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import ast
import os
import json
import pickle
import numpy as np
from tqdm import tqdm
import pandas as pd
from datetime import datetime
import yaml
# Import your actual modules exactly as app.py does
from utils.visualizations import get_instances, load_interp_space, trigger_precomputed_region, handle_zoom_with_retries
from utils.ui import update_task_display
def load_config(path="config/config.yaml"):
with open(path, "r") as f:
return yaml.safe_load(f)
def precompute_all_caches(
models_to_test=None,
instances_to_process=None,
config_path="config/config.yaml"
):
"""
Precompute all cache files using the EXACT same methods as app.py.
This follows the exact flow: load_task → update_task_display → run_visualization
"""
if models_to_test is None:
models_to_test = [
'gabrielloiseau/LUAR-MUD-sentence-transformers',
'gabrielloiseau/LUAR-CRUD-sentence-transformers',
'miladalsh/light-luar',
'AnnaWegmann/Style-Embedding'
]
print("=" * 60)
print("CACHE PRECOMPUTATION STARTED")
print(f"Timestamp: {datetime.now()}")
print(f"Models to test: {len(models_to_test)}")
print("=" * 60)
# Load configuration and instances EXACTLY like app.py
cfg = load_config(config_path)
print(f"Configuration loaded from {config_path}")
print(f"config : \n{cfg}")
instances, instance_ids = get_instances(cfg['instances_to_explain_path'])
interp = load_interp_space(cfg)
clustered_authors_df = interp['clustered_authors_df']
if instances_to_process is None:
instances_to_process = instance_ids
print(f"Processing {len(instances_to_process)} instances with {len(models_to_test)} models")
total_combinations = len(models_to_test) * len(instances_to_process)
current_combination = 0
cache_stats = {
'embeddings_generated': 0,
'tsne_computed': 0,
'regions_computed': 0,
'errors': []
}
for model_name in models_to_test:
print(f"\n{'=' * 40}")
print(f"PROCESSING MODEL: {model_name}")
print(f"{'=' * 40}")
for instance_id in tqdm(instances_to_process, desc=f"Processing instances for {model_name.split('/')[-1]}"):
current_combination += 1
try:
print(f"\n[{current_combination}/{total_combinations}] Processing Instance {instance_id}")
# STEP 1: Replicate the exact flow from load_button.click()
print(" → Replicating load_button.click() flow...")
# Create ground truth (using placeholder since we're caching)
ground_truth_author = None # Will be determined by the instance data
# Call update_task_display EXACTLY like app.py does
task_results = update_task_display(
mode="Predefined HRS Task", # Always use predefined for caching
iid=f"Task {instance_id}",
instances=instances,
background_df=clustered_authors_df,
mystery_file=None, # Not used for predefined
cand1_file=None, # Not used for predefined
cand2_file=None, # Not used for predefined
cand3_file=None, # Not used for predefined
true_author=ground_truth_author,
model_radio=model_name,
custom_model_input=""
)
# Extract the results exactly like app.py expects
(header_html, mystery_html, c0_html, c1_html, c2_html,
mystery_state, c0_state, c1_state, c2_state,
task_authors_embeddings_df, background_authors_embeddings_df,
predicted_author, ground_truth_author) = task_results
print(f" ✓ Embeddings generated for {len(task_authors_embeddings_df)} task authors")
print(f" ✓ Background embeddings: {len(background_authors_embeddings_df)} authors")
cache_stats['embeddings_generated'] += 1
# STEP 2: Replicate the exact flow from run_btn.click()
print(" → Replicating run_btn.click() flow...")
# Call visualize_clusters_plotly EXACTLY like app.py does
viz_results = visualize_clusters_plotly(
iid=int(instance_id),
cfg=cfg,
instances=instances,
model_radio=model_name,
custom_model_input="",
task_authors_df=task_authors_embeddings_df,
background_authors_embeddings_df=background_authors_embeddings_df,
pred_idx=predicted_author,
gt_idx=ground_truth_author
)
# Extract results exactly like app.py expects
(fig, style_names, bg_proj, bg_ids, bg_authors_df,
precomputed_regions_state, precomputed_regions_radio) = viz_results
print(f" ✓ t-SNE projection computed")
print(f" ✓ Precomputed regions generated")
cache_stats['tsne_computed'] += 1
cache_stats['regions_computed'] += 1
print(f" ✓ Instance {instance_id} with model {model_name} completed successfully")
print(" → Testing region zoom simulation...")
if precomputed_regions_state:
regions_dict = ast.literal_eval(precomputed_regions_state)
test_regions = list(regions_dict.keys())
for region_name in test_regions:
try:
print(f" → Testing region: {region_name}")
# Step 3a: Simulate region selection (trigger_precomputed_region)
zoom_payload = trigger_precomputed_region(region_name, regions_dict)
if zoom_payload: # Only proceed if we got a valid zoom payload
# Step 3b: Simulate axis_ranges.change() (handle_zoom_with_retries)
zoom_results = handle_zoom_with_retries(
event_json=zoom_payload,
bg_proj=bg_proj,
bg_lbls=bg_ids,
clustered_authors_df=background_authors_embeddings_df,
task_authors_df=task_authors_embeddings_df
)
# Extract results like app.py does
(features_rb_update, gram2vec_rb_update, llm_style_feats_analysis,
feature_list_state, visible_zoomed_authors) = zoom_results
print(f" ✓ LLM features cached for region: {region_name}")
except Exception as e:
print(f" ✗ Failed to cache features for region {region_name}: {e}")
# Continue with other regions even if one fails
continue
except Exception as e:
error_msg = f"Error processing instance {instance_id} with model {model_name}: {str(e)}"
print(f" ✗ {error_msg}")
cache_stats['errors'].append(error_msg)
import traceback
traceback.print_exc()
continue
# Print final statistics
print("\n" + "=" * 60)
print("CACHE PRECOMPUTATION COMPLETED")
print("=" * 60)
print(f"Embeddings generated: {cache_stats['embeddings_generated']}")
print(f"t-SNE projections computed: {cache_stats['tsne_computed']}")
print(f"Region sets computed: {cache_stats['regions_computed']}")
print(f"Errors encountered: {len(cache_stats['errors'])}")
if cache_stats['errors']:
print("\nERROR DETAILS:")
for error in cache_stats['errors']:
print(f" - {error}")
return cache_stats
# Import the exact functions your app uses
from utils.visualizations import visualize_clusters_plotly
if __name__ == "__main__":
# Test with a small subset first
instances=[i for i in range(20)] # First 20 instances for testing
cache_stats = precompute_all_caches(
models_to_test=[
'gabrielloiseau/LUAR-MUD-sentence-transformers'
],
instances_to_process=instances
)
print(f"\nCache precomputation completed with {len(cache_stats['errors'])} errors.") |