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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
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