Remove print_consensus_summary from consensus module

Deleted the print_consensus_summary function from consensus.py and removed its import and export from __init__.py. This streamlines the API by eliminating a function primarily used for printing summaries, likely in favor of other reporting or logging mechanisms.

Author: cafferychen777

python/mllmcelltype/__init__.py

@@ -4,7 +4,6 @@
 from .consensus import (
     check_consensus,
     interactive_consensus_annotation,
-    print_consensus_summary,
     process_controversial_clusters,
 )
 from .functions import get_provider
@@ -58,7 +57,6 @@
     "check_consensus",
     "process_controversial_clusters",
     "interactive_consensus_annotation",
-    "print_consensus_summary",
     # URL utilities
     "resolve_provider_base_url",
     "get_default_api_url",

python/mllmcelltype/consensus.py

@@ -1207,104 +1207,3 @@ def interactive_consensus_annotation(
             "max_discussion_rounds": max_discussion_rounds,
         },
     }
-
-
-def print_consensus_summary(result: dict[str, Any]) -> None:
-    """Print a summary of consensus annotation results.
-
-    Args:
-        result: Dictionary containing consensus results from interactive_consensus_annotation
-
-    """
-    if "error" in result:
-        print(f"Error: {result['error']}")
-        return
-
-    print("\n=== CONSENSUS ANNOTATION SUMMARY ===\n")
-
-    # Print metadata
-    metadata = result.get("metadata", {})
-    print(f"Timestamp: {metadata.get('timestamp', 'Unknown')}")
-    print(f"Species: {metadata.get('species', 'Unknown')}")
-    if metadata.get("tissue"):
-        print(f"Tissue: {metadata['tissue']}")
-    models_list = metadata.get("models", [])
-    model_names = []
-    for model in models_list:
-        if isinstance(model, dict):
-            model_names.append(model.get("model", "Unknown"))
-        else:
-            model_names.append(str(model))
-    print(f"Models used: {', '.join(model_names)}")
-    print(f"Consensus threshold: {metadata.get('consensus_threshold', 0.6)}")
-    print()
-
-    # Print controversial clusters
-    controversial = result.get("controversial_clusters", [])
-    if controversial:
-        print(f"Controversial clusters: {len(controversial)} - {', '.join(controversial)}")
-    else:
-        print("No controversial clusters found.")
-    print()
-
-    # Print consensus annotations with consensus proportion and entropy
-    consensus = result.get("consensus", {})
-    consensus_proportion = result.get("consensus_proportion", {})
-    entropy = result.get("entropy", {})
-    resolved = result.get("resolved", {})
-
-    print("Cluster annotations:")
-    for cluster, annotation in sorted(consensus.items(), key=lambda x: x[0]):
-        stored_cp = consensus_proportion.get(cluster, 0)
-        stored_entropy = entropy.get(cluster, 0)
-        if cluster in resolved:
-            # For resolved clusters, show CP and H if available in the discussion logs
-            discussion_logs = result.get("discussion_logs", {})
-            cp_value = "N/A"
-            h_value = "N/A"
-
-            # Try to extract CP and H from discussion logs
-            if cluster in discussion_logs:
-                logs = discussion_logs[cluster]
-                # Check if logs is a list or string
-                # Convert logs to string if it's a list, otherwise use directly
-                logs_text = "\n".join(logs) if isinstance(logs, list) else logs
-
-                # Look for CP and H in the last round
-                for line in reversed(logs_text.split("\n")):
-                    if "Consensus Proportion (CP):" in line:
-                        cp_parts = line.split("Consensus Proportion (CP):")[1].strip().split()
-                        if cp_parts:
-                            cp_value = cp_parts[0]
-                    if "Shannon Entropy (H):" in line:
-                        h_parts = line.split("Shannon Entropy (H):")[1].strip().split()
-                        if h_parts:
-                            h_value = h_parts[0]
-
-            print(f"  Cluster {cluster}: {annotation} [Resolved, CP: {cp_value}, H: {h_value}]")
-        else:
-            # For non-resolved clusters, use the calculated CP and entropy values
-            cp_value = stored_cp
-            h_value = stored_entropy
-
-            # Display different messages based on agreement level
-            # Use the already calculated entropy value
-            print(f"  Cluster {cluster}: {annotation} [CP: {cp_value:.2f}, H: {h_value:.2f}]")
-    print()
-
-    # Print model annotations comparison for controversial clusters
-    if controversial:
-        print("\nModel annotations for controversial clusters:")
-        model_annotations = result.get("model_annotations", {})
-        models = list(model_annotations.keys())
-
-        for cluster in controversial:
-            print(f"\nCluster {cluster}:")
-            for model in models:
-                if cluster in model_annotations.get(model, {}):
-                    print(f"  {model}: {model_annotations[model].get(cluster, 'Unknown')}")
-            if cluster in resolved:
-                print(f"  RESOLVED: {resolved[cluster]}")
-            print()
-
-