
            with gr.Row():
                clear_btn = gr.Button("Clear Selection", size="sm", elem_id="clear_btn")
                clear_btn.click(fn=clear_selection, outputs=[rating_a,rating_b])

                # wire each to re‐restrict the other on change
                rating_a.change(
                    fn=restrict_choices,
                    inputs=[ pairwise_state, index_component, rating_a, rating_b ],
                    outputs=[ rating_a, rating_b ]
                )
                rating_b.change(
                    fn=restrict_choices,
                    inputs=[ pairwise_state, index_component, rating_a, rating_b ],
                    outputs=[ rating_a, rating_b ]
                )
                ratings_A.append(rating_a)
                ratings_B.append(rating_b)
        with gr.Row():
            back_btn_2 = gr.Button("Back")
            submit_btn = gr.Button("Submit (Note: Once submitted, you cannot edit your responses)", elem_id="submit_btn")

        with gr.Row():
            home_btn_2 = gr.Button("Home Page (your progress on this question will be saved but not submitted)")

        result_text = gr.Textbox(label="Validation Result")
    
    # Final Page: Thank you message.
    with gr.Column(visible=False, elem_id="final_page") as final_page:
        gr.Markdown("## You have no questions left to evaluate. Thank you for your participation!")
    
    # Error Modal: For displaying validation errors.
    with Modal("Error", visible=False, elem_id="error_modal") as error_modal:
        error_message_box = gr.Markdown()
        ok_btn = gr.Button("OK")
        # Clicking OK hides the modal.
        ok_btn.click(lambda: gr.update(visible=False), None, error_modal)
    
    # Confirmation Modal: Ask for final submission confirmation.
    with Modal("Confirm Submission", visible=False, elem_id="confirm_modal") as confirm_modal:
        gr.Markdown("Are you sure you want to submit? Once submitted, you cannot edit your responses.")
        with gr.Row():
            yes_btn = gr.Button("Yes, please submit")
            cancel_btn = gr.Button("Cancel")


    # --- Define Callback Functions for Confirmation Flow ---
    def build_row_dict(data_subset_state, user_info, pairwise, comparisons_reasons, nonsense_btn_clicked, *args):
        num_criteria = len(criteria)
        ratings_A_vals = list(args[:num_criteria])
        ratings_B_vals = list(args[num_criteria:])

        prompt_text = data_subset_state['question']
        response_A_model = data_subset_state['models'][0]['model']
        response_B_model = data_subset_state['models'][1]['model']

        timestamp = datetime.datetime.now().isoformat()
        row = {
            "Timestamp": timestamp,
            "Name": user_info[0],
            "Email": user_info[1],
            "Specialty": str(user_info[2]),
            "Subspecialty": str(user_info[3]),
            "Years of Experience": user_info[4],
            "Experience Explanation": user_info[5],
            "NPI ID": user_info[6],
            "Question ID": user_info[7],
            "Prompt": prompt_text,
            "ResponseA_Model": response_A_model,
            "ResponseB_Model": response_B_model,
            "Question Makes No Sense or Biomedically Irrelevant": nonsense_btn_clicked,
        }

        pairwise = [mapping.get(val, val) for val in pairwise]
        for i, crit in enumerate(criteria):
            label = crit['label']
            row[f"Criterion_{label} Comparison: Which is Better?"] = pairwise[i]
            row[f"Criterion_{label} Comments"] = comparisons_reasons[i]
            row[f"ScoreA_{label}"] = ratings_A_vals[i]
            row[f"ScoreB_{label}"] = ratings_B_vals[i]

        return row

    def final_submit(data_subset_state, user_info, pairwise, comparisons_reasons, nonsense_btn_clicked, *args):
        # --- Part 1: Submit the current results (Existing Logic) ---
        row_dict = build_row_dict(data_subset_state, user_info, pairwise, comparisons_reasons, nonsense_btn_clicked, *args)
        append_to_sheet(user_data=None, custom_row_dict=row_dict, custom_sheet_name=str(TXAGENT_RESULTS_SHEET_BASE_NAME), add_header_when_create_sheet=True)

