Update app.py

app.py

@@ -71,10 +71,10 @@ def extract_features(sequence):
     all_features_dict.update(pseudo_features)
 
     feature_values = list(all_features_dict.values())
-    feature_array = np.array(feature_values).reshape(1, -1) # Reshape to (1, n_features) - CORRECT SHAPE
+    feature_array = np.array(feature_values).reshape(-1, 1) # Reshape to (1, n_features) - CORRECT SHAPE
     print(f"Shape of feature_array before normalization: {feature_array.shape}") # Debug print
 
-    normalized_features = scaler.transform(feature_array) # Normalize - NO TRANSPOSE
+    normalized_features = scaler.transform(feature_array.T) # Normalize - NO TRANSPOSE
     normalized_features = normalized_features.flatten() # Flatten AFTER normalization if needed
 
 
@@ -84,7 +84,7 @@ def extract_features(sequence):
             selected_feature_dict[feature] = normalized_features[i]
 
     selected_feature_df = pd.DataFrame([selected_feature_dict])
-    selected_feature_array = selected_feature_df.to_numpy()
+    selected_feature_array = selected_feature_df.T.to_numpy()
 
     return selected_feature_array
 
@@ -95,8 +95,8 @@ def predict(sequence):
     if isinstance(features, str) and features.startswith("Error:"):
         return features
 
-    prediction = model.predict(features)[0]
-    probabilities = model.predict_proba(features)[0]
+    prediction = model.predict(features.T)[0]
+    probabilities = model.predict_proba(features.T)[0]
 
     if prediction == 0:
         return f"{probabilities[0] * 100:.2f}% chance of being an Antimicrobial Peptide (AMP)"