Upload 4 files

best_new_v2.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:75b7fee7b255144dd980793d9083f8194396a43075963818159491607a09e885
+size 546150559

packages.txt

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+python-opencv-headless
+libgl1

web.py

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+import streamlit as st
+from PIL import Image
+from ultralytics import YOLO
+import time
+
+st.title('Aloe Vera Plant Disease Analyser')
+
+col1,col2=st.columns(2)
+upload_stat=0
+with col1:
+    
+    input_image=st.file_uploader('Upload The Image File Here',type=['jpg','png'])
+    
+    confirmation=st.button('Submit')
+    if confirmation==True:
+        st.image(input_image)
+        upload_stat=1
+        
+with col2:
+    st.subheader('Detection:')
+    if upload_stat==1:
+        image_new=Image.open(input_image)
+        model1 = YOLO('git_weight.pt')
+        model2=YOLO('best.pt')
+        model3=YOLO('best_new_v2.pt')
+        model4=YOLO('best_v4.pt')
+        
+        results1 = model1(image_new)
+        results2= model2(image_new)
+        results3= model3(image_new)
+        result4=model4(image_new)
+        st.subheader('Model 1')
+        for r in results1:
+            im_array = r.plot()  # plot a BGR numpy array of predictions
+            im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
+            st.image(im)  # show image
+        st.subheader('Model 2')
+        for r in results2:
+            im_array = r.plot()  # plot a BGR numpy array of predictions
+            im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
+            st.image(im)  # show image
+        st.subheader('Model 3')
+        for r in results3:
+            im_array = r.plot()  # plot a BGR numpy array of predictions
+            im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
+            st.image(im)  # show image
+        for r in result4:
+            im_array = r.plot()  # plot a BGR numpy array of predictions
+            im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
+            st.image(im)  # show image
+
+