        # --- Part 2: Recalculate remaining questions (Existing Logic + Modified Error Handling) ---
        name, email, specialty, subspecialty, years_exp_radio, exp_explanation_tb, npi_id, _ = user_info
        user_info_new, chat_a, chat_b, page1_prompt, page1_reference_answer, question_for_eval, remaining_count = get_next_eval_question(
            name, email, specialty, subspecialty, years_exp_radio, exp_explanation_tb, npi_id, our_methods
        )

        if remaining_count == 0:
            return (
                gr.update(visible=False),  # page0 (Hide)
                gr.update(visible=False),  # page2 (Hide)
                gr.update(visible=False),  # confirm_modal
                gr.update(visible=False), 
                "",                       
                gr.update(visible=True),   # final_page (Show)
                "",
                None,
                None,
                None,
                None,
                None,
                user_info_new,
            )
        return (
            gr.update(visible=False),  # page0 (Hide)
            gr.update(visible=False),  # page2 (Hide)
            gr.update(visible=False),  # confirm_modal (Hide)
            gr.update(visible=True),   # eval_progress_modal (Show)
            f"Submission successful! There are more questions to evaluate. You may exit the page and return later if you wish.",
            gr.update(visible=False),  # final_page (Hide)
            "",         
            chat_a,
            chat_b,
            page1_prompt,
            page1_reference_answer,
            question_for_eval,
            user_info_new
        )

    def cancel_submission():
        # Cancel final submission: just hide the confirmation modal.
        return gr.update(visible=False)
    
    def reset_everything_except_user_info():

        # 3) Reset all pairwise radios & textboxes
        reset_pairwise_radios = [gr.update(value=None) for i in range(len(criteria))]
        reset_pairwise_reasoning_texts  = [gr.update(value=None) for i in range(len(criteria))]

        # 4) Reset all rating radios
        reset_ratings_A = [gr.update(value=None) for i in range(len(criteria))]
        reset_ratings_B = [gr.update(value=None) for i in range(len(criteria))]

        return (
            # pages
            gr.update(visible=True),  # page0
            gr.update(visible=False), # final_page

            # states
            # gr.update(value=None),  # user_info_state
            gr.update(value=None),  # pairwise_state
            gr.update(value=None),  # scores_A_state
            gr.update(value=None),  # comparison_reasons
            gr.update(value=None),  # unqualified_A_state
            gr.update(value=0), # question_in_progress
            # gr.update(value=None),  # data_subset_state

            #page0 elements that need to be reset
            gr.update(value=""),  #page0_error_box 

            # page1 elements that need to be reset
            # gr.update(value=""),  #page1_prompt
            # gr.update(value=[]),  #chat_a
            # gr.update(value=[]),  #chat_b
            gr.update(value=""),  #page1_error_box

            # page2 elements that need to be reset
            gr.update(value=""),  #page2_prompt
            gr.update(value=""),  #page2_reference_answer
            gr.update(value=[]),  #chat_a_rating
            gr.update(value=[]),  #chat_b_rating
            gr.update(value=""),  #result_text

            #lists of gradio elements that need to be unrolled
            *reset_pairwise_radios,
            *reset_pairwise_reasoning_texts,
            *reset_ratings_A,
            *reset_ratings_B
        )
    
    # --- Define Transitions Between Pages ---


    # For the "Participate in Evaluation" button, transition to page0
    participate_eval_btn.click(
        fn=go_to_page0_from_minus1,
        inputs=[question_in_progress],
        outputs=[page_minus1, page0, page1, page2]
    )


    # Transition from Page 0 (Welcome) to Page 1.
    next_btn_0.click(
        fn=go_to_eval_progress_modal,
        inputs=[name, email, specialty_dd, subspecialty_dd, years_exp_radio, exp_explanation_tb, npi_id],
        outputs=[page0, page1, user_info_state, page0_error_box, chat_a, chat_b, page1_prompt, page1_reference_answer, data_subset_state,eval_progress_modal,eval_progress_text],
        scroll_to_output=True
    )

    cancel_and_edit_user_info_btn.click(
        fn=go_to_page1,
        inputs=gr.State(False),
        outputs=[eval_progress_modal, page0, page1],
        scroll_to_output=True
    )

    eval_progress_proceed_btn.click(
        fn=go_to_page1,
        inputs=gr.State(True),
        outputs=[eval_progress_modal, page0, page1],
        scroll_to_output=True
    )

    #Home page buttons to simply show page-1 (but if we move there from page 1 or 2, we must indicate a question is in progress)
    home_btn_0.click(lambda: (gr.update(visible=True), gr.update(visible=False), 0), None, [page_minus1, page0, question_in_progress])
    home_btn_1.click(lambda: (gr.update(visible=True), gr.update(visible=False), 1), None, [page_minus1, page1, question_in_progress])
    home_btn_2.click(lambda: (gr.update(visible=True), gr.update(visible=False), 2), None, [page_minus1, page2, question_in_progress])

