Create model.py

model.py

@@ -0,0 +1,42 @@
+import joblib
+import pandas as pd
+import numpy as np
+from landmarks import normalize_landmarks, calculate_angles
+
+@st.cache_resource
+def load_model():
+    """Load the pre-trained Random Forest model."""
+    try:
+        return joblib.load('best_random_forest_model.pkl')
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        return None
+
+def process_and_predict(image, model):
+    """
+    Process the uploaded image to extract hand landmarks and predict the ASL sign.
+    
+    Parameters:
+        image (numpy.ndarray): The uploaded image.
+        model (sklearn.base.BaseEstimator): The pre-trained model.
+
+    Returns:
+        tuple: A tuple containing predicted probabilities and landmarks.
+    """
+    mp_hands = mp.solutions.hands
+    with mp_hands.Hands(static_image_mode=True, max_num_hands=1, min_detection_confidence=0.5) as hands:
+        image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        results = hands.process(image_rgb)
+        
+        if results.multi_hand_landmarks:
+            landmarks = np.array([[lm.x, lm.y] for lm in results.multi_hand_landmarks[0].landmark])
+            landmarks_normalized = normalize_landmarks(landmarks)
+            angles = calculate_angles(landmarks_normalized)
+            
+            angle_columns = [f'angle_{i}' for i in range(len(angles))]
+            angles_df = pd.DataFrame([angles], columns=angle_columns)
+            
+            probabilities = model.predict_proba(angles_df)[0]
+            return probabilities, landmarks
+    
+    return None, None