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app.py

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+import streamlit as st
+from PIL import Image
+import os
+import shutil
+from ultralytics import YOLO
+
+# 1. Welcome message and brief description
+st.title("Car Brand Detector")
+st.subheader("Upload the image of a car (or multiple cars) and we will detect the brand!")
+
+# 2. Initialize YOLO with our custom trained model name
+model = YOLO("best.pt")
+
+# 3. Add a file uploader widget to the sidebar
+uploaded_file = st.sidebar.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+uploaded_image_path = ''
+
+# Check if a file was uploaded
+if uploaded_file is not None:
+
+    col1, col2 = st.columns((1, 2))
+
+    with col1:
+        # Display the uploaded image
+        image = Image.open(uploaded_file)
+
+        # Calculate the new width based on the aspect ratio
+        aspect_ratio = image.width / image.height
+        new_width = int(400 * aspect_ratio)
+        
+        # Resize the image while maintaining aspect ratio
+        image_resized = image.resize((new_width, 400))
+
+        st.image(image_resized, caption='Uploaded Image')
+
+        # Save the uploaded file to a directory named 'uploaded_files'
+        save_dir = 'uploaded_files'
+        os.makedirs(save_dir, exist_ok=True)  # Create the directory if it doesn't exist
+        file_path = os.path.join(save_dir, uploaded_file.name)
+        with open(file_path, 'wb') as f:
+            f.write(uploaded_file.getvalue())
+
+        uploaded_image_path = file_path
+
+    with col2:
+        # Create a button for brand detection
+        if st.button('Detect Brand'):
+            # Perform brand detection when the button is clicked
+            # Use the pre-trained YOLO model to detect the car brand from the uploaded image
+            results = model.predict(source=uploaded_image_path, save=True, save_txt=True)
+            
+            # Display the uploaded image
+            image = Image.open('runs/detect/predict/'+uploaded_file.name)
+
+            # Calculate the new width based on the aspect ratio
+            aspect_ratio = image.width / image.height
+            new_width = int(400 * aspect_ratio)
+
+            # Resize the image while maintaining aspect ratio
+            image_resized = image.resize((new_width, 400))
+
+            classes = []
+            for detection in results:
+                # Assuming detection.boxes.cls and detection.boxes.conf are tensors with multiple elements
+                for class_id_tensor, confidence_tensor in zip(detection.boxes.cls, detection.boxes.conf):
+                    class_id = int(class_id_tensor.item())
+                    confidence = round(float(confidence_tensor.item()), 2)
+                    class_name = results[0].names[class_id].capitalize()
+                    classes.append((class_name, confidence))
+                
+            caption = 'Brand(s) detected: '+str(classes)     
+            st.image(image_resized, caption=caption)
+
+            for c in classes:
+                st.write("Brand:", c[0],"--- Confidence:", c[1])
+    
+            # Delete the 'uploaded_files' directory after brand detection
+            if os.path.exists('uploaded_files'):
+                shutil.rmtree('uploaded_files')
+
+            # Delete the 'runs' directory after brand detection
+            if os.path.exists('runs'):
+                shutil.rmtree('runs')

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d2eef332bcfda1146dfeba4860ca25fd060601a7d4613f9eafd6590390d7d917
+size 22553251

requirements.txt

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+Pillow
+streamlit
+opencv-python
+ultralytics