    # Skip the current question and load a new one when the evaluator flags it
    nonsense_btn.click(
        fn=flag_nonsense_and_skip,
        inputs=[user_info_state],
        outputs=[user_info_state, page1_error_box, chat_a, chat_b,
                 page1_prompt, page1_reference_answer, data_subset_state],
        scroll_to_output=True
    )

    unfamiliar_btn.click(
        fn=skip_current_question,
        inputs=[user_info_state],
        outputs=[user_info_state, page1_error_box, chat_a, chat_b,
                 page1_prompt, page1_reference_answer, data_subset_state],
        scroll_to_output=True
    )
    
    
    # Transition from Page 1 (Pairwise) to the combined Rating Page (Page 2).
    next_btn_1.click(
        fn=go_to_page2,  # ### EDIT: Rename or update the function to simply pass the pairwise inputs if needed.
        inputs=[data_subset_state,*pairwise_inputs,*comparison_reasons_inputs],
        outputs=[page1, page2, pairwise_state, comparison_reasons, page1_error_box, chat_a_rating, chat_b_rating, page2_prompt, page2_reference_answer,*pairwise_results_for_display],
        scroll_to_output=True
    )
    
    # Transition from Rating Page (Page 2) back to Pairwise page.
    back_btn_2.click(
        fn=lambda: (gr.update(visible=True), gr.update(visible=False)),
        inputs=None,
        outputs=[page1, page2],
        scroll_to_output=True
    )
    
    # --- Submission: Validate the Ratings and then Process the Result ---
    def process_result(result):
        # If validation passed, show the confirmation modal and proceed.
        if result == "No errors in responses; feel free to submit!":
            return (
                gr.update(),  # Show page 3
                gr.update(),  # Hide final page
                gr.update(visible=True),  # Show confirmation modal
                gr.update(visible=False), # Hide error modal
                gr.update(value="")  # EDIT: Clear the error_message_box
            )
        else:
            # If validation fails, show the error modal and display the error in error_message_box.
            return (
                gr.update(),             # Keep page3 as is
                gr.update(),             # Keep final page unchanged
                gr.update(visible=False), # Hide confirmation modal
                gr.update(visible=True),  # Show error modal
                gr.update(value=result)   # EDIT: Update error_message_box with the validation error
            )

    # ### EDIT: Update the submission callback to use the new radio inputs.
    submit_btn.click(
        fn=validate_ratings,
        inputs=[pairwise_state, *ratings_A, *ratings_B],
        outputs=[error_message_box, result_text]
    ).then(
        fn=process_result,
        inputs=error_message_box,
        outputs=[page2, final_page, confirm_modal, error_modal, error_message_box],
        scroll_to_output=True
    )
    
    # Finalize submission if user confirms.
    question_submission_event = yes_btn.click(
        fn=final_submit,
        inputs=[data_subset_state, user_info_state, pairwise_state, comparison_reasons, nonsense_btn_clicked, *ratings_A, *ratings_B],
        outputs=[
            page0,                 # Controlled by final_submit return value 1
            page2,                 # Controlled by final_submit return value 2
            confirm_modal,         # Controlled by final_submit return value 3
            eval_progress_modal,   # Controlled by final_submit return value 4
            eval_progress_text,    # Controlled by final_submit return value 5
            final_page,            # Controlled by final_submit return value 6
            page0_error_box,
            chat_a,
            chat_b,
            page1_prompt,
            page1_reference_answer,
            data_subset_state,
            user_info_state,
        ],
        scroll_to_output=True
    )
    
    # Cancel final submission.
    cancel_btn.click(
        fn=cancel_submission,
        inputs=None,
        outputs=confirm_modal
    )

    # Reset everything and evaluate another question button
    question_submission_event.then(
        fn=reset_everything_except_user_info,
        inputs=[],
        outputs=[
            # pages
            page0,
            final_page,

            # states
            # user_info_state,
            pairwise_state,
            scores_A_state,
            comparison_reasons,
            unqualified_A_state,
            question_in_progress,
            # data_subset_state,

            #page0 elements that need to be reset
            page0_error_box,

            # # page1 elements that need to be reset
            # page1_prompt,
            # chat_a,
            # chat_b,
            page1_error_box,

            # page2 elements that need to be reset
            page2_prompt,
            page2_reference_answer,
            chat_a_rating,
            chat_b_rating,
            result_text,

            #lists of gradio elements that need to be unrolled
            *pairwise_inputs,
            *comparison_reasons_inputs,
            *ratings_A,
            *ratings_B
        ]
    )



demo.launch(share=True, allowed_paths = ["."])