Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,6 +1,5 @@
 import streamlit as st
 from ultralytics import YOLO
-from keras.utils import get_file
 from PIL import Image
 import io
 
@@ -12,14 +11,13 @@ st.set_page_config(
     initial_sidebar_state="expanded",
 )
 
-# --- NEW Theme-Aware Custom CSS ---
+# --- Theme-Aware Custom CSS ---
 st.markdown("""
 <style>
     /* --- Base Styles (Shared) --- */
     .st-expander {
         border-radius: 10px;
     }
-
     /* --- LIGHT THEME --- */
     [data-theme="light"] .stApp {
         background-color: #f0f2f6; /* Light gray background */
@@ -34,7 +32,6 @@ st.markdown("""
         border: 1px solid #ddd;
         background-color: #ffffff;
     }
-
     /* --- DARK THEME --- */
     [data-theme="dark"] .stApp {
         background-color: #0E1117; /* Default Streamlit dark background */
@@ -57,7 +54,6 @@ st.markdown("""
 """, unsafe_allow_html=True)
 
 # --- Model Loading ---
-# Use st.cache_resource to load the model only once
 @st.cache_resource
 def load_model(model_path):
     """
@@ -71,13 +67,8 @@ def load_model(model_path):
         st.error(f"Error loading model: {e}")
         return None
 
-# MODEL_PATH = get_file(
-#     force_download=False,
-#     origin="https://huggingface.co/CineAI/RSSI/resolve/main/rssi_last.pt",
-#     file_hash="c689b8a006e574bf5e4f2ff252230cb59ac3de42ca24875000144c2f552bd837"
-# )
+# Path to the model file inside the 'src' directory
 MODEL_PATH = './src/rssi_last.pt'
-print(f"MODEL_PATH: {MODEL_PATH}")
 model = load_model(MODEL_PATH)
 
 # --- Sidebar ---
@@ -89,7 +80,6 @@ st.sidebar.markdown("---")
 uploaded_file = st.sidebar.file_uploader(
     "Upload an image...", type=["jpg", "jpeg", "png"]
 )
-
 st.sidebar.markdown("---")
 st.sidebar.markdown(
     "**About this App**\n\n"
@@ -99,10 +89,8 @@ st.sidebar.markdown(
 
 # --- Main Page ---
 st.title("🖼️ Custom Object Detection with YOLO")
-st.write("Upload an image via the sidebar to see the model's predictions.")
 
 if uploaded_file is not None:
-    print(f"uploaded_file: {uploaded_file}")
     # Read the uploaded image file
     image_data = uploaded_file.getvalue()
     original_image = Image.open(io.BytesIO(image_data))
@@ -112,7 +100,8 @@ if uploaded_file is not None:
     
     with col1:
         st.subheader("Original Image")
-        st.image(original_image, caption="Your uploaded image.", use_column_width=True)
+        # CHANGED: use_container_width is the new, recommended parameter
+        st.image(original_image, caption="Your uploaded image.", use_container_width=True)
 
     if model:
         # Perform inference
@@ -129,7 +118,8 @@ if uploaded_file is not None:
 
         with col2:
             st.subheader("Detected Objects")
-            st.image(annotated_image_rgb, caption="Image with detected objects.", use_column_width=True)
+            # CHANGED: use_container_width is the new, recommended parameter
+            st.image(annotated_image_rgb, caption="Image with detected objects.", use_container_width=True)
 
         # Display detection details
         st.subheader("Detection Details")