Update app.py

app.py

@@ -18,22 +18,28 @@ logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(
 boundary_model = None
 keras_model = None
 kmer_to_index = None
+model_dir = "models/"  # Adjust this if the models are in a different subdirectory
 try:
-    boundary_model = GenePredictor("best_boundary_aware_model.pth")
-    logging.info("Boundary model loaded successfully")
-except FileNotFoundError as e:
-    logging.warning(f"Boundary model not found: {e}. Skipping Boundary-Aware step.")
+    boundary_path = os.path.join(model_dir, "best_boundary_aware_model.pth")
+    if os.path.exists(boundary_path):
+        boundary_model = GenePredictor(boundary_path)
+        logging.info(f"Boundary model loaded successfully from {boundary_path}")
+    else:
+        logging.warning(f"Boundary model not found at {boundary_path}. Skipping Boundary-Aware step.")
 except Exception as e:
-    logging.error(f"Error loading Boundary model: {e}. Skipping Boundary-Aware step.")
+    logging.error(f"Error loading Boundary model from {boundary_path}: {e}. Skipping Boundary-Aware step.")
 try:
-    keras_model = load_model("best_model.keras")
-    with open("kmer_to_index.pkl", "rb") as f:
-        kmer_to_index = pickle.load(f)
-    logging.info("Keras model and kmer_to_index loaded successfully")
-except FileNotFoundError as e:
-    logging.warning(f"Keras model or kmer_to_index not found: {e}. Skipping Keras step.")
+    keras_path = os.path.join(model_dir, "best_model.keras")
+    kmer_path = os.path.join(model_dir, "kmer_to_index.pkl")
+    if os.path.exists(keras_path) and os.path.exists(kmer_path):
+        keras_model = load_model(keras_path)
+        with open(kmer_path, "rb") as f:
+            kmer_to_index = pickle.load(f)
+        logging.info(f"Keras model and kmer_to_index loaded successfully from {keras_path} and {kmer_path}")
+    else:
+        logging.warning(f"Keras model or kmer_to_index not found at {keras_path} or {kmer_path}. Skipping Keras step.")
 except Exception as e:
-    logging.error(f"Error loading Keras model or kmer_to_index: {e}. Skipping Keras step.")
+    logging.error(f"Error loading Keras model or kmer_to_index from {keras_path} or {kmer_path}: {e}. Skipping Keras step.")
 
 # --------- Utilities ---------
 def predict_with_keras(sequence):
@@ -70,7 +76,7 @@ def run_full_pipeline(dna_input):
                 logging.error(f"Boundary model prediction failed: {e}")
                 step1_out = dna_input
         else:
-            logging.info("Boundary model skipped due to missing file")
+            logging.info("Boundary model skipped due to missing or failed load")
 
         # Step 2: Keras Prediction (Optional)
         step2_out = step1_out  # Fallback to step1_out if model missing
@@ -82,11 +88,11 @@ def run_full_pipeline(dna_input):
                 logging.error(f"Keras prediction failed: {e}")
                 step2_out = step1_out
         else:
-            logging.info("Keras model skipped due to missing file")
+            logging.info("Keras model skipped due to missing or failed load")
 
         # Step 3: Run ML Simplified Tree using PhylogeneticTreeAnalyzer with existing CSV
         analyzer = ml_simplified_tree.PhylogeneticTreeAnalyzer()
-        csv_path = "f gene clean.csv"
+        csv_path = "f gene clean dataset.csv"
         if not os.path.exists(csv_path):
             ml_output = f"Error: Expected CSV file '{csv_path}' not found"
             logging.error(ml_output)
@@ -138,7 +144,7 @@ def run_full_pipeline(dna_input):
 # --------- Gradio Interface and API ---------
 with gr.Blocks() as gr_interface:
     gr.Markdown("# Sequential Phylogenetic Inference Pipeline")
-    gr.Markdown("This pipeline generates a phylogenetic tree using the existing 'f gene clean.csv'. Model-based steps are optional if files are missing.")
+    gr.Markdown("This pipeline generates a phylogenetic tree using the existing 'f gene clean.csv'. Model files are expected in the 'models/' subdirectory and are optional.")
     
     dna_input = gr.Textbox(label="Input DNA Sequence", placeholder="Enter DNA sequence (e.g., ATGCGTAACTAGCTAGCTAA)")
     submit_button = gr.Button("Generate Tree")