Upload app.py

app.py

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+import streamlit as st
+import numpy as np
+import pickle
+import cv2
+
+# -------------------------------
+# Page Config
+# -------------------------------
+st.set_page_config(page_title="🚗 Car Brand Logo Classifier")
+
+st.title("🚗 Car Brand Logo Classifier")
+
+# -------------------------------
+# Load the full pipeline (Scaler + PCA + Classifier)
+# -------------------------------
+with open("car_logo_pipeline.pkl", "rb") as f:
+    pipeline = pickle.load(f)
+
+# -------------------------------
+# File Uploader
+# -------------------------------
+uploaded_file = st.file_uploader("Upload a car logo image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file:
+    # Read uploaded file directly into OpenCV format (BGR, uint8) just like training
+    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+    img_array = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
+
+    # Display image in Streamlit (convert to RGB for display only)
+    st.image(cv2.cvtColor(img_array, cv2.COLOR_BGR2RGB), caption="Uploaded Logo", use_column_width=True)
+
+    # Resize to match training preprocessing
+    resized_img = cv2.resize(img_array, (300, 300))
+
+    # Flatten for model input
+    flat_img = resized_img.flatten().reshape(1, -1)
+
+    # Predict button
+    if st.button("🔍 Predict Brand"):
+        prediction = pipeline.predict(flat_img)[0]
+        st.markdown(f"## ✅ Predicted Brand: `{prediction}`")