Update app.py

app.py

@@ -3,35 +3,26 @@ import cv2
 import numpy as np
 import mediapipe as mp
 import pickle
-import os
-
-# Define the models directory
-models_dir = 'models'
-
-# Load models
-def load_model(file_path):
-    try:
-        with open(file_path, 'rb') as file:
-            return pickle.load(file)
-    except FileNotFoundError:
-        st.error(f"Error: Model file not found at {file_path}")
-    except Exception as e:
-        st.error(f"Error loading model from {file_path}: {e}")
-    return None
+
+# Load models directly
+with open('random_forest_model.pkl', 'rb') as file:
+    random_forest_model = pickle.load(file)
+with open('svm_model.pkl', 'rb') as file:
+    svm_model = pickle.load(file)
+with open('hard_voting_classifier.pkl', 'rb') as file:
+    hard_voting_model = pickle.load(file)
+with open('soft_voting_classifier.pkl', 'rb') as file:
+    soft_voting_model = pickle.load(file)
+with open('label_encoder.pkl', 'rb') as file:
+    label_encoder = pickle.load(file)
 
 models = {
-    'Random Forest': load_model(os.path.join(models_dir, 'random_forest_model.pkl')),
-    'SVM': load_model(os.path.join(models_dir, 'svm_model.pkl')),
-    'Hard Voting': load_model(os.path.join(models_dir, 'hard_voting_classifier.pkl')),
-    'Soft Voting': load_model(os.path.join(models_dir, 'soft_voting_classifier.pkl'))
+    'Random Forest': random_forest_model,
+    'SVM': svm_model,
+    'Hard Voting': hard_voting_model,
+    'Soft Voting': soft_voting_model
 }
 
-# Load label encoder
-label_encoder = load_model(os.path.join(models_dir, 'label_encoder.pkl'))
-if label_encoder is None:
-    st.error("Failed to load label encoder. Exiting.")
-    st.stop()
-
 # Initialize MediaPipe Hands
 mp_hands = mp.solutions.hands
 hands = mp_hands.Hands(static_image_mode=True, max_num_hands=1, min_detection_confidence=0.5)
@@ -67,8 +58,6 @@ def predict_alphabet(image):
     predictions = {}
 
     for model_name, model in models.items():
-        if model is None:
-            continue
         prediction, probability = get_model_predictions(data_aux, model)
         predictions[model_name] = (prediction, probability)