| import streamlit as st |
| import numpy as np |
| import cv2 |
| import pickle |
| from pathlib import Path |
|
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| |
| SRC_PATH = Path(__file__).parent |
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| |
| with open(SRC_PATH / "pca_model.pkl", "rb") as f: |
| pca = pickle.load(f) |
|
|
| with open(SRC_PATH / "svc_model.pkl", "rb") as f: |
| svc = pickle.load(f) |
|
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| with open(SRC_PATH / "scaler.pkl", "rb") as f: |
| scaler = pickle.load(f) |
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| |
| label_map = { |
| 0: "fresh apple", |
| 1: "fresh banana", |
| 2: "fresh orange", |
| 3: "rotten apple", |
| 4: "rotten banana", |
| 5: "rotten orange" |
| } |
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| |
| st.title("🍎 Fruit Image Classification") |
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| |
| 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.") |
|
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| st.markdown("**Classes:** fresh apple, fresh banana, fresh orange, rotten apple, rotten banana, rotten orange") |
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| uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) |
|
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| if uploaded_file is not None: |
| |
| file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8) |
| img = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR) |
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| img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) |
| st.image(img_rgb, caption="Uploaded Image", use_container_width=True) |
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| 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") |
|
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| st.success(f"Predicted Class: {label}") |
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|
