Update app.py

app.py

@@ -2,82 +2,67 @@ 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)
+        st.session_state['image'] = Image.open(
+            requests.get(st.session_state.img_url, stream=True).raw)
     else:
-        st.session_state['image'] = None
+        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:
-        st.session_state['image'] = None
+        del st.session_state['image']
+
 
 def cam_upload():
     if st.session_state.camera:
         st.session_state['image'] = Image.open(st.session_state.camera)
     else:
-        st.session_state['image'] = None
+        del st.session_state['image']
+
 
-# Initialize session state
 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'])
+    img_upload_tab, cam_upload_tab, url_upload_tab = st.tabs(
+        ['📂 Upload', '📸 CAMERA', '🔗 URL'])
 
     with img_upload_tab:
-        st.file_uploader(label="Upload a Skin Lesion", on_change=file_upload, key='file_upload')
+        uploaded_img = st.file_uploader(
+            label="Upload a Skin Lesion", on_change=file_upload, key='file_upload'
+        )
     with cam_upload_tab:
-        st.camera_input(label='Take a picture of Skin Lesion', on_change=cam_upload, key='camera')
+        camera_img = st.camera_input(
+            label='Take a picture of a Skin Lesion', on_change=cam_upload, key='camera'
+        )
     with url_upload_tab:
-        st.text_input(label="Enter the Skin Lesion URL", value=URL, on_change=download_image, key="img_url")
+        img_url = st.text_input(
+            label="Enter the Skin Lesion URL", value=URL, on_change=download_image, key="img_url"
+        )
 
-    if st.session_state.image:
+    if 'image' in st.session_state:
         st.image(st.session_state['image'])
 
-    st.button(label='Analyze Skin Lesion', type='primary', use_container_width=True, key='analyze_btn')
-
-# Display initial information from info.py
-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)
-
-# Analyze the image if the button is pressed
-if st.session_state.get('image') is not None and st.session_state.get('analyze_btn'):
+    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'])
@@ -95,3 +80,30 @@ if st.session_state.get('image') is not None and st.session_state.get('analyze_b
 
         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)