--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/predict_source.py	Wed Apr 29 15:04:37 2026 +0000
@@ -0,0 +1,96 @@
+import argparse
+import sys
+import os
+import pandas as pd
+import joblib
+import warnings
+
+# Suppress scikit-learn warnings about feature names if they pop up
+warnings.filterwarnings("ignore", category=UserWarning)
+
+def main():
+    parser = argparse.ArgumentParser(description="Predict the source of an isolate using a trained Random Forest model and Mash distances.")
+    parser.add_argument("-i", "--input", required=True, help="Input Mash screen/dist file for one or more isolates.")
+    
+    # --- KEY FIX 1: Replaced -m and -f with a single -b (bundle) argument ---
+    parser.add_argument("-b", "--bundle", required=True, help="Path to the bundled model and features (.joblib file)")
+    
+    parser.add_argument("-t", "--threshold", type=float, default=0.95, help="Mash identity threshold (default: 0.95)")
+    parser.add_argument("-o", "--output", default="predictions.tsv", help="Output file for predictions (default: predictions.tsv)")
+    args = parser.parse_args()
+
+    print(f"Loading model bundle: {args.bundle}")
+    
+    try:
+        # --- KEY FIX 2: Load the dictionary and extract both pieces ---
+        bundle = joblib.load(args.bundle)
+        rf_model = bundle['model']
+        training_features = bundle['features']
+        print(f"Successfully loaded model and {len(training_features)} features.")
+    except Exception as e:
+        print(f"FATAL: Error loading model bundle: {e}")
+        sys.exit(1)
+
+    print(f"Loading and processing input data: {args.input}")
+    
+    try:
+        df = pd.read_csv(args.input, sep='\s+', header=None, engine='python')
+
+        # Your format is from 'mash screen', where the columns are:
+        # Identity, Shared-hashes, Median-multiplicity, P-value, Query-ID
+        if len(df.columns) >= 5:
+            print("--> Standard headerless Mash output detected.")
+            # Keep only the first 5 columns to be safe
+            df = df.iloc[:, :5]
+            df.columns = ['Identity', 'Shared_Hashes', 'Median_Multiplicity', 'P_value', 'Plasmid_ID']
+            
+            # The 'Identity' is already the first column, just convert it to numeric
+            df['Identity'] = pd.to_numeric(df['Identity'], errors='coerce')
+            
+            # We need to manually add the 'Run' column. For screen output, the Query-ID (isolate name)
+            # is not present in the file itself. We must get it from the filename.
+            run_id = os.path.splitext(os.path.basename(args.input))[0]
+            df['Run'] = run_id
+        else:
+            print(f"FATAL: Input file format not recognized. Expected at least 5 columns for Mash output, but got {len(df.columns)}.")
+            sys.exit(1)
+            
+        df.dropna(subset=['Identity'], inplace=True)
+        df['Run'] = df['Run'].astype(str).str.strip()
+        
+    except Exception as e: 
+        print(f"FATAL: Error reading input file '{args.input}'. Error: {e}")
+        sys.exit(1)
+
+    print(f"Filtering features (Identity >= {args.threshold})...")
+    filtered_df = df[df['Identity'] >= args.threshold].copy()
+    
+    if filtered_df.empty:
+        print("Warning: No plasmid hits met the identity threshold. Cannot make a prediction.")
+        sys.exit(0)
+
+    new_data_matrix = filtered_df.pivot_table(index='Run', columns='Plasmid_ID', values='Identity', fill_value=0)
+    
+    print("Aligning input features with the trained model...")
+    aligned_matrix = pd.DataFrame(0, index=new_data_matrix.index, columns=training_features)
+    common_plasmids = new_data_matrix.columns.intersection(training_features)
+    aligned_matrix[common_plasmids] = new_data_matrix[common_plasmids]
+
+    print(f"Making predictions for {len(aligned_matrix)} isolate(s)...")
+    predictions = rf_model.predict(aligned_matrix)
+    probabilities = rf_model.predict_proba(aligned_matrix)
+    max_probs = probabilities.max(axis=1)
+
+    results_df = pd.DataFrame({
+        'Run': aligned_matrix.index,
+        'Predicted_Source': predictions,
+        'Confidence_Score': max_probs
+    })
+
+    results_df.to_csv(args.output, sep='\t', index=False)
+    print(f"\n✅ Predictions complete! Saved to {args.output}")
+    print("--- PREDICTION RESULTS ---")
+    print(results_df.to_string(index=False))
+
+if __name__ == "__main__":
+    main()