Update app.py

app.py

@@ -1,4 +1,4 @@
-# app.py
+# streamlit_yolo_app.py
 
 import streamlit as st
 import cv2
@@ -7,39 +7,21 @@ from ultralytics import YOLO
 from PIL import Image
 
 # -------------------------------
-# Streamlit Page Config
+# Streamlit App Title
 # -------------------------------
-st.set_page_config(page_title="YOLOv8 Object Detection", page_icon="🤖", layout="wide")
-
-# -------------------------------
-# App Title & Description
-# -------------------------------
-st.title("🤖 YOLOv8 Object Detection")
-st.markdown(
-    """
-    Welcome to the **YOLOv8 Object Detection App** 🎯  
-
-    - Upload an image and let YOLOv8 detect objects in real-time.  
-    - This demo is built with **[Ultralytics YOLOv8](https://docs.ultralytics.com/)** and **[Streamlit](https://streamlit.io/)**.  
-    - Download the annotated image with detected objects.  
-
-    ---
-    """
-)
+st.title("YOLOv8 Object Detection")
+model = YOLO("yolov8n.pt")
 
 # -------------------------------
 # Load YOLO Model
-# -------------------------------
 @st.cache_resource
-def load_model(model_name="yolov8n.pt"):
+def load_model(model_name):
     return YOLO(model_name)
 
-model = load_model()
-
 # -------------------------------
 # Upload Image
 # -------------------------------
-uploaded_file = st.file_uploader("📷 Upload an image", type=["jpg", "jpeg", "png"])
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
 
 if uploaded_file is not None:
     # Load uploaded image
@@ -47,12 +29,15 @@ if uploaded_file is not None:
     img_array = np.array(image)
 
     # Perform Inference
-    with st.spinner("🔍 Detecting objects..."):
-        results = model(img_array)
+    results = model(img_array)
 
     # Annotated results
     annotated_img = results[0].plot()
 
+    # Get shape of annotated image
+    h, w, c = annotated_img.shape
+    st.write(f"**Annotated Image Shape:** {h} x {w} x {c}")
+
     # Display Input and Output
     col1, col2 = st.columns(2)
     with col1:
@@ -60,11 +45,8 @@ if uploaded_file is not None:
     with col2:
         st.image(annotated_img, caption="Detected Objects", use_column_width=True)
 
-    # Show some info about the result
-    st.success(f"✅ Objects detected: {len(results[0].boxes)}")
-
     # Option to download the annotated image
     result_bgr = cv2.cvtColor(annotated_img, cv2.COLOR_RGB2BGR)
     cv2.imwrite("annotated_output.jpg", result_bgr)
     with open("annotated_output.jpg", "rb") as f:
-        st.download_button("📥 Download Annotated Image", f, "annotated_output.jpg")
+        st.download_button("Download Annotated Image", f, "annotated_output.jpg")