Delete model.py

model.py

@@ -1,42 +0,0 @@
-import joblib
-import pandas as pd
-import numpy as np
-from landmarks import normalize_landmarks, calculate_angles
-import streamlit as st
-@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