app.py

# --- START OF FILE app.py ---

import json
import gradio as gr
import pandas as pd
import plotly.express as px
import os
import numpy as np
import duckdb
from tqdm.auto import tqdm # Standard tqdm for console, gr.Progress will track it
import time
import ast # For safely evaluating string representations of lists/dicts

# --- Constants ---
MODEL_SIZE_RANGES = {
    "Small (<1GB)": (0, 1), "Medium (1-5GB)": (1, 5), "Large (5-20GB)": (5, 20),
    "X-Large (20-50GB)": (20, 50), "XX-Large (>50GB)": (50, float('inf'))
}
PROCESSED_PARQUET_FILE_PATH = "models_processed.parquet"
HF_PARQUET_URL = 'https://huggingface.co/datasets/cfahlgren1/hub-stats/resolve/main/models.parquet' # Added for completeness within app.py context

TAG_FILTER_CHOICES = [
    "Audio & Speech", "Time series", "Robotics", "Music", "Video", "Images",
    "Text", "Biomedical", "Sciences"
]

PIPELINE_TAGS = [
 'text-generation', 'text-to-image', 'text-classification', 'text2text-generation',
 'audio-to-audio', 'feature-extraction', 'image-classification', 'translation',
 'reinforcement-learning', 'fill-mask', 'text-to-speech', 'automatic-speech-recognition',
 'image-text-to-text', 'token-classification', 'sentence-similarity', 'question-answering',
 'image-feature-extraction', 'summarization', 'zero-shot-image-classification',
 'object-detection', 'image-segmentation', 'image-to-image', 'image-to-text',
 'audio-classification', 'visual-question-answering', 'text-to-video',
 'zero-shot-classification', 'depth-estimation', 'text-ranking', 'image-to-video',
 'multiple-choice', 'unconditional-image-generation', 'video-classification',
 'text-to-audio', 'time-series-forecasting', 'any-to-any', 'video-text-to-text',
 'table-question-answering',
]

def extract_model_size(safetensors_data):
    try:
        if pd.isna(safetensors_data): return 0.0
        data_to_parse = safetensors_data
        if isinstance(safetensors_data, str):
            try:
                if (safetensors_data.startswith('{') and safetensors_data.endswith('}')) or \
                   (safetensors_data.startswith('[') and safetensors_data.endswith(']')):
                    data_to_parse = ast.literal_eval(safetensors_data)
                else: data_to_parse = json.loads(safetensors_data)
            except: return 0.0
        if isinstance(data_to_parse, dict) and 'total' in data_to_parse:
            try:
                total_bytes_val = data_to_parse['total']
                size_bytes = float(total_bytes_val)
                return size_bytes / (1024 * 1024 * 1024)
            except (ValueError, TypeError): pass
        return 0.0
    except: return 0.0

def extract_org_from_id(model_id):
    if pd.isna(model_id): return "unaffiliated"
    model_id_str = str(model_id)
    return model_id_str.split("/")[0] if "/" in model_id_str else "unaffiliated"

def process_tags_for_series(series_of_tags_values):
    processed_tags_accumulator = []

    for i, tags_value_from_series in enumerate(tqdm(series_of_tags_values, desc="Standardizing Tags", leave=False, unit="row")):
        temp_processed_list_for_row = []
        current_value_for_error_msg = str(tags_value_from_series)[:200] # Truncate for long error messages

        try:
            # Order of checks is important!
            # 1. Handle explicit Python lists first
            if isinstance(tags_value_from_series, list):
                current_tags_in_list = []
                for idx_tag, tag_item in enumerate(tags_value_from_series):
                    try:
                        # Ensure item is not NaN before string conversion if it might be a float NaN in a list
                        if pd.isna(tag_item): continue 
                        str_tag = str(tag_item)
                        stripped_tag = str_tag.strip()
                        if stripped_tag:
                            current_tags_in_list.append(stripped_tag)
                    except Exception as e_inner_list_proc:
                        print(f"ERROR processing item '{tag_item}' (type: {type(tag_item)}) within a list for row {i}. Error: {e_inner_list_proc}. Original list: {current_value_for_error_msg}")
                temp_processed_list_for_row = current_tags_in_list

