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Procfile

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+web: sh setup.sh && streamlit run apps.py

apps.py

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+import time
+from turtle import width
+import torch
+import folium
+import numpy as np
+import pandas as pd
+import streamlit as st
+from folium.plugins import MarkerCluster
+from streamlit_folium import folium_static 
+
+st.set_page_config(
+        page_title="Ship Detection using YOLOv5 Medium Model",
+        page_icon=":ship:",
+        layout="wide"
+    )
+
+st.write("# Welcome to Ship Detection Application! :satellite:")
+st.markdown(
+        """
+        This application is build based on YOLOv5 with extral large model. User just
+        upload an image, and press the 'Predict' button to make a prediction base on
+        a training model before.
+
+        ### For more information, please visit:
+
+        - Check out [my github](https://github.com/bills1912)
+        - Jump into YOLOv5 [documentation](https://docs.ultralytics.com/)
+
+    """
+    )
+
+st.write("## Ship Imagery Prediction")
+map_col1, map_col2, map_col3 = st.columns(3)
+
+ais = pd.read_csv("https://raw.githubusercontent.com/bills1912/marin-vessels-detection/main/data/MarineTraffic_VesselExport_2022-11-25.csv")
+ais_jakarta = ais[ais['Destination Port'] == 'JAKARTA']
+ais_list = ais_jakarta.values.tolist()
+f = folium.Figure(width=1000, height=500)
+jakarta_vessels = folium.Map(location=[-5.626954250925966, 106.70735731868719], zoom_start=8).add_to(f)
+ais_data = folium.FeatureGroup(name="marine_vessels")
+mCluster = MarkerCluster(name="Marine Vessels")
+for i in ais_list:
+  html = f"<h3>{i[1]}</h3> Vessel Type: {i[8]} </br> Destination Port: {i[2]} </br> Reported Destination: {i[4]} </br> Current Port: {i[6]}\
+          </br> Latitude: {i[9]} </br> Longitude: {i[10]}"
+  iframe = folium.IFrame(html)
+  popup = folium.Popup(iframe, min_width=250, max_width=300)
+  ais_data.add_child(mCluster.add_child(folium.Marker(location=[i[10], i[11]], popup=popup, icon=folium.Icon(color="black", icon="ship", prefix="fa"))))
+jakarta_vessels.add_child(ais_data)
+folium_static(jakarta_vessels, width=1370, height=700)
+
+
+st.write("### Model evaluation:")
+eval_col1, eval_col2, eval_col3, eval_col4 = st.columns(spec=4)
+eval_col1.metric("Precision", "89.52%")
+eval_col2.metric("Recall", "83.54%")
+eval_col3.metric("mAP 0.5", "85.39%")
+eval_col4.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')
+    # st.write(uploaded_file.)
+
+prediction = st.button("Predict")
+if prediction:
+    ship_model = torch.hub.load('ultralytics/yolov5', 'custom', path="supercomputer/best.pt", force_reload=True)
+    # results = ship_model(f"C:/Users/bilva/YOLOv5/ship_test/{uploaded_file.name}")
+    results = ship_model(f"C:/Google Earth Pro/images/{uploaded_file.name}")
+    with st.spinner("Loading..."):
+        time.sleep(3.5)
+        st.success("Done!")
+    st.image(np.squeeze(results.render()))
+    results.print()
+    # with st.echo():
+    #     st.text(f"results.print()")
+    # st.markdown(results.print())
+    # for percent_progress in range (100):
+    #     time.sleep(0.1)
+    #     progress.progress(percent_progress + 1)

requirements.txt

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+numpy>=1.17.3
+pandas==1.3.5
+streamlit==1.13.0
+tensorflow==2.8.0
+pandas-datareader==0.10.0
+sklearn==0.0
+keras==2.8.0
+typing-extensions>=3.10.0
+streamlit-aggrid==0.3.2
+tenacity==8.0.1
+plotly==5.9.0

runtime.txt

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+python-3.9.14