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streamlit_app.py

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+import streamlit as st
+from PIL import Image
+import os
+import shutil
+
+# ------------------------------
+# Streamlit Config (must be first)
+# ------------------------------
+st.set_page_config(page_title="Image Categorization Demo", layout="wide")
+
+# ------------------------------
+# Load YOLO classification model
+# ------------------------------
+@st.cache_resource
+def load_model():
+    from ultralytics import YOLO
+    model = YOLO("yolov8m-cls.pt")  # replace with your trained model
+    return model
+
+model = load_model()
+
+# ------------------------------
+# Helper: manage temp folder
+# ------------------------------
+TEMP_FOLDER = "temfolder"
+
+def prepare_temp_folder():
+    if os.path.exists(TEMP_FOLDER):
+        shutil.rmtree(TEMP_FOLDER)
+    os.makedirs(TEMP_FOLDER)
+
+def cleanup_temp_folder():
+    if os.path.exists(TEMP_FOLDER):
+        shutil.rmtree(TEMP_FOLDER)
+
+# ------------------------------
+# Streamlit UI
+# ------------------------------
+st.title("Image Categorization Demo")
+
+with st.form("upload_form", clear_on_submit=True):
+    uploaded_files = st.file_uploader(
+        "Upload one or more images", 
+        type=["jpg", "jpeg", "png"], 
+        accept_multiple_files=True
+    )
+
+    col1, col2 = st.columns([1, 1])
+    submit = col1.form_submit_button("🚀 Submit for Classification")
+    refresh = col2.form_submit_button("🔄 Refresh")
+
+if refresh:
+    cleanup_temp_folder()
+    st.success("🧹 Uploads cleared!")
+
+if submit:
+    if not uploaded_files:
+        st.warning("⚠️ Please upload at least one image before submitting.")
+    else:
+        total_files = len(uploaded_files)
+        st.write(f"🔍 Classifying **{total_files}** images...")
+
+        # Prepare clean folder
+        prepare_temp_folder()
+
+        results_by_class = {}
+
+        progress = st.progress(0)          # progress bar
+        status_text = st.empty()           # placeholder for progress text
+
+        for idx, file in enumerate(uploaded_files, start=1):
+            # Save uploaded file into temfolder
+            img_path = os.path.join(TEMP_FOLDER, file.name)
+            with open(img_path, "wb") as f:
+                f.write(file.read())
+
+            # Run YOLO classification
+            results = model(img_path)
+            pred_class = results[0].names[results[0].probs.top1]
+
+            # Group images by predicted class
+            if pred_class not in results_by_class:
+                results_by_class[pred_class] = []
+            results_by_class[pred_class].append(img_path)
+
+            # Update progress bar + text
+            percent = int((idx / total_files) * 100)
+            progress.progress(idx / total_files)
+            status_text.text(f"Processing {idx}/{total_files} images ({percent}%)")
+
+        st.success("✅ Classification complete!")
+
+        # ------------------------------
+        # Show gallery grouped by class
+        # ------------------------------
+        for cls, img_list in results_by_class.items():
+            st.subheader(f"📂 Category: **{cls}** ({len(img_list)})")
+            cols = st.columns(4)  # show 4 images per row
+            for i, img_path in enumerate(img_list):
+                with cols[i % 4]:
+                    st.image(Image.open(img_path), use_column_width=True)
+
+        # Cleanup after displaying
+        cleanup_temp_folder()

yolov8m-cls.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:864010e833b46be542ed849b2eb0fdacf51e4679dc677c18dcc4505244d08ec1
+size 34285204