            # 2. Handle NumPy arrays
            elif isinstance(tags_value_from_series, np.ndarray):
                # Convert to list, then process elements, handling potential NaNs within the array
                current_tags_in_list = []
                for idx_tag, tag_item in enumerate(tags_value_from_series.tolist()): # .tolist() is crucial
                    try:
                        if pd.isna(tag_item): continue # Check for NaN after converting to Python type
                        str_tag = str(tag_item)
                        stripped_tag = str_tag.strip()
                        if stripped_tag:
                            current_tags_in_list.append(stripped_tag)
                    except Exception as e_inner_array_proc:
                        print(f"ERROR processing item '{tag_item}' (type: {type(tag_item)}) within a NumPy array for row {i}. Error: {e_inner_array_proc}. Original array: {current_value_for_error_msg}")
                temp_processed_list_for_row = current_tags_in_list
            
            # 3. Handle simple None or pd.NA after lists and arrays (which might contain pd.NA elements handled above)
            elif tags_value_from_series is None or pd.isna(tags_value_from_series): # Now pd.isna is safe for scalars
                temp_processed_list_for_row = []

            # 4. Handle strings (could be JSON-like, list-like, or comma-separated)
            elif isinstance(tags_value_from_series, str):
                processed_str_tags = []
                # Attempt ast.literal_eval for strings that look like lists/tuples
                if (tags_value_from_series.startswith('[') and tags_value_from_series.endswith(']')) or \
                   (tags_value_from_series.startswith('(') and tags_value_from_series.endswith(')')):
                    try:
                        evaluated_tags = ast.literal_eval(tags_value_from_series)
                        if isinstance(evaluated_tags, (list, tuple)): # Check if eval result is a list/tuple
                            # Recursively process this evaluated list/tuple, as its elements could be complex
                            # For simplicity here, assume elements are simple strings after eval
                            current_eval_list = []
                            for tag_item in evaluated_tags:
                                if pd.isna(tag_item): continue
                                str_tag = str(tag_item).strip()
                                if str_tag: current_eval_list.append(str_tag)
                            processed_str_tags = current_eval_list
                    except (ValueError, SyntaxError):
                        pass # If ast.literal_eval fails, let it fall to JSON or comma split

                # If ast.literal_eval didn't populate, try JSON
                if not processed_str_tags:
                    try:
                        json_tags = json.loads(tags_value_from_series)
                        if isinstance(json_tags, list):
                            # Similar to above, assume elements are simple strings after JSON parsing
                            current_json_list = []
                            for tag_item in json_tags:
                                if pd.isna(tag_item): continue
                                str_tag = str(tag_item).strip()
                                if str_tag: current_json_list.append(str_tag)
                            processed_str_tags = current_json_list
                    except json.JSONDecodeError:
                        # If not a valid JSON list, fall back to comma splitting as the final string strategy
                        processed_str_tags = [tag.strip() for tag in tags_value_from_series.split(',') if tag.strip()]
                    except Exception as e_json_other:
                        print(f"ERROR during JSON processing for string '{current_value_for_error_msg}' for row {i}. Error: {e_json_other}")
                        processed_str_tags = [tag.strip() for tag in tags_value_from_series.split(',') if tag.strip()] # Fallback

                temp_processed_list_for_row = processed_str_tags
            
            # 5. Fallback for other scalar types (e.g., int, float that are not NaN)
            else:
                # This path is for non-list, non-ndarray, non-None/NaN, non-string types.
                # Or for NaNs that slipped through if they are not None or pd.NA (e.g. float('nan'))
                if pd.isna(tags_value_from_series): # Catch any remaining NaNs like float('nan')
                     temp_processed_list_for_row = []
                else:
                    str_val = str(tags_value_from_series).strip()
                    temp_processed_list_for_row = [str_val] if str_val else []
            
            processed_tags_accumulator.append(temp_processed_list_for_row)

        except Exception as e_outer_tag_proc:
            print(f"CRITICAL UNHANDLED ERROR processing row {i}: value '{current_value_for_error_msg}' (type: {type(tags_value_from_series)}). Error: {e_outer_tag_proc}. Appending [].")
            processed_tags_accumulator.append([])
            
    return processed_tags_accumulator

def load_models_data(force_refresh=False, tqdm_cls=None):
    if tqdm_cls is None: tqdm_cls = tqdm
    overall_start_time = time.time()
    print(f"Gradio load_models_data called with force_refresh={force_refresh}")

