| 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 akiec, bcc, bkl, df, mel, nv, vasc, vit_base_patch_16 | |
| load_dotenv() | |
| URL = 'https://i.stack.imgur.com/gPR77.jpg' | |
| 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'] = Image.open(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("Skin Cancer Classifier") | |
| with st.sidebar: | |
| img_upload_tab, cam_upload_tab, url_upload_tab = st.tabs( | |
| ['📂 Upload', '📸 CAMERA', '🔗 URL']) | |
| with img_upload_tab: | |
| uploaded_img = st.file_uploader( | |
| label="Upload a Skin Lesion", on_change=file_upload, key='file_upload' | |
| ) | |
| with cam_upload_tab: | |
| camera_img = st.camera_input( | |
| label='Take a picture of a Skin Lesion', on_change=cam_upload, key='camera' | |
| ) | |
| with url_upload_tab: | |
| img_url = st.text_input( | |
| label="Enter the Skin Lesion URL", value=URL, on_change=download_image, key="img_url" | |
| ) | |
| if 'image' in st.session_state: | |
| st.image(st.session_state['image']) | |
| analyze_btn = st.button(label='Analyze Skin Lesion', type='primary', use_container_width=True, key='analyze_btn') | |
| if 'image' in st.session_state and st.session_state['analyze_btn']: | |
| with st.spinner("Analyzing..."): | |
| pipe = pipeline("image-classification", model="sharren/vit-beta2-0.99") | |
| 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:.2f}% confidence' | |
| with st.expander(label=result_body, expanded=True): | |
| st.subheader(f':red[{result["label"].item()}] Detected') | |
| st.bar_chart(data=df, x='label', y='score') | |
| with st.expander(label="Skin lesion 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, tab_3, tab_4, tab_5, tab_6, tab_7 = st.tabs([ | |
| 'Actinic Keratoses', 'Basal Cell Carcinoma', 'Benign Keratosis', 'Dermatofibroma', | |
| 'Melanoma', 'Melanocytic Nevi', 'Vascular Lesion' | |
| ]) | |
| with tab_1: | |
| st.subheader('Actinic Keratoses') | |
| st.markdown(body=akiec) | |
| with tab_2: | |
| st.subheader('Basal Cell Carcinoma') | |
| st.markdown(body=bcc) | |
| with tab_3: | |
| st.subheader('Benign Lesions of the Keratosis Type') | |
| st.markdown(body=bkl) | |
| with tab_4: | |
| st.subheader('Dermatofibroma') | |
| st.markdown(body=df) | |
| with tab_5: | |
| st.subheader('Melanoma') | |
| st.markdown(body=mel) | |
| with tab_6: | |
| st.subheader('Melanocytic Nevi') | |
| st.markdown(body=nv) | |
| with tab_7: | |
| st.subheader('Vascular Lesion') | |
| st.markdown(body=vasc) | |