Create app.py

app.py

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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(
+        ['📂 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="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 :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)
+        
+# import gradio as gr
+# from transformers import pipeline
+
+# pipe = pipeline("image-classification", "sharren/vit-beta2-0.99")
+
+# def image_classifier(image):
+#     outputs = pipe(image)
+#     results = {}
+#     for result in outputs:
+#         results[result['label']] = result['score']
+#     return results
+
+# title = "Skin Cancer ViT Classifier"
+# description = """
+# This application serves to classify skin lesion images based on their skin cancer type. Trained using Vision Transformer (ViT), it has achieved a validation accuracy of 86%.
+# """
+
+# demo = gr.Interface(fn=image_classifier, inputs=gr.Image(type="pil"), outputs="label", title=title, description=description, 
+#                     examples = "./assets",
+#                    theme = "gstaff/sketch")
+# demo.launch(show_api=False)