    expected_cols_in_processed_parquet = [
        'id', 'downloads', 'downloadsAllTime', 'likes', 'pipeline_tag', 'tags', 'params',
        'size_category', 'organization', 'has_audio', 'has_speech', 'has_music',
        'has_robot', 'has_bio', 'has_med', 'has_series', 'has_video', 'has_image',
        'has_text', 'has_science', 'is_audio_speech', 'is_biomed',
        'data_download_timestamp'
    ]

    if not force_refresh and os.path.exists(PROCESSED_PARQUET_FILE_PATH):
        print(f"Attempting to load pre-processed data from: {PROCESSED_PARQUET_FILE_PATH}")
        try:
            df = pd.read_parquet(PROCESSED_PARQUET_FILE_PATH)
            elapsed = time.time() - overall_start_time
            missing_cols = [col for col in expected_cols_in_processed_parquet if col not in df.columns]
            if missing_cols:
                raise ValueError(f"Pre-processed Parquet is missing columns: {missing_cols}. Please run preprocessor or refresh data in app.")
            
            # --- Diagnostic for 'has_robot' after loading parquet ---
            if 'has_robot' in df.columns:
                robot_count_parquet = df['has_robot'].sum()
                print(f"DIAGNOSTIC (App - Parquet Load): 'has_robot' column found. Number of True values: {robot_count_parquet}")
                if 0 < robot_count_parquet < 10:
                     print(f"Sample 'has_robot' models (from parquet): {df[df['has_robot']]['id'].head().tolist()}")
            else:
                print("DIAGNOSTIC (App - Parquet Load): 'has_robot' column NOT FOUND.")
            # --- End Diagnostic ---

            msg = f"Successfully loaded pre-processed data in {elapsed:.2f}s. Shape: {df.shape}"
            print(msg)
            return df, True, msg
        except Exception as e:
            print(f"Could not load pre-processed Parquet: {e}. ")
            if force_refresh: print("Proceeding to fetch fresh data as force_refresh=True.")
            else:
                 err_msg = (f"Pre-processed data could not be loaded: {e}. "
                           "Please use 'Refresh Data from Hugging Face' button.")
                 return pd.DataFrame(), False, err_msg

    df_raw = None
    raw_data_source_msg = ""
    if force_refresh:
        print("force_refresh=True (Gradio). Fetching fresh data...")
        fetch_start = time.time()
        try:
            query = f"SELECT * FROM read_parquet('{HF_PARQUET_URL}')" # Ensure HF_PARQUET_URL is defined
            df_raw = duckdb.sql(query).df()
            if df_raw is None or df_raw.empty: raise ValueError("Fetched data is empty or None.")
            raw_data_source_msg = f"Fetched by Gradio in {time.time() - fetch_start:.2f}s. Rows: {len(df_raw)}"
            print(raw_data_source_msg)
        except Exception as e_hf:
            return pd.DataFrame(), False, f"Fatal error fetching from Hugging Face (Gradio): {e_hf}"
    else: 
        err_msg = (f"Pre-processed data '{PROCESSED_PARQUET_FILE_PATH}' not found/invalid. "
                   "Run preprocessor or use 'Refresh Data' button.")
        return pd.DataFrame(), False, err_msg

    print(f"Initiating processing for data newly fetched by Gradio. {raw_data_source_msg}")
    df = pd.DataFrame()
    proc_start = time.time()
    
    core_cols = {'id': str, 'downloads': float, 'downloadsAllTime': float, 'likes': float,
                 'pipeline_tag': str, 'tags': object, 'safetensors': object}
    for col, dtype in core_cols.items():
        if col in df_raw.columns:
            df[col] = df_raw[col]
            if dtype == float: df[col] = pd.to_numeric(df[col], errors='coerce').fillna(0.0)
            elif dtype == str: df[col] = df[col].astype(str).fillna('')
        else:
            if col in ['downloads', 'downloadsAllTime', 'likes']: df[col] = 0.0
            elif col == 'pipeline_tag': df[col] = ''
            elif col == 'tags': df[col] = pd.Series([[] for _ in range(len(df_raw))])
            elif col == 'safetensors': df[col] = None 
            elif col == 'id': return pd.DataFrame(), False, "Critical: 'id' column missing."
    
