Update app.py

app.py

@@ -5,7 +5,6 @@ import streamlit as st
 from PIL import Image
 from collections import Counter
 import os
-import requests
 
 # Streamlit app title
 st.set_page_config(page_title="Object Detection App", page_icon="🖼️", layout="centered")
@@ -22,48 +21,23 @@ 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 download a file without using the progress bar
-def download_file_no_progress(url, file_name, dest_dir):
-    file_path = os.path.join(dest_dir, file_name)
-    if not os.path.exists(file_path):
-        print(f"Downloading {file_name}...")
-        # Disable the progress bar by using requests
-        response = requests.get(url, stream=True)
-        with open(file_path, 'wb') as f:
-            for chunk in response.iter_content(chunk_size=1024):
-                if chunk:
-                    f.write(chunk)
-    return file_path
-
-# Function to perform object detection with manual model file paths
-def detect_common_objects_skip_progress(image, model='yolov3', skip_progress=False):
-    if skip_progress:
-        # Manually set paths for config and weights if progress bar should be disabled
-        model_config = 'path_to_config.cfg'  # Provide the path to the .cfg file
-        model_weights = 'path_to_weights.weights'  # Provide the path to the .weights file
-    else:
-        # Download the config and weights files if not already present
-        model_config = download_file_no_progress(
-            url="https://github.com/opencv/opencv_contrib/blob/master/modules/dnn/data/yolov3.cfg", 
-            file_name="yolov3.cfg", 
-            dest_dir="."
-        )
-        model_weights = download_file_no_progress(
-            url="https://github.com/opencv/opencv_contrib/blob/master/modules/dnn/data/yolov3.weights", 
-            file_name="yolov3.weights", 
-            dest_dir="."
-        )
+# Function to skip download and use local files for the model
+def detect_common_objects_skip_progress(image, model='yolov3'):
+    model_config = 'yolov3.cfg'  # Path to your downloaded .cfg file
+    model_weights = 'yolov3.weights'  # Path to your downloaded .weights file
     
-    # Run the object detection
-    box, label, count = cv.detect_common_objects(image, model=model)
+    # Perform object detection using the provided model files
+    box, label, count = cv.detect_common_objects(image, model=model, 
+                                                 model_config=model_config, 
+                                                 model_weights=model_weights)
     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 with progress bar disabled
-    box, label, count = detect_common_objects_skip_progress(image, skip_progress=True)
+    # Perform object detection with manually provided paths
+    box, label, count = detect_common_objects_skip_progress(image)
     
     # Draw bounding boxes on the image
     output_image = draw_bbox(image, box, label, count)