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import streamlit as st | |
import pandas as pd | |
import requests | |
from dotenv import load_dotenv | |
from transformers import pipeline | |
from PIL import Image | |
from info import pneumonia, covid19, vit_base_patch_16 | |
load_dotenv() | |
URL = 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTcY7VeTAy72aEPJbHmABvnGzW5gzrvSKRzOg&usqp=CAU' | |
def download_image(): | |
if st.session_state.img_url: | |
st.session_state['image'] = Image.open( | |
requests.get(st.session_state.img_url, stream=True).raw) | |
else: | |
del st.session_state['image'] | |
def file_upload(): | |
if st.session_state.file_upload: | |
st.session_state['image'] = Image.open(st.session_state.file_upload) | |
else: | |
del st.session_state['image'] | |
def cam_upload(): | |
if st.session_state.camera: | |
st.session_state['image'] = st.session_state.camera | |
else: | |
del st.session_state['image'] | |
if 'image' not in st.session_state: | |
st.session_state['image'] = Image.open(requests.get(URL, stream=True).raw) | |
st.header("Pneumonia and Covid19 Detector") | |
with st.sidebar: | |
img_upload, cam_upload, url_upload = st.tabs( | |
['π Upload', 'πΈ CAMERA', 'π URL']) | |
with img_upload: | |
uploaded_img = st.file_uploader( | |
label="Upload an X-ray image", on_change=file_upload, key='file_upload' | |
) | |
with cam_upload: | |
camera_img = st.camera_input( | |
label='Take a picture of X-ray', on_change=cam_upload, key='camera' | |
) | |
with url_upload: | |
img_url = st.text_input( | |
label="Enter the X-ray URL", value=URL, on_change=download_image, key="img_url" | |
) | |
st.image(st.session_state.image) | |
analyze_btn = st.button(label='Analyze X-ray', type='primary', | |
use_container_width=True, key='analyze_btn') | |
if st.session_state.image and st.session_state.analyze_btn: | |
with st.spinner(): | |
pipe = pipeline("image-classification", | |
model="Ajay-user/vit-base-patch16-224-finetuned-pneumonia-detection") | |
response = pipe(st.session_state.image) | |
df = pd.DataFrame(response) | |
result = df.nlargest(n=1, columns='score') | |
result_body = f'Model predicts : {result["label"].item()} with {result["score"].item()*100 :0.2f}% confidence' | |
with st.expander(label=result_body, expanded=True): | |
st.subheader(body=f':red[{result["label"].item()}] Detected') | |
st.bar_chart(data=df, x='label', y='score') | |
with st.expander(label="X-ray image analyzed"): | |
st.image(st.session_state.image) | |
with st.expander(label="Model Details"): | |
st.markdown(body=vit_base_patch_16) | |
else: | |
tab_1, tab_2 = st.tabs(['Pneumonia', 'Coronavirus']) | |
with tab_1: | |
st.subheader('Pneumonia') | |
st.markdown(body=pneumonia) | |
with tab_2: | |
st.subheader('Coronavirus') | |
st.markdown(body=covid19) | |