    output_filesize_col_name = 'params'
    if output_filesize_col_name in df_raw.columns and pd.api.types.is_numeric_dtype(df_raw[output_filesize_col_name]):
        df[output_filesize_col_name] = pd.to_numeric(df_raw[output_filesize_col_name], errors='coerce').fillna(0.0)
    elif 'safetensors' in df.columns:
        safetensors_iter = df['safetensors']
        if tqdm_cls != tqdm :
             safetensors_iter = tqdm_cls(df['safetensors'], desc="Extracting model sizes (GB)")
        df[output_filesize_col_name] = [extract_model_size(s) for s in safetensors_iter]
        df[output_filesize_col_name] = pd.to_numeric(df[output_filesize_col_name], errors='coerce').fillna(0.0)
    else:
        df[output_filesize_col_name] = 0.0

    def get_size_category_gradio(size_gb_val):
        try: numeric_size_gb = float(size_gb_val)
        except (ValueError, TypeError): numeric_size_gb = 0.0
        if pd.isna(numeric_size_gb): numeric_size_gb = 0.0
        if 0 <= numeric_size_gb < 1: return "Small (<1GB)"
        elif 1 <= numeric_size_gb < 5: return "Medium (1-5GB)"
        elif 5 <= numeric_size_gb < 20: return "Large (5-20GB)"
        elif 20 <= numeric_size_gb < 50: return "X-Large (20-50GB)"
        elif numeric_size_gb >= 50: return "XX-Large (>50GB)"
        else: return "Small (<1GB)"
    df['size_category'] = df[output_filesize_col_name].apply(get_size_category_gradio)

    df['tags'] = process_tags_for_series(df['tags'])
    df['temp_tags_joined'] = df['tags'].apply(
        lambda tl: '~~~'.join(str(t).lower() for t in tl if pd.notna(t) and str(t).strip()) if isinstance(tl, list) else ''
    )
    tag_map = {
        'has_audio': ['audio'], 'has_speech': ['speech'], 'has_music': ['music'],
        'has_robot': ['robot', 'robotics'],
        'has_bio': ['bio'], 'has_med': ['medic', 'medical'],
        'has_series': ['series', 'time-series', 'timeseries'],
        'has_video': ['video'], 'has_image': ['image', 'vision'],
        'has_text': ['text', 'nlp', 'llm']
    }
    for col, kws in tag_map.items():
        pattern = '|'.join(kws)
        df[col] = df['temp_tags_joined'].str.contains(pattern, na=False, case=False, regex=True)
    df['has_science'] = (
        df['temp_tags_joined'].str.contains('science', na=False, case=False, regex=True) &
        ~df['temp_tags_joined'].str.contains('bigscience', na=False, case=False, regex=True)
    )
    del df['temp_tags_joined']
    df['is_audio_speech'] = (df['has_audio'] | df['has_speech'] |
                            df['pipeline_tag'].str.contains('audio|speech', case=False, na=False, regex=True))
    df['is_biomed'] = df['has_bio'] | df['has_med']
    df['organization'] = df['id'].apply(extract_org_from_id)

    if 'safetensors' in df.columns and \
       not (output_filesize_col_name in df_raw.columns and pd.api.types.is_numeric_dtype(df_raw[output_filesize_col_name])):
        df = df.drop(columns=['safetensors'], errors='ignore')
    
    # --- Diagnostic for 'has_robot' after app-side processing (force_refresh path) ---
    if force_refresh and 'has_robot' in df.columns:
        robot_count_app_proc = df['has_robot'].sum()
        print(f"DIAGNOSTIC (App - Force Refresh Processing): 'has_robot' column processed. Number of True values: {robot_count_app_proc}")
        if 0 < robot_count_app_proc < 10:
            print(f"Sample 'has_robot' models (App processed): {df[df['has_robot']]['id'].head().tolist()}")
    # --- End Diagnostic ---

    print(f"Data processing by Gradio completed in {time.time() - proc_start:.2f}s.")
    
    total_elapsed = time.time() - overall_start_time
    final_msg = f"{raw_data_source_msg}. Processing by Gradio took {time.time() - proc_start:.2f}s. Total: {total_elapsed:.2f}s. Shape: {df.shape}"
    print(final_msg)
    return df, True, final_msg


def make_treemap_data(df, count_by, top_k=25, tag_filter=None, pipeline_filter=None, size_filter=None, skip_orgs=None):
    if df is None or df.empty: return pd.DataFrame()
    filtered_df = df.copy()
    col_map = { "Audio & Speech": "is_audio_speech", "Music": "has_music", "Robotics": "has_robot",
                "Biomedical": "is_biomed", "Time series": "has_series", "Sciences": "has_science",
                "Video": "has_video", "Images": "has_image", "Text": "has_text"}
    
