Update app.py

app.py

@@ -1,54 +1,54 @@
-# streamlit_yolo_pose.py
-
-import streamlit as st
-import cv2
-import numpy as np
-from ultralytics import YOLO
-from PIL import Image
-
-# -------------------------------
-# Streamlit App Title
-# -------------------------------
-st.title("YOLOv8 Pose Detection App")
-
-# -------------------------------
-# Load YOLO Pose Model
-# -------------------------------
-@st.cache_resource
-def load_model(model_name="yolov8n-pose.pt"):
-    return YOLO(model_name)
-
-model = load_model()
-
-# -------------------------------
-# Upload Image
-# -------------------------------
-uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
-
-if uploaded_file is not None:
-    # Load uploaded image
-    image = Image.open(uploaded_file).convert("RGB")
-    img_array = np.array(image)
-
-    # Perform Inference
-    results = model(img_array)
-
-    # Annotated results with keypoints
-    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:
-        st.image(image, caption="Original Image", use_column_width=True)
-    with col2:
-        st.image(annotated_img, caption="Pose Detected", use_column_width=True)
-
-    # Option to download the annotated image
-    result_bgr = cv2.cvtColor(annotated_img, cv2.COLOR_RGB2BGR)
-    cv2.imwrite("pose_output.jpg", result_bgr)
-    with open("pose_output.jpg", "rb") as f:
+# streamlit_yolo_pose.py
+
+import streamlit as st
+import cv2
+import numpy as np
+from ultralytics import YOLO
+from PIL import Image
+
+# -------------------------------
+# Streamlit App Title
+# -------------------------------
+st.title("YOLOv8 Pose Detection App")
+
+# -------------------------------
+# Load YOLO Pose Model
+# -------------------------------
+@st.cache_resource
+def load_model(model_name="yolov8n-pose.pt"):
+    return YOLO(model_name)
+
+model = load_model()
+
+# -------------------------------
+# Upload Image
+# -------------------------------
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Load uploaded image
+    image = Image.open(uploaded_file).convert("RGB")
+    img_array = np.array(image)
+
+    # Perform Inference
+    results = model(img_array)
+
+    # Annotated results with keypoints
+    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:
+        st.image(image, caption="Original Image", use_column_width=True)
+    with col2:
+        st.image(annotated_img, caption="Pose Detected", use_column_width=True)
+
+    # Option to download the annotated image
+    result_bgr = cv2.cvtColor(annotated_img, cv2.COLOR_RGB2BGR)
+    cv2.imwrite("pose_output.jpg", result_bgr)
+    with open("pose_output.jpg", "rb") as f:
         st.download_button("Download Pose Image", f, "pose_output.jpg")