Update app.py

app.py

@@ -144,9 +144,12 @@ model = attempt_load(weight_path, map_location=torch.device('cpu'))  # load FP32
 Rats are common pests in urban and rural environments, posing threats to public health and causing damage to property. Effective rat detection is crucial for pest control and management. However, manual rat detection can be time-consuming and labor-intensive. Therefore, we developed an object detection model using YOLOv7 specifically tailored for rat detection. This model aims to automate the process of rat detection, making it faster, more efficient, and accessible.
 
 Usage Instructions:
-Upload Image: Users can upload their own images containing rats for detection.
-Image URL: Users can input the URL of an image containing rats for detection.
-Use Random Default Image: Users can select a default image provided by the system for detection. The system will randomly choose one of the default images and perform detection on it.
+1.Upload Image: Users can upload their own images containing rats for detection.
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+2.Image URL: Users can input the URL of an image containing rats for detection.
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+3.Use Random Default Image: Users can select a default image provided by the system for detection. The system will randomly choose one of the default images and perform detection on it.
+
 Upon selecting an option, the model will perform rat detection on the chosen image and display the results, including bounding boxes around detected rats. Users can then analyze the results to identify rat presence in the image.
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 # Modify Streamlit app code