Adding app file, requirements text, and model weights

app.py

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+# Import required libraries
+import PIL
+import cv2
+import streamlit as st
+from ultralytics import YOLO
+import tempfile
+
+
+# Replace the relative path to your weight file
+model_path = '/content/drive/MyDrive/wildfire_augmented_final/experiment_113/weights/best.pt'
+
+# Setting page layout
+st.set_page_config(
+    page_title="Object Detection using YOLOv8",  # Setting page title
+    page_icon="🤖",     # Setting page icon
+    layout="wide",      # Setting layout to wide
+    initial_sidebar_state="expanded"    # Expanding sidebar by default
+)
+
+# Creating sidebar
+with st.sidebar:
+    st.header("Image/Video Config")     # Adding header to sidebar
+    # Adding file uploader to sidebar for selecting images and videos
+    source_file = st.file_uploader(
+        "Choose an image or video...", type=("jpg", "jpeg", "png", 'bmp', 'webp', 'mp4'))
+
+    # Model Options
+    confidence = float(st.slider(
+        "Select Model Confidence", 25, 100, 40)) / 100
+
+# Creating main page heading
+st.title("Object Detection using YOLOv8")
+
+# Creating two columns on the main page
+col1, col2 = st.columns(2)
+
+# Adding image to the first column if image is uploaded
+with col1:
+    if source_file:
+        # Check if the file is an image
+        if source_file.type.split('/')[0] == 'image':
+            # Opening the uploaded image
+            uploaded_image = PIL.Image.open(source_file)
+            # Adding the uploaded image to the page with a caption
+            st.image(source_file,
+                     caption="Uploaded Image",
+                     use_column_width=True
+                     )
+        else:
+            tfile = tempfile.NamedTemporaryFile(delete=False) 
+            tfile.write(source_file.read())
+            vidcap = cv2.VideoCapture(tfile.name)
+
+try:
+    model = YOLO(model_path)
+except Exception as ex:
+    st.error(
+        f"Unable to load model. Check the specified path: {model_path}")
+    st.error(ex)
+
+if st.sidebar.button('Detect Objects'):
+    if source_file.type.split('/')[0] == 'image':
+        res = model.predict(uploaded_image,
+                            conf=confidence
+                            )
+        boxes = res[0].boxes
+        res_plotted = res[0].plot()[:, :, ::-1]
+        with col2:
+            st.image(res_plotted,
+                     caption='Detected Image',
+                     use_column_width=True
+                     )
+            try:
+                with st.expander("Detection Results"):
+                    for box in boxes:
+                        st.write(box.xywh)
+            except Exception as ex:
+                st.write("No image is uploaded yet!")
+    else:
+        # Open the video file
+        success, image = vidcap.read()
+        while success:
+            res = model.predict(image,
+                                conf=confidence
+                                )
+            boxes = res[0].boxes
+            res_plotted = res[0].plot()[:, :, ::-1]
+            with col2:
+                st.image(res_plotted,
+                         caption='Detected Frame',
+                         use_column_width=True
+                         )
+                try:
+                    with st.expander("Detection Results"):
+                        for box in boxes:
+                            st.write(box.xywh)
+                except Exception as ex:
+                    st.write("No video is uploaded yet!")
+            success, image = vidcap.read()

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:05a5e990d1bcb3e4f2b6f38d48baffba4418baf65f3d426af0cecce43ecd4eab
+size 6236761

requirements.txt

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+streamlit==1.29.0
+torch==1.8.0
+Pillow==7.1.0
+opencv-python-headless==4.6.0
+ultralytics==8.0.221