Upload 2 files

sample.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5d1e0b40866bcddf08ae2adc974d050e970f1c89856cdd5ccef84b93823b643c
+size 3094968

weed_streamlit.py

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+import PIL
+
+import streamlit as st
+from transformers import AutoImageProcessor
+from transformers import AutoModelForImageClassification
+import torch
+
+# Replace the relative path to your weight file
+model_path = 'weights/yolov8n.pt'
+
+model_dict={"vit":"uisikdag/weed_vit_balanced",
+            "deit":"uisikdag/weed_deit_balanced",
+            "convnext":"uisikdag/weeds_convnext_balanced",
+            "resnet":"uisikdag/weed_resnet_balanced",
+            "beit":"uisikdag/weed_beit_balanced"}
+
+
+# Setting page layout
+st.set_page_config(
+    page_title="Weed Classification",  # Setting page title
+    page_icon="🤖",     # Setting page icon
+    layout="wide",      # Setting layout to wide
+    initial_sidebar_state="expanded"    # Expanding sidebar by default
+)
+
+# Creating sidebar
+with st.sidebar:
+    st.header("Settings")     # Adding header to sidebar
+
+    model_idx=st.selectbox("Select Base Classifier",{'vit','deit','convnext','resnet','beit'})
+    model=model_dict[model_idx]
+
+    # Adding file uploader to sidebar for selecting images
+    source_img = st.file_uploader(
+        "Choose an image...", type=("jpg", "jpeg", "png", 'bmp', 'webp'))
+    
+    with open('sample.zip', 'rb') as f:
+        st.download_button('Sample Images', f, file_name='images.zip')
+    
+# Creating main page heading
+st.title("Weed Classification with \N{hugging face} Transformers")
+
+# Creating two columns on the main page
+col1, col2 = st.columns(2)
+
+# Adding image to the first column if image is uploaded
+with col1:
+    if source_img:
+        # Opening the uploaded image
+        uploaded_image = PIL.Image.open(source_img)
+        # Adding the uploaded image to the page with a caption
+        st.image(source_img,
+                 caption="Uploaded Image",
+                 use_column_width=True
+                 )
+    else:
+        uploaded_image=None
+        st.write('Please upload an image')
+
+with col2:
+        if st.sidebar.button('Classify'):
+            if uploaded_image is not None:
+                image_processor = AutoImageProcessor.from_pretrained(model)
+                inputs = image_processor(uploaded_image, return_tensors="pt")
+                model = AutoModelForImageClassification.from_pretrained(model)
+                with torch.no_grad():
+                    logits = model(**inputs).logits
+                predicted_label = logits.argmax(-1).item()
+                out=model.config.id2label[predicted_label]
+                out='The predicted class for the image is: '+out
+                st.text(out)
+