Spaces:
Running
Running
File size: 7,265 Bytes
4862c84 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 |
"""Helpers for the **View Clusters** tab – both the interactive HTML and
fallback dataframe view."""
from typing import List
import pandas as pd
from .state import app_state
from .utils import (
search_clusters_by_text,
search_clusters_only,
create_interactive_cluster_viewer,
get_cluster_statistics,
format_cluster_dataframe,
)
__all__ = ["view_clusters_interactive", "view_clusters_table"]
# ---------------------------------------------------------------------------
# Interactive HTML view
# ---------------------------------------------------------------------------
def view_clusters_interactive(
selected_models: List[str],
cluster_level: str,
search_term: str = "",
) -> str:
if app_state["clustered_df"] is None:
return (
"<p style='color: #e74c3c; padding: 20px;'>❌ Please load data first "
"using the 'Load Data' tab</p>"
)
df = app_state["clustered_df"].dropna(subset=["property_description"]).copy()
# Apply search filter first
if search_term and search_term.strip():
df = search_clusters_only(df, search_term.strip(), cluster_level)
# Build interactive viewer
cluster_html = create_interactive_cluster_viewer(df, selected_models, cluster_level)
# Statistics summary at the top
stats = get_cluster_statistics(df, selected_models)
if not stats:
return (
"<p style='color: #e74c3c; padding: 20px;'>❌ No cluster data available</p>"
)
# Get additional metrics from cluster_scores
cluster_scores = app_state.get("metrics", {}).get("cluster_scores", {})
# Calculate average quality scores and frequency
total_frequency = 0
quality_scores_list = []
metric_names = set()
for cluster_name, cluster_data in cluster_scores.items():
total_frequency += cluster_data.get("proportion", 0) * 100
quality_scores = cluster_data.get("quality", {})
if quality_scores:
quality_scores_list.extend(quality_scores.values())
metric_names.update(quality_scores.keys())
avg_quality = sum(quality_scores_list) / len(quality_scores_list) if quality_scores_list else 0
metrics_suffix = f" ({', '.join(sorted(metric_names))})" if metric_names else ""
stats_html = f"""
<div style="
background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%);
color: white;
padding: 20px;
border-radius: 8px;
margin-bottom: 20px;
box-shadow: 0 4px 6px rgba(0,0,0,0.1);
">
<h3 style="margin: 0 0 15px 0;">Cluster Statistics</h3>
<div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 15px;">
<div>
<div style="font-size: 24px; font-weight: bold;">{stats['total_properties']:,}</div>
<div style="opacity: 0.9;">Total Properties</div>
</div>
<div>
<div style="font-size: 24px; font-weight: bold;">{stats['total_models']}</div>
<div style="opacity: 0.9;">Models</div>
</div>
<div>
<div style="font-size: 24px; font-weight: bold;">{avg_quality:.3f}</div>
<div style="opacity: 0.9;">Avg Quality{metrics_suffix}</div>
</div>
"""
if cluster_level == "fine" and "fine_clusters" in stats:
stats_html += f"""
<div>
<div style="font-size: 24px; font-weight: bold;">{stats['fine_clusters']}</div>
<div style="opacity: 0.9;">Fine Clusters</div>
</div>
<div>
<div style="font-size: 24px; font-weight: bold;">{stats['avg_properties_per_fine_cluster']:.1f}</div>
<div style="opacity: 0.9;">Avg Properties/Cluster</div>
</div>
"""
elif cluster_level == "coarse" and "coarse_clusters" in stats:
stats_html += f"""
<div>
<div style="font-size: 24px; font-weight: bold;">{stats['coarse_clusters']}</div>
<div style="opacity: 0.9;">Coarse Clusters</div>
</div>
<div>
<div style="font-size: 24px; font-weight: bold;">{stats['avg_properties_per_coarse_cluster']:.1f}</div>
<div style="opacity: 0.9;">Avg Properties/Cluster</div>
</div>
"""
stats_html += """
</div>
</div>
"""
# Add a note if coarse clusters were requested but not available
if cluster_level == "coarse" and "coarse_clusters" not in stats and "fine_clusters" in stats:
stats_html += """
<div style="
background: #fff3cd;
border-left: 4px solid #ffc107;
padding: 10px 15px;
margin-bottom: 15px;
border-radius: 4px;
">
⚠️ <strong>Note:</strong> Coarse clusters not available in this dataset. Showing fine clusters instead.
</div>
"""
# Additional filter chips
filter_info = ""
if search_term and search_term.strip():
filter_info += f"""
<div style="
background: #e3f2fd;
border-left: 4px solid #2196f3;
padding: 10px 15px;
margin-bottom: 15px;
border-radius: 4px;
">
🔍 <strong>Search Filter:</strong> "{search_term}"
</div>
"""
if selected_models:
filter_info += f"""
<div style="
background: #f3e5f5;
border-left: 4px solid #9c27b0;
padding: 10px 15px;
margin-bottom: 15px;
border-radius: 4px;
">
🎯 <strong>Selected Models:</strong> {', '.join(selected_models)}
</div>
"""
return stats_html + filter_info + cluster_html
# ---------------------------------------------------------------------------
# Dataframe fallback view
# ---------------------------------------------------------------------------
def view_clusters_table(
selected_models: List[str],
cluster_level: str,
search_term: str = "",
) -> pd.DataFrame:
if app_state["clustered_df"] is None:
return pd.DataFrame({"Message": ["Please load data first using the 'Load Data' tab"]})
df = app_state["clustered_df"].copy()
if search_term and search_term.strip():
df = search_clusters_only(df, search_term.strip(), cluster_level)
formatted_df = format_cluster_dataframe(df, selected_models, cluster_level)
if formatted_df.empty:
if search_term and search_term.strip():
return pd.DataFrame({"Message": [f"No results found for search term '{search_term}'. Try a different search term."]})
elif selected_models:
available_models = df["model"].unique().tolist() if "model" in df.columns else []
return pd.DataFrame({"Message": [
f"No data found for selected models: {', '.join(selected_models)}. "
f"Available models: {', '.join(available_models)}"
]})
else:
return pd.DataFrame({"Message": [
"No data available. Please check your data files and try reloading."
]})
return formatted_df |