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app.py
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import streamlit as st
from fastai.vision.all import *
from plotly import express as px
import requests
st.set_page_config(
page_title="Mevalar",
page_icon="🍏",
layout="centered",
initial_sidebar_state="expanded",
)
# Sidebar
with st.sidebar:
st.title("Baholang")
with st.form('score', clear_on_submit=True):
stars = st.select_slider('Nechta ⭐️ bilan siylaysiz:', ['', '⭐️', '⭐️⭐️', '⭐️⭐️⭐️', '⭐️⭐️⭐️⭐️', '⭐️⭐️⭐️⭐️⭐️'], )
feedback = st.text_area('Fikr yoki taklifingiz:')
submitted = st.form_submit_button('Yuborish')
if submitted:
message = f'Stars: {stars}\n\nMessage: {feedback}'
try:
res = requests.get(
f"https://api.telegram.org/bot{st.secrets.get('BOT_TOKEN')}/sendMessage?chat_id={st.secrets.get('ADMIN_ID')}&text={message}"
)
except:
st.error("Yuborolmadim, nimadir xato ketdi😔")
finally:
if res.status_code == 200:
st.success('Yuborildi, raxmat😎')
else:
st.error("Yuborolmadim, nimadir xato bo'ldi shekili😔")
st.write('---')
st.title("Resurslar")
st.header('Modellar')
st.write('''
- <a href="https://drive.google.com/file/d/1hMqUjQGT_aJan4XL1Ari9z-mvYOx3DlS/view?usp=share_link" style="color:green;" target="_blank">fruits_filter.pkl</a>
- <a href="https://drive.google.com/file/d/1_wfwQNWlERAXWKur-nnWpZi13KLu5nRB/view?usp=share_link" style="color:green;" target="_blank">fruits.pkl</a>
''', unsafe_allow_html=True)
st.header('Train qilingan colablar:')
st.write('''
- <a href="https://colab.research.google.com/drive/12hZ9ZhEMYVovVDYwuDyoyAc1jwqYyZE7?usp=sharing" style="color:green;" target="_blank">fruits</a>
- <a href="https://colab.research.google.com/drive/1jeTKtDIbsKRsQxLglRSDyAgXF5LLkpJq?usp=sharing" style="color:green;" target="_blank">fruits_filter</a>
''', unsafe_allow_html=True)
st.header('Foydalanilgan datasetlar:')
st.write('''
- <a href="https://www.kaggle.com/datasets/itsahmad/indoor-scenes-cvpr-2019" style="color:green;" target="_blank">MIT Indoor Scenes</a>
- <a href="https://storage.googleapis.com/openimages/web/factsfigures_v4.html" style="color:green;" target="_blank">Open Images Dataset V4</a>
''', unsafe_allow_html=True)
st.header('Github:')
st.write(''' <a href="https://github.com/RDonii/CNN_meva" style="color:green;" target="_blank">CNN_Mevalar</a>''', unsafe_allow_html=True)
# st.write("""
# ---
# *Streamlitni tanishtirganingiz uchun alohida raxmat aytmoqchiman. Yetarlicha imkoniyatli, juda qulay va sodda ekan.* 😊
# """)
# loading
lbs = {
'Apple': 'Olma',
'Pear': 'Nok',
'Banana': 'Banan',
'Lemon': 'Limon',
'Tomato': 'Pomidor',
'Grape': 'Uzum'
}
main_ml = load_learner('fruits.pkl')
filter_ml = load_learner('fruits_filter.pkl')
# Main page
st.title('Mevalar')
with st.expander("Qo'llanma"):
st.write('''
Loyihada ikkita DL modeli train va deploy qilindi.
Yuklangan rasim foydalanuvchi ixtiyoriga qarab filter qilinadi va quyidagi obyektlardan birini rasimdan topishga xarakat qiladi:
- Olma
- Nok
- Banan
- Limon
- Pomidor
- Uzum
''')
st.info('*Resurslarni chap yuqoridagi tugmani bosish orqali saydbardan topishingiz mumkin*.')
st.warning("Filter modelni train qilishda manfiy klass uchun ishlatilingan dataset uy ichkarisida olingan rasimlar bolgani uchun **xozicha** ko'cha rasimlari bilan ishlashda xatoliklar kuzatilinishi mumkin.", icon='⚠️')
st.write('---')
uploaded = st.file_uploader('Iltimos rasim yuklang:', type=['jpg', 'jpeg', 'png'])
with st.expander("Sozlamalar"):
filter_available = st.checkbox("FILTER", value=True, help="Mevani tanishdan avval, rasimda meva mavjud yoki yo'qligini aniqlash filteri.")
facc = st.slider('Filter uchun minimal ehtimollik', 1, 99, value=85)
macc = st.slider('Meva turi uchun minimal ehtimollik', 1, 99, value=85)
st.write('---')
if uploaded:
with st.spinner("Loading..."):
img = PILImage.create(uploaded)
# filter on
if filter_available:
fpred, fprob_id, fprobs = filter_ml.predict(img)
res, main_res, filt_res = st.tabs(["Natija", "Ko'rsatgichlar", "Filter ko'rsatgichlari"])
with filt_res:
st.header("`fruits_filter` modeli natijalari")
st.write(f'Eng yuqori ehtimollik {fprobs[fprob_id].item()*100:.2f}% {"musbat" if int(fpred)==1 else "manfiy"}')
ffig = px.bar(y=fprobs*100, x=filter_ml.dls.vocab, orientation='v',
labels={
"x": "Natijalar",
"y": "Ehtimollik %",
}
)
st.plotly_chart(ffig)
# filter posive
if int(fpred)==1 and fprobs[fprob_id].item()*100>facc:
mpred, mprob_id, mprobs = main_ml.predict(img)
with res:
if mprobs[mprob_id].item()*100>macc:
st.header(lbs[mpred])
st.image(img, width=600)
else:
st.error("Meva turi uchun ehtimollik so'ralganidan past chiqdi. Yetarlicha ishonchimiz komil emas.", icon='❌')
with main_res:
st.header("`fruits` modeli natijalari")
st.write(f'Eng yuqori ehtimollik {mprobs[mprob_id].item()*100:.2f}% {lbs[mpred]}')
mfig = px.bar(y=mprobs*100, x=main_ml.dls.vocab, orientation='v',
labels={
"x": "Mevalar",
"y": "Ehtimollik %",
}
)
st.plotly_chart(mfig)
else:
with res:
st.error("Rasmda meva aniqlanmadi", icon='❌')
st.image(img, width=600)
with main_res:
st.error("Rasmda meva aniqlanmadi. Filter natijalarini ko'ring", icon='❌')
else:
res, main_res = st.tabs(["Natija", "Ko'rsatgichlar"])
mpred, mprob_id, mprobs = main_ml.predict(img)
with res:
if mprobs[mprob_id].item()*100>macc:
st.header(lbs[mpred])
st.image(img, width=600)
else:
st.error("Meva turi uchun ehtimollik so'ralganidan past chiqdi. Yetarlicha ishonchimiz komil emas.", icon='❌')
with main_res:
st.header("`fruits modeli` natijalari")
st.write(f'Eng yuqori ehtimollik {mprobs[mprob_id].item()*100:.2f}% {lbs[mpred]}')
mfig = px.bar(y=mprobs*100, x=main_ml.dls.vocab, orientation='v',
labels={
"x": "Mevalar",
"y": "Ehtimollik %",
}
)
st.plotly_chart(mfig)