    # --- Diagnostic within make_treemap_data ---
    if 'has_robot' in filtered_df.columns:
        initial_robot_count = filtered_df['has_robot'].sum()
        print(f"DIAGNOSTIC (make_treemap_data entry): Input df has {initial_robot_count} 'has_robot' models.")
    else:
        print("DIAGNOSTIC (make_treemap_data entry): 'has_robot' column NOT in input df.")
    # --- End Diagnostic ---

    if tag_filter and tag_filter in col_map:
        target_col = col_map[tag_filter]
        if target_col in filtered_df.columns:
            # --- Diagnostic for specific 'Robotics' filter application ---
            if tag_filter == "Robotics":
                count_before_robot_filter = filtered_df[target_col].sum()
                print(f"DIAGNOSTIC (make_treemap_data): Applying 'Robotics' filter. Models with '{target_col}'=True before this filter step: {count_before_robot_filter}")
            # --- End Diagnostic ---
            filtered_df = filtered_df[filtered_df[target_col]]
            if tag_filter == "Robotics":
                 print(f"DIAGNOSTIC (make_treemap_data): After 'Robotics' filter ({target_col}), df rows: {len(filtered_df)}")
        else:
            print(f"Warning: Tag filter column '{col_map[tag_filter]}' not found in DataFrame.")
    if pipeline_filter:
        if "pipeline_tag" in filtered_df.columns:
            filtered_df = filtered_df[filtered_df["pipeline_tag"] == pipeline_filter]
        else:
            print(f"Warning: 'pipeline_tag' column not found for filtering.")
    if size_filter and size_filter != "None" and size_filter in MODEL_SIZE_RANGES.keys():
        if 'size_category' in filtered_df.columns:
            filtered_df = filtered_df[filtered_df['size_category'] == size_filter]
        else:
            print("Warning: 'size_category' column not found for filtering.")
    if skip_orgs and len(skip_orgs) > 0:
        if "organization" in filtered_df.columns:
            filtered_df = filtered_df[~filtered_df["organization"].isin(skip_orgs)]
        else:
            print("Warning: 'organization' column not found for filtering.")
    if filtered_df.empty: return pd.DataFrame()
    if count_by not in filtered_df.columns or not pd.api.types.is_numeric_dtype(filtered_df[count_by]):
        filtered_df[count_by] = pd.to_numeric(filtered_df.get(count_by), errors="coerce").fillna(0.0)
    org_totals = filtered_df.groupby("organization")[count_by].sum().nlargest(top_k, keep='first')
    top_orgs_list = org_totals.index.tolist()
    treemap_data = filtered_df[filtered_df["organization"].isin(top_orgs_list)][["id", "organization", count_by]].copy()
    treemap_data["root"] = "models"
    treemap_data[count_by] = pd.to_numeric(treemap_data[count_by], errors="coerce").fillna(0.0) 
    return treemap_data

def create_treemap(treemap_data, count_by, title=None):
    if treemap_data.empty:
        fig = px.treemap(names=["No data matches filters"], parents=[""], values=[1])
        fig.update_layout(title="No data matches the selected filters", margin=dict(t=50, l=25, r=25, b=25))
        return fig
    fig = px.treemap(
        treemap_data, path=["root", "organization", "id"], values=count_by,
        title=title or f"HuggingFace Models - {count_by.capitalize()} by Organization",
        color_discrete_sequence=px.colors.qualitative.Plotly
    )
    fig.update_layout(margin=dict(t=50, l=25, r=25, b=25))
    fig.update_traces(textinfo="label+value+percent root", hovertemplate="<b>%{label}</b><br>%{value:,} " + count_by + "<br>%{percentRoot:.2%} of total<extra></extra>")
    return fig

with gr.Blocks(title="HuggingFace Model Explorer", fill_width=True) as demo:
    models_data_state = gr.State(pd.DataFrame())
    loading_complete_state = gr.State(False)

