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# Import streamlit
import streamlit as st
from streamlit_option_menu import option_menu
from PIL import Image
import pandas as pd
from fastai.vision.all import *
from fastai.learner import load_learner
#######################################################################################################################
def upload_photo(model=None, vocab=None, key=None):
options = st.multiselect("**Find & select multiple names, then perform Web Search, Download, Upload & Detect...**",
vocab,
vocab[100:108],
key="birds_detector 1")
st.text(f"Your selection: {options}")
uploaded_image = st.file_uploader(
"**Upload an image**", type=["jpg", "png", "jpeg"], key=key)
st.divider()
if uploaded_image:
image = Image.open(uploaded_image)
st.image(image, use_column_width=True)
if st.button("**Detect**", type="primary"):
output = model.predict(image)
st.markdown(f"""
<div style="text-align:center;">
<h1>{output[0]}</h1>
</div>""",
unsafe_allow_html=True)
st.image(
image, caption=f'{output[0]} {max(output[2]).item() * 100:.2f}%', use_column_width=True)
#######################################################################################################################
def capture_photo(model=None, vocab=None, key=None):
capture_toggle = st.toggle(
label="**`Enable Camera`**", key="birds_capture_photo")
if capture_toggle:
# Check if the cancel checkbox is not selected
img_file_buffer = st.camera_input(
label="Take a picture (`try to keep the subject at the center`)", key=key)
if img_file_buffer:
# To read image file buffer as a PIL Image:
image = Image.open(img_file_buffer)
st.image(image, use_column_width=True)
if st.button(label="Detect", key="pets_capture_detect"):
output = model.predict(image)
st.markdown(f"""<div style="text-align:center;">
<h1>{output[0]}</h1>
</div>""",
unsafe_allow_html=True)
st.image(
image, caption=f'{output[0]} {max(output[2]).item() * 100:.2f}%', use_column_width=True)
#######################################################################################################################
def model_info():
# Model performance on Freezed Layers
st.subheader("Model performance with Resnet50 (freezed layers)")
freezed_data = {
'epoch': [0, 1, 2, 3, 4],
'train_loss': [1.280186, 0.786523, 0.491951, 0.343532, 0.297919],
'valid_loss': [0.447442, 0.183758, 0.107396, 0.067139, 0.058960],
'accuracy': [0.870476, 0.948190, 0.969524, 0.984381, 0.985905],
'error_rate': [0.129524, 0.051810, 0.030476, 0.015619, 0.014095],
'time': ['13:50', '11:06', '11:17', '10:44', '11:12']
}
df = pd.DataFrame(freezed_data)
# Streamlit table
st.table(df)
# Visualize data as line plot
st.line_chart(data=df, x="epoch", y=['train_loss', 'valid_loss', 'accuracy', 'error_rate'], height=600)
# Model performance on Unfreezed Layers
st.subheader("Model performance with Resnet50 (unfreezed layers)")
# Create a DataFrame with the provided data
unfreezed_data = {
'epoch': [0, 1, 2, 3, 4],
'train_loss': [0.989004, 0.699652, 0.447756, 0.258508, 0.160852],
'valid_loss': [0.391414, 0.176702, 0.089814, 0.037954, 0.029704],
'accuracy': [0.896381, 0.949714, 0.975238, 0.991238, 0.992762],
'error_rate': [0.103619, 0.050286, 0.024762, 0.008762, 0.007238],
'time': ['17:05', '12:41', '12:12', '14:43', '12:30']
}
df = pd.DataFrame(unfreezed_data)
# Streamlit table
st.table(df)
# Visualize data as line plot
st.line_chart(data=df, x="epoch", y=['train_loss', 'valid_loss', 'accuracy', 'error_rate'], height=600)
####################################################################################################################### |