Add new package

app.py

@@ -59,13 +59,13 @@ 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.)
+    folder_path = st.text_input("Folder path of model")
 
 prediction = st.button("Predict")
 if prediction:
-    ship_model = torch.hub.load('ultralytics/yolov5', 'custom', path="supercomputer/best.pt", force_reload=True)
+    ship_model = torch.hub.load('ultralytics/yolov5', 'custom', path="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}")
+    results = ship_model(f"{folder_path}/{uploaded_file.name}")
     with st.spinner("Loading..."):
         time.sleep(3.5)
         st.success("Done!")

apps.py

@@ -1,79 +0,0 @@
-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')
-    folder_path = st.text_input('Folder path of image')
-
-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"{folder_path}/{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)