Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+example1.jpg filter=lfs diff=lfs merge=lfs -text
+example2.jpg filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,36 @@
+import streamlit as st
+from PIL import Image
+import numpy as np
+from ultralytics import YOLO  # Make sure this import works in your Hugging Face environment
+
+# Load the model
+@st.cache(allow_output_mutation=True)
+def load_model():
+    model = YOLO("weights.pt")  # Adjust path if needed
+    return model
+
+model = load_model()
+
+st.title("Circuit Sketch Recognition")
+
+# File uploader allows user to add their own image
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+    st.write("")
+    st.write("Detecting...")
+
+    # Perform inference
+    results = model.predict(uploaded_file)
+    r = results[0]
+    im_bgr = r.plot(conf=False, pil=True, font_size=32, line_width=2)  # Returns a PIL image if pil=True
+    im_rgb = Image.fromarray(im_bgr[..., ::-1])  # Convert BGR to RGB
+
+    # Display the prediction
+    st.image(im_rgb, caption='Prediction', use_column_width=True)
+
+# Optionally, display pre-computed example images
+if st.checkbox('Show Example Results'):
+    st.image(['example1.jpg', 'example2.jpg'], width=300, caption=['Example 1', 'Example 2'])

requirements.txt

@@ -0,0 +1,4 @@
+ultralytics
+stremlit
+Pillow
+numpy

weights.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0791285b924f954f0370a13739ca87e2569a90bf935c0afdd69797f9dc2bbf0a
+size 52120385