Update 2.py

2.py

@@ -1,59 +1,29 @@
-import streamlit as st
-from ultralytics import YOLO
-from PIL import Image
-import tempfile
-import os
-
-# import pkg_resources
-
-# def generate_requirements(filename="requirements.txt"):
-#     """
-#     Generate a requirements.txt file containing installed pip packages and versions.
-#     """
-#     # Get all installed packages
-#     installed_packages = pkg_resources.working_set
-#     packages_list = sorted([f"{pkg.key}=={pkg.version}" for pkg in installed_packages])
-
-#     # Write to requirements.txt
-#     with open(filename, "w") as f:
-#         for package in packages_list:
-#             f.write(package + "\n")
-    
-#     print(f"Requirements saved to {filename}")
-
-# if __name__ == "__main__":
-#     generate_requirements()
-
-
-# Load YOLO model (COCO-pretrained)
-model = YOLO('yolov8s.pt')  # or 'yolov10s.pt'
-
-st.title("YOLO Object Detection with Streamlit")
-st.write("Upload an image and see object detection results in real-time!")
-
-# Upload image
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-
-if uploaded_file is not None:
-    # Convert uploaded file to PIL image
-    image = Image.open(uploaded_file).convert("RGB")
-    st.image(image, caption="Uploaded Image", width='stretch')
-
-    # Temporary save to disk for YOLO inference
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp_file:
-        temp_path = tmp_file.name
-        image.save(temp_path)
-
-    # Run YOLO inference
-    results = model(temp_path)
-
-    # Show detection results in Streamlit
-    st.image(results[0].plot(), caption="Detected Objects", width='stretch')
-
-    # # Save results to output folder
-    # output_dir = "outputs"
-    # os.makedirs(output_dir, exist_ok=True)
-    # results.save(output_dir)
-    # st.write(f"Detection results saved in `{output_dir}/`")
-    # Clean up temporary file
-    os.remove(temp_path)   
+import streamlit as st
+from ultralytics import YOLO
+from PIL import Image
+import tempfile
+import os
+
+model = YOLO('yolov8s.pt')  # or 'yolov10s.pt'
+
+st.title("YOLO Object Detection with Streamlit")
+st.write("Upload an image and see object detection results in real-time!")
+
+# Upload image
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Convert uploaded file to PIL image
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploaded Image", width='stretch')
+
+    # Temporary save to disk for YOLO inference
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp_file:
+        temp_path = tmp_file.name
+        image.save(temp_path)
+
+    # Run YOLO inference
+    results = model(temp_path)
+
+    # Show detection results in Streamlit
+    st.image(results[0].plot(), caption="Detected Objects", width='stretch')