Update app.py

app.py

@@ -94,7 +94,13 @@ try:
 except Exception as e:
     print(f"⚠️ Could not patch Gradio client utils: {e}")
 
-MODEL_PATH = "fraud_lgbm_calibrated.pkl"
+# Get the directory where this script is located
+SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) if '__file__' in globals() else os.getcwd()
+
+MODEL_PATH = os.path.join(SCRIPT_DIR, "fraud_lgbm_calibrated.pkl")
+SAMPLE_DATASET_PATH = os.path.join(SCRIPT_DIR, "sample_transactions.csv")
+DATASET_PATH = os.path.join(SCRIPT_DIR, "dataset", "fraudTest.csv")
+
 model = None
 
 def load_model():
@@ -1450,35 +1456,64 @@ with gr.Blocks(
     
     # Function to use dataset file directly
     def use_dataset_file():
-        dataset_path = "dataset/fraudTest.csv"
-        if os.path.exists(dataset_path):
+        if os.path.exists(DATASET_PATH):
             # Return the file path as a string - the predict function will handle it
-            return dataset_path
+            return DATASET_PATH
         else:
             return None
     
     # When button is clicked, trigger prediction with dataset file
     def use_dataset_and_predict(threshold_val):
-        dataset_path = "dataset/fraudTest.csv"
-        if os.path.exists(dataset_path):
+        print(f"DEBUG: Looking for dataset at: {DATASET_PATH}")
+        print(f"DEBUG: File exists: {os.path.exists(DATASET_PATH)}")
+        
+        if os.path.exists(DATASET_PATH):
+            print(f"DEBUG: Found dataset, calling predict_fraud_enhanced...")
             # Call predict function directly with dataset path
-            return predict_fraud_enhanced(dataset_path, threshold_val)
+            return predict_fraud_enhanced(DATASET_PATH, threshold_val)
         else:
+            # Try relative path as fallback
+            fallback_path = "dataset/fraudTest.csv"
+            if os.path.exists(fallback_path):
+                print(f"DEBUG: Found dataset at relative path: {fallback_path}")
+                return predict_fraud_enhanced(fallback_path, threshold_val)
+            
             return (
-                "❌ Error: dataset/fraudTest.csv not found. Please upload a file instead.",
+                f"❌ Error: Dataset not found.\n\n"
+                f"**Expected locations:**\n"
+                f"- {DATASET_PATH}\n"
+                f"- {fallback_path}\n\n"
+                f"Please upload a file instead.",
                 pd.DataFrame(),
                 None, None, None, None, None, None, None, None, None, None, None, None, None, None
             )
     
     # When Sample button is clicked, use sample dataset
     def use_sample_and_predict(threshold_val):
-        sample_path = "sample_transactions.csv"
-        if os.path.exists(sample_path):
+        print(f"DEBUG: Looking for sample dataset at: {SAMPLE_DATASET_PATH}")
+        print(f"DEBUG: File exists: {os.path.exists(SAMPLE_DATASET_PATH)}")
+        print(f"DEBUG: Current working directory: {os.getcwd()}")
+        print(f"DEBUG: Script directory: {SCRIPT_DIR}")
+        
+        if os.path.exists(SAMPLE_DATASET_PATH):
+            print(f"DEBUG: Found sample dataset, calling predict_fraud_enhanced...")
             # Call predict function directly with sample path
-            return predict_fraud_enhanced(sample_path, threshold_val)
+            return predict_fraud_enhanced(SAMPLE_DATASET_PATH, threshold_val)
         else:
+            # Try relative path as fallback
+            fallback_path = "sample_transactions.csv"
+            if os.path.exists(fallback_path):
+                print(f"DEBUG: Found sample dataset at relative path: {fallback_path}")
+                return predict_fraud_enhanced(fallback_path, threshold_val)
+            
             return (
-                "❌ Error: sample_transactions.csv not found. Please run generate_sample_dataset.py first.",
+                f"❌ Error: Sample dataset not found.\n\n"
+                f"**Expected locations:**\n"
+                f"- {SAMPLE_DATASET_PATH}\n"
+                f"- {fallback_path}\n\n"
+                f"**Current working directory:** {os.getcwd()}\n"
+                f"**Script directory:** {SCRIPT_DIR}\n\n"
+                f"Please ensure the sample_transactions.csv file exists in one of these locations.",
                 pd.DataFrame(),
                 None, None, None, None, None, None, None, None, None, None, None, None, None, None
             )