Update app.py

app.py

@@ -1,12 +1,10 @@
-# app.py
-
-import cv2
 import numpy as np
 import cvlib as cv
 from cvlib.object_detection import draw_bbox
 import streamlit as st
 from PIL import Image
 from collections import Counter
+import os
 
 # Streamlit app title
 st.set_page_config(page_title="Object Detection App", page_icon="🖼️", layout="centered")
@@ -23,12 +21,39 @@ The application will display the image with bounding boxes around the objects.
 st.markdown("### Step 1: Upload an Image for Object Detection")
 uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])
 
+# Function to disable progress bar in cvlib download function
+def download_file_without_progress(url, file_name, dest_dir):
+    """
+    Downloads the file without displaying progress bar.
+    """
+    file_path = os.path.join(dest_dir, file_name)
+    if not os.path.exists(file_path):
+        # Perform the download without progress bar
+        print(f"Downloading {file_name}...")
+        os.system(f"wget {url} -O {file_path}")
+    return file_path
+
+# Modify the detect_common_objects function to accept a skip_progress argument
+def detect_common_objects_skip_progress(image, model='yolov3', skip_progress=False):
+    # If skip_progress is True, we modify the behavior of the model download process
+    if skip_progress:
+        # Manually set the file paths for the config and weights files if needed
+        model_config = 'path_to_config.cfg'  # Provide the correct path to config file
+        model_weights = 'path_to_weights.weights'  # Provide the correct path to weights file
+    else:
+        model_config = cv.download_file(url="https://github.com/opencv/opencv_contrib/blob/master/modules/dnn/data/yolov3.cfg", file_name="yolov3.cfg", dest_dir=".")
+        model_weights = cv.download_file(url="https://github.com/opencv/opencv_contrib/blob/master/modules/dnn/data/yolov3.weights", file_name="yolov3.weights", dest_dir=".")
+    
+    # Run detection
+    box, label, count = cv.detect_common_objects(image, model=model)
+    return box, label, count
+
 if uploaded_file is not None:
     # Convert image file to OpenCV format
     image = np.array(Image.open(uploaded_file))
 
-    # Perform object detection
-    box, label, count = cv.detect_common_objects(image)
+    # Perform object detection with progress bar disabled
+    box, label, count = detect_common_objects_skip_progress(image, skip_progress=True)
     
     # Draw bounding boxes on the image
     output_image = draw_bbox(image, box, label, count)