Upload app.py

app.py

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+import streamlit as st
+from ultralytics import YOLO
+import tempfile
+import pandas as pd
+
+
+model = YOLO('best.pt')
+
+st.title('Spare-it Segmentation Model')
+
+input_method = st.radio("Choose the input method:", ("Upload an Image", "Take a Picture"))
+
+if input_method == "Upload an Image":
+    image_data = st.file_uploader("Upload an image", type=['jpg', 'jpeg', 'png'])
+
+elif input_method == "Take a Picture":
+    image_data = st.camera_input("Take a picture")
+
+if image_data is not None:
+    # Create a temporary file to store the input image
+    with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as tmp_file:
+        tmp_file.write(image_data.getvalue())
+        image_path = tmp_file.name
+
+    results = model(image_path)
+
+    category_names = results[0].names  
+
+    predictions = {}
+    for cls_id, conf in zip(results[0].boxes.cls, results[0].boxes.conf):
+        cls_id = int(cls_id)
+        conf = float(conf)
+        class_name = category_names[cls_id]
+        if class_name in predictions:
+            predictions[class_name].append(conf)
+        else:
+            predictions[class_name] = [conf]
+
+    num_masks = len(results[0].masks.masks) 
+
+    st.write(f"Total {num_masks} objects found.")
+    for category, confidences in predictions.items():
+        st.write(f"{len(confidences)} {category}: {['{:.2f}'.format(c) for c in confidences]}")
+
+    with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as output_tmp:
+        for result in results:
+            result.save(save_dir=output_tmp.name)
+        st.image(output_tmp.name, caption='Segmented Image', use_column_width=True)
+