# --- 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="%{label}
%{value:,} " + count_by + "
%{percentRoot:.2%} of total")
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 ---