Update streamlit_app.py

streamlit_app.py

@@ -3,10 +3,19 @@ import cv2
 import numpy as np
 import tensorflow as tf
 from PIL import Image
+import matplotlib.pyplot as plt
+import seaborn as sns
 import pandas as pd
 import mediapipe as mp
-from datetime import datetime
+import tempfile
+import os
+import json
+import time
+from typing import List, Dict, Optional
 import plotly.express as px
+import plotly.graph_objects as go
+from datetime import datetime
+
 
 st.set_page_config(
     page_title="ASL Recognition App",
@@ -15,6 +24,7 @@ st.set_page_config(
     initial_sidebar_state="expanded"
 )
 
+
 st.markdown("""
 <style>
     .main-header {
@@ -24,7 +34,7 @@ st.markdown("""
         margin-bottom: 2rem;
     }
     .prediction-box {
-        background-color: #262730;
+        background-color: #262730;  /* dark gray-blue */
         padding: 1rem;
         border-radius: 10px;
         border-left: 5px solid #1f77b4;
@@ -84,7 +94,7 @@ class ASLStreamlitApp:
         image = np.expand_dims(image, axis=0)
         return image
 
-    def extract_hand_region(self, image: np.ndarray):
+    def extract_hand_region(self, image: np.ndarray) -> Optional[np.ndarray]:
         try:
             rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
             results = self.hands.process(rgb_image)
@@ -108,7 +118,7 @@ class ASLStreamlitApp:
             st.error(f"Error extracting hand: {str(e)}")
             return None, None
 
-    def predict_sign(self, image: np.ndarray, use_hand_detection: bool = True):
+    def predict_sign(self, image: np.ndarray, use_hand_detection: bool = True) -> Dict:
         if MODEL is None:
             st.error("Model not loaded!")
             return {}
@@ -147,7 +157,7 @@ class ASLStreamlitApp:
             st.error(f"Prediction error: {str(e)}")
             return {}
 
-    def display_prediction_results(self, results):
+    def display_prediction_results(self, results: Dict):
         if not results:
             return
         predicted_class = results['predicted_class']
@@ -185,13 +195,13 @@ class ASLStreamlitApp:
             'confidence': confidence
         })
 
-    def display_image_with_detection(self, results):
+    def display_image_with_detection(self, results: Dict):
         if not results or 'original_image' not in results:
             return
         col1, col2 = st.columns(2)
         with col1:
             st.subheader("Original Image")
-            original = results['original_image'].copy()
+            original = results['original_image']
             if results['hand_detected'] and results['bbox']:
                 x_min, y_min, x_max, y_max = results['bbox']
                 cv2.rectangle(original, (x_min, y_min), (x_max, y_max), (0, 255, 0), 3)
@@ -221,7 +231,7 @@ class ASLStreamlitApp:
             if st.button("Save Word"):
                 if st.session_state.current_word:
                     st.success(f"Saved: '{st.session_state.current_word}'")
-                    # You can add code here to save to a file/db
+                    # Save to file/db if needed
 
     def prediction_history_interface(self):
         st.subheader("📊 Prediction History")
@@ -324,4 +334,4 @@ def main():
     app.run()
 
 if __name__ == "__main__":
-    main()
+    main()