Spaces:
Sleeping
Sleeping
File size: 2,924 Bytes
7e1d4ae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
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(
['📂 Upload', '📸 CAMERA', '🔗 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)
|