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import streamlit as st
from ultralytics import YOLO
import tempfile
import pandas as pd
model = YOLO('best.pt')
st.title('Spare-it Segmentation Model')
# Performance table data
st.header("Best Model Performance")
df = pd.read_csv('performance_table.csv')
st.dataframe(df)
with st.expander("See Example Results"):
st.write("Here are some example images with detections:")
st.image('example1.jpg')
st.image('example2.jpg')
st.image('example3.jpg')
st.image('example4.jpg')
st.image('example5.jpg')
input_method = st.radio("Choose the input method:", ("Upload an Image", "Take a Picture"))
if input_method == "Upload an Image":
image_data = st.file_uploader("Upload an image", type=['jpg', 'jpeg', 'png'])
elif input_method == "Take a Picture":
image_data = st.camera_input("Take a picture")
if image_data is not None:
with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as tmp_file:
tmp_file.write(image_data.getvalue())
image_path = tmp_file.name
results = model(image_path)
category_names = results[0].names
predictions = {}
for cls_id, conf in zip(results[0].boxes.cls, results[0].boxes.conf):
cls_id = int(cls_id)
conf = float(conf)
class_name = category_names[cls_id]
if class_name in predictions:
predictions[class_name].append(conf)
else:
predictions[class_name] = [conf]
num_masks = len(results[0].masks.data)
st.write(f"Total {num_masks} objects found.")
for category, confidences in predictions.items():
st.write(f"{len(confidences)} {category}: {['{:.2f}'.format(c) for c in confidences]}")
for result in results:
plotted_img = result.plot()
st.image(plotted_img, caption='Segmented Image', use_container_width =True)
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