Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,20 +1,88 @@
 import streamlit as st
-from PIL import Image
 import numpy as np
 import cv2
+import pickle
+from pathlib import Path
 
-st.title("Upload & View Image")
+# ------------------------------
+# Paths
+# ------------------------------
+SRC_PATH = Path(__file__).parent  # points to src/
 
-uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+# ------------------------------
+# Load models
+# ------------------------------
+with open(SRC_PATH / "pca_model.pkl", "rb") as f:
+    pca = pickle.load(f)
 
-# This prints info to Streamlit console
-st.write("Uploaded file:", uploaded_file)
+with open(SRC_PATH / "svc_model.pkl", "rb") as f:
+    svc = pickle.load(f)
+
+with open(SRC_PATH / "scaler.pkl", "rb") as f:
+    scaler = pickle.load(f)
+
+# ------------------------------
+# Label mapping
+# ------------------------------
+label_map = {
+    0: "fresh apple",
+    1: "fresh banana",
+    2: "fresh orange",
+    3: "rotten apple",
+    4: "rotten banana",
+    5: "rotten orange"
+}
+
+# ------------------------------
+# Streamlit UI
+# ------------------------------
+st.title("🍎 Fruit Image Classification")
+
+# ---- Example images ----
+st.subheader("Example Images")
+example_images = [
+    "Fresh Apple.jpg",
+    "Fresh Banana.jpg",
+    "Fresh Orange.jpg",
+    "Rotten Apple.jpg",
+    "Rotten Banana.jpg",
+    "Rotten Orange.jpg"
+]
+
+cols = st.columns(3)
+for i, img_file in enumerate(example_images):
+    img_path = SRC_PATH / img_file
+    if img_path.exists():
+        img = cv2.imread(str(img_path))
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        cols[i % 3].image(img, caption=img_file.split(".")[0], width=150)
+    else:
+        st.warning(f"Image '{img_file}' not found.")
+
+st.markdown("**Classes:** fresh apple, fresh banana, fresh orange, rotten apple, rotten banana, rotten orange")
+
+# ------------------------------
+# File uploader
+# ------------------------------
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
 if uploaded_file is not None:
-    # Make sure to open the uploaded file
-    image = Image.open(uploaded_file)
-    st.image(image, caption="Original Image", use_column_width=True)
+    # Read uploaded file as OpenCV image
+    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+    img = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
+
+    # Convert BGR → RGB for display
+    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    st.image(img_rgb, caption="Uploaded Image", use_container_width=True)
+
+    # ------------------------------
+    # Preprocess & predict
+    # ------------------------------
+    img_resized = cv2.resize(img, (64, 64))
+    img_flat = img_resized.flatten().reshape(1, -1)
+    img_scaled = scaler.transform(img_flat)
+    img_pca = pca.transform(img_scaled)
+    pred = svc.predict(img_pca)[0]
+    label = label_map.get(pred, "Unknown")
 
-    st.success("Image loaded successfully!")
-else:
-    st.info("Please upload an image.")
+    st.success(f"Predicted Class: {label}")