Update app.py

app.py

@@ -1,11 +1,7 @@
 import streamlit as st
-import cv2
-import numpy as np
-import mediapipe as mp
-import joblib
-import pandas as pd
-from numpy.linalg import norm
-import matplotlib.pyplot as plt
+from model import load_model, process_and_predict
+from landmarks import normalize_landmarks, calculate_angles
+from visualization import plot_hand_landmarks
 import os
 
 st.set_page_config(layout="wide")
@@ -15,76 +11,6 @@ all_alphabets = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
 excluded_alphabets = 'DMNPTUVXZ'
 working_alphabets = ''.join(set(all_alphabets) - set(excluded_alphabets))
 
-# Function to load the Random Forest model
-@st.cache_resource
-def load_model():
-    try:
-        return joblib.load('best_random_forest_model.pkl')
-    except Exception as e:
-        st.error(f"Error loading model: {e}")
-        return None
-
-# Load the model using the cached function
-model = load_model()
-
-# Ensure the model is loaded before proceeding
-if model is None:
-    st.stop()
-
-# Function to normalize landmarks
-def normalize_landmarks(landmarks):
-    center = np.mean(landmarks, axis=0)
-    landmarks_centered = landmarks - center
-    std_dev = np.std(landmarks_centered, axis=0)
-    landmarks_normalized = landmarks_centered / std_dev
-    return np.nan_to_num(landmarks_normalized)
-
-# Function to calculate angles between landmarks
-def calculate_angles(landmarks):
-    angles = []
-    for i in range(20):
-        for j in range(i + 1, 21):
-            vector = landmarks[j] - landmarks[i]
-            angle_x = np.arccos(np.clip(vector[0] / norm(vector), -1.0, 1.0))
-            angle_y = np.arccos(np.clip(vector[1] / norm(vector), -1.0, 1.0))
-            angles.extend([angle_x, angle_y])
-    return angles
-
-# Function to process image and predict alphabet
-def process_and_predict(image):
-    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
-
-# Function to plot hand landmarks
-def plot_hand_landmarks(landmarks, title):
-    fig, ax = plt.subplots(figsize=(5, 5))
-    ax.scatter(landmarks[:, 0], landmarks[:, 1], c='blue', s=20)
-    mp_hands = mp.solutions.hands
-    for connection in mp_hands.HAND_CONNECTIONS:
-        start_idx = connection[0]
-        end_idx = connection[1]
-        ax.plot([landmarks[start_idx, 0], landmarks[end_idx, 0]],
-                [landmarks[start_idx, 1], landmarks[end_idx, 1]], 'r-', linewidth=1)
-    ax.invert_yaxis()
-    ax.set_title(title, fontsize=12)
-    ax.axis('off')
-    return fig
-
 # README content
 readme_content = f"""
 ## How it works
@@ -110,6 +36,13 @@ Note: The model's performance may vary and is subject to improvement.
 The "View Hand Landmarks" tab allows users to see hand landmarks for pre-loaded ASL signs.
 """
 
+# Load the model
+model = load_model()
+
+# Ensure the model is loaded before proceeding
+if model is None:
+    st.stop()
+
 # Streamlit app
 st.title("ASL Recognition App")
 
@@ -130,16 +63,16 @@ with tab1:
                 col1, col2 = st.columns(2)
                 with col1:
                     st.image(image, caption="Uploaded Image", use_column_width=True)
-                
-                probabilities, landmarks = process_and_predict(image)
-                
+
+                probabilities, landmarks = process_and_predict(image, model)
+
                 if probabilities is not None and landmarks is not None:
                     with col2:
                         st.subheader("Top 5 Predictions:")
                         top_indices = np.argsort(probabilities)[::-1][:5]
                         for i in top_indices:
                             st.write(f"{model.classes_[i]}: {probabilities[i]:.2f}")
-                    
+
                     fig = plot_hand_landmarks(landmarks, "Detected Hand Landmarks")
                     st.pyplot(fig)
                 else:
@@ -163,7 +96,7 @@ with tab2:
                     try:
                         image = cv2.imread(image_path)
                         if image is not None:
-                            probabilities, landmarks = process_and_predict(image)
+                            probabilities, landmarks = process_and_predict(image, model)
                             if landmarks is not None:
                                 fig = plot_hand_landmarks(landmarks, f"Hand Landmarks for {alphabet}")
                                 st.pyplot(fig)
@@ -174,4 +107,4 @@ with tab2:
                     except Exception as e:
                         st.error(f"Error processing image for {alphabet}")
                 else:
-                    st.error(f"Image not found for {alphabet}")
+                    st.error(f"Image not found for {alphabet}")