Create app.py

app.py

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+import streamlit as st
+from ultralytics import YOLO
+import pandas as pd
+import numpy as np
+from io import StringIO
+import PIL
+from PIL import Image
+import requests
+from io import BytesIO
+
+
+st.title("cancer-dect")
+st.text("Upload image here:")
+uploaded_file = st.file_uploader("Choose a file", type=['png', 'jpg'])
+if uploaded_file is not None:
+    
+
+    st.write("filename:", uploaded_file.name)
+    uploaded_image = PIL.Image.open(uploaded_file)
+    st.image(uploaded_image, caption='Input', width=200)
+
+    # st.divider()
+
+    type_=st.selectbox("Choose any one type of detection", (('Brain'), ('Skin')))
+    conf = st.slider('Set confidence level percentage', 0, 100, 25)
+
+    if type_=='Brain':
+        model= YOLO("cd_detect.pt")
+        confi=conf/100
+        res=model(uploaded_image, conf=confi)
+        img=res[0].plot()
+        a=res[0]
+        if a.masks is not None:
+            st.image(img, caption='Output', width=600)
+        else:
+            st.write("No detections found")
+
+    if type_=='Skin':
+        # Load a model
+        # model = YOLO('yolov8n.pt')  # load an official model
+        model= YOLO("skin_detect.pt")
+        confi=conf/100
+        res=model(uploaded_image, conf=confi)
+        img=res[0].plot()
+        a=res[0]
+        if a.masks is not None:
+            st.image(img, caption='Output', width=600)
+        else:
+            st.write("No detections found")