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import torch
import streamlit as st
from PIL import Image
import io
# Load the YOLOv5 model
model = torch.hub.load('ultralytics/yolov5', 'custom', path='fire.pt') # Load custom model
# Streamlit interface
st.title("YOLOv5 Image Detection")
st.write("Upload an image to detect objects using YOLOv5")
# File uploader for image
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Convert the uploaded file to a PIL Image
image = Image.open(uploaded_file)
# Run the YOLOv5 model
results = model(image)
# Save the results image
results_image = results.render()[0] # Render returns a list, we take the first element
# Convert the numpy array result to an image
results_image = Image.fromarray(results_image)
# Save to a buffer
buf = io.BytesIO()
results_image.save(buf, format='JPEG')
byte_im = buf.getvalue()
# Display the input and output images side by side
st.image(image, caption='Uploaded Image', use_column_width=True)
st.image(results_image, caption='Detected Image', use_column_width=True)
# Provide a download button for the output image
st.download_button(
label="Download Output Image",
data=byte_im,
file_name="output.jpg",
mime="image/jpeg"
)
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