    with gr.Row(): gr.Markdown("# HuggingFace Models TreeMap Visualization")
    with gr.Row():
        with gr.Column(scale=1):
            count_by_dropdown = gr.Dropdown(label="Metric", choices=[("Downloads (last 30 days)", "downloads"), ("Downloads (All Time)", "downloadsAllTime"), ("Likes", "likes")], value="downloads")
            filter_choice_radio = gr.Radio(label="Filter Type", choices=["None", "Tag Filter", "Pipeline Filter"], value="None")
            tag_filter_dropdown = gr.Dropdown(label="Select Tag", choices=TAG_FILTER_CHOICES, value=None, visible=False)
            pipeline_filter_dropdown = gr.Dropdown(label="Select Pipeline Tag", choices=PIPELINE_TAGS, value=None, visible=False)
            size_filter_dropdown = gr.Dropdown(label="Model Size Filter", choices=["None"] + list(MODEL_SIZE_RANGES.keys()), value="None")
            top_k_slider = gr.Slider(label="Number of Top Organizations", minimum=5, maximum=50, value=25, step=5)
            skip_orgs_textbox = gr.Textbox(label="Organizations to Skip (comma-separated)", value="TheBloke,MaziyarPanahi,unsloth,modularai,Gensyn,bartowski")
            generate_plot_button = gr.Button(value="Generate Plot", variant="primary", interactive=False)
            refresh_data_button = gr.Button(value="Refresh Data from Hugging Face", variant="secondary")
        with gr.Column(scale=3):
            plot_output = gr.Plot()
            status_message_md = gr.Markdown("Initializing...")
            data_info_md = gr.Markdown("")

    def _update_button_interactivity(is_loaded_flag):
        return gr.update(interactive=is_loaded_flag)
    loading_complete_state.change(fn=_update_button_interactivity, inputs=loading_complete_state, outputs=generate_plot_button)

    def _toggle_filters_visibility(choice):
        return gr.update(visible=choice == "Tag Filter"), gr.update(visible=choice == "Pipeline Filter")
    filter_choice_radio.change(fn=_toggle_filters_visibility, inputs=filter_choice_radio, outputs=[tag_filter_dropdown, pipeline_filter_dropdown])

    def ui_load_data_controller(force_refresh_ui_trigger=False, progress=gr.Progress(track_tqdm=True)):
        print(f"ui_load_data_controller called with force_refresh_ui_trigger={force_refresh_ui_trigger}")
        status_msg_ui = "Loading data..."
        data_info_text = ""
        current_df = pd.DataFrame()
        load_success_flag = False
        data_as_of_date_display = "N/A"
        try:
            current_df, load_success_flag, status_msg_from_load = load_models_data(
                force_refresh=force_refresh_ui_trigger, tqdm_cls=progress.tqdm
            )
            if load_success_flag:
                if force_refresh_ui_trigger: 
                    data_as_of_date_display = pd.Timestamp.now(tz='UTC').strftime('%B %d, %Y, %H:%M:%S %Z')
                elif 'data_download_timestamp' in current_df.columns and not current_df.empty and pd.notna(current_df['data_download_timestamp'].iloc[0]):
                    timestamp_from_parquet = pd.to_datetime(current_df['data_download_timestamp'].iloc[0])
                    if timestamp_from_parquet.tzinfo is None:
                        timestamp_from_parquet = timestamp_from_parquet.tz_localize('UTC')
                    data_as_of_date_display = timestamp_from_parquet.strftime('%B %d, %Y, %H:%M:%S %Z')
                else:
                    data_as_of_date_display = "Pre-processed (date unavailable)"
                
                size_dist_lines = []
                if 'size_category' in current_df.columns:
                    for cat in MODEL_SIZE_RANGES.keys():
                        count = (current_df['size_category'] == cat).sum()
                        size_dist_lines.append(f"  - {cat}: {count:,} models")
                else: size_dist_lines.append("  - Size category information not available.")
                size_dist = "\n".join(size_dist_lines)
                
                data_info_text = (f"### Data Information\n"
                                  f"- Overall Status: {status_msg_from_load}\n" 
                                  f"- Total models loaded: {len(current_df):,}\n"
                                  f"- Data as of: {data_as_of_date_display}\n"
                                  f"- Size categories:\n{size_dist}")
                
