Update app.py

app.py

@@ -9,6 +9,8 @@ from datetime import datetime, timedelta
 import re
 from ultralytics import YOLO
 import pandas as pd
+import cv2
+from PIL import Image
 
 # Initialize models
 ocr = PaddleOCR(lang='en')
@@ -239,12 +241,25 @@ if uploaded_file is not None:
         results = object_model(image_path)
         detected_objects = []
         
+        # Load the image using OpenCV
+        image = cv2.imread(image_path)
+        
         for result in results:
             boxes = result.boxes.data.cpu().numpy()
             for box in boxes:
                 class_id = int(box[5])
                 confidence = box[4]  # Assuming the confidence score is at index 4
                 detected_objects.append((result.names[class_id], confidence))
+                
+                # Draw bounding box and label on the image
+                x1, y1, x2, y2 = map(int, box[:4])
+                label = f"{result.names[class_id]} {confidence * 100:.2f}%"
+                cv2.rectangle(image, (x1, y1), (x2, y2), (255, 0, 0), 2)
+                cv2.putText(image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
+        
+        # Convert the image back to RGB for display in Streamlit
+        image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        st.image(image_rgb, caption='Detected Objects', use_container_width=True)
         
         # Count occurrences and average confidence of each object
         object_data = {}