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import time
from turtle import width
import torch
import numpy as np
import streamlit as st
def app():
st.write("## Ship Imagery Prediction")
st.write("### Model evaluation:")
eval_col1, eval_col2, eval_col3, eval_col4, eval_col5 = st.columns(spec=5)
eval_col1.metric("Precision", "89.52%")
eval_col2.metric("Recall", "83.54%")
eval_col3.metric("F1-Score", "86.43%")
eval_col4.metric("mAP 0.5", "85.39%")
eval_col5.metric("mAP 0.5:0.95", "62.63%")
uploaded_file = st.file_uploader("Choose a ship imagery")
if uploaded_file is not None:
st.image(uploaded_file, caption='Image to predict')
folder_path = st.text_input("Image path",
help="This field the image path field that the model will predict the object inside the image that we have uploaded",
placeholder="Copy the path of image to this field")
prediction = st.button("Predict")
if prediction:
ship_model = torch.hub.load('ultralytics/yolov5', 'custom', path="apps/model/main_model.pt", force_reload=True)
ship_model.conf = 0.6
ship_model.iou = 0.55
results = ship_model(f"{folder_path}")
with st.spinner("Loading..."):
time.sleep(3.5)
st.success("Done!")
st.image(np.squeeze(results.render()))
results.print()
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