                # # --- MODIFICATION: Add 'has_robot' count to UI data_info_text ---
                # if not current_df.empty and 'has_robot' in current_df.columns:
                #     robot_true_count = current_df['has_robot'].sum()
                #     data_info_text += f"\n- **Models flagged 'has_robot'**: {robot_true_count}"
                #     if 0 < robot_true_count <= 10: # If a few are found, list some IDs
                #         sample_robot_ids = current_df[current_df['has_robot']]['id'].head(5).tolist()
                #         data_info_text += f"\n  - Sample 'has_robot' model IDs: `{', '.join(sample_robot_ids)}`"
                # elif not current_df.empty:
                #     data_info_text += "\n- **Models flagged 'has_robot'**: 'has_robot' column not found in loaded data."
                # # --- END MODIFICATION ---

                status_msg_ui = "Data loaded successfully. Ready to generate plot."
            else: 
                data_info_text = f"### Data Load Failed\n- {status_msg_from_load}"
                status_msg_ui = status_msg_from_load 
        except Exception as e:
            status_msg_ui = f"An unexpected error occurred in ui_load_data_controller: {str(e)}"
            data_info_text = f"### Critical Error\n- {status_msg_ui}"
            print(f"Critical error in ui_load_data_controller: {e}")
            load_success_flag = False 
        return current_df, load_success_flag, data_info_text, status_msg_ui

    def ui_generate_plot_controller(metric_choice, filter_type, tag_choice, pipeline_choice, 
                                   size_choice, k_orgs, skip_orgs_input, df_current_models):
        if df_current_models is None or df_current_models.empty:
            empty_fig = create_treemap(pd.DataFrame(), metric_choice, "Error: Model Data Not Loaded")
            error_msg = "Model data is not loaded or is empty. Please load or refresh data first."
            gr.Warning(error_msg)
            return empty_fig, error_msg
        tag_to_use = tag_choice if filter_type == "Tag Filter" else None
        pipeline_to_use = pipeline_choice if filter_type == "Pipeline Filter" else None
        size_to_use = size_choice if size_choice != "None" else None
        orgs_to_skip = [org.strip() for org in skip_orgs_input.split(',') if org.strip()] if skip_orgs_input else []
        
        # --- Diagnostic before calling make_treemap_data ---
        if 'has_robot' in df_current_models.columns:
            robot_count_before_treemap = df_current_models['has_robot'].sum()
            print(f"DIAGNOSTIC (ui_generate_plot_controller): df_current_models entering make_treemap_data has {robot_count_before_treemap} 'has_robot' models.")
        # --- End Diagnostic ---

        treemap_df = make_treemap_data(df_current_models, metric_choice, k_orgs, tag_to_use, pipeline_to_use, size_to_use, orgs_to_skip)
        
        title_labels = {"downloads": "Downloads (last 30 days)", "downloadsAllTime": "Downloads (All Time)", "likes": "Likes"}
        chart_title = f"HuggingFace Models - {title_labels.get(metric_choice, metric_choice)} by Organization"
        plotly_fig = create_treemap(treemap_df, metric_choice, chart_title)
        if treemap_df.empty:
            plot_stats_md = "No data matches the selected filters. Try adjusting your filters."
        else:
            total_items_in_plot = len(treemap_df['id'].unique())
            total_value_in_plot = treemap_df[metric_choice].sum()
            plot_stats_md = (f"## Plot Statistics\n- **Models shown**: {total_items_in_plot:,}\n- **Total {metric_choice}**: {int(total_value_in_plot):,}")
        return plotly_fig, plot_stats_md

    demo.load(
        fn=lambda progress=gr.Progress(track_tqdm=True): ui_load_data_controller(force_refresh_ui_trigger=False, progress=progress),
        inputs=[],
        outputs=[models_data_state, loading_complete_state, data_info_md, status_message_md]
    )
    refresh_data_button.click(
        fn=lambda progress=gr.Progress(track_tqdm=True): ui_load_data_controller(force_refresh_ui_trigger=True, progress=progress),
        inputs=[], 
        outputs=[models_data_state, loading_complete_state, data_info_md, status_message_md]
    )
    generate_plot_button.click(
        fn=ui_generate_plot_controller,
        inputs=[count_by_dropdown, filter_choice_radio, tag_filter_dropdown, pipeline_filter_dropdown,
                size_filter_dropdown, top_k_slider, skip_orgs_textbox, models_data_state],
        outputs=[plot_output, status_message_md]
    )

if __name__ == "__main__":
    if not os.path.exists(PROCESSED_PARQUET_FILE_PATH):
        print(f"WARNING: Pre-processed data file '{PROCESSED_PARQUET_FILE_PATH}' not found.")
        print("It is highly recommended to run the preprocessing script (e.g., preprocess.py) first.") # Corrected script name
    else:
        print(f"Found pre-processed data file: '{PROCESSED_PARQUET_FILE_PATH}'.")
    demo.launch()

# --- END OF FILE app.py ---