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f0305ab 61c7463 f0305ab 61c7463 f0305ab 61c7463 f0305ab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | import streamlit as st
import numpy as np
from PIL import Image
import tensorflow as tf
st.set_page_config(page_title="Image Classification", layout="wide")
st.title("Image Classification")
# ---------------------------------------------------
# CLASS LABELS (MUST MATCH TRAINING ORDER EXACTLY)
# ---------------------------------------------------
CLASS_NAMES = [
"person", # 0
"bicycle", # 1
"car", # 2
"motorcycle", # 3
"airplane", # 4
"bus", # 5
"train", # 6
"truck", # 7
"traffic light", # 8
"stop sign", # 9
"bench", # 10
"bird", # 11
"cat", # 12
"dog", # 13
"horse", # 14
"cow", # 15
"elephant", # 16
"bottle", # 17
"cup", # 18
"bowl", # 19
"pizza", # 20
"cake", # 21
"chair", # 22
"couch", # 23
"potted plant", # 24
"bed" # 25
]
# ---------------------------------------------------
# MODEL LOADING (CACHED)
# ---------------------------------------------------
@st.cache_resource
def load_model(path):
return tf.keras.models.load_model(path)
MODEL_PATHS = {
"VGG16": "models/vgg16_smartvision.h5",
"ResNet50": "models/resnet50_smartvision.h5",
"MobileNetV2": "models/mobilenetv2_smartvision.h5",
"EfficientNetB0": "models/efficientnetb0_smartvision.h5"
}
# ---------------------------------------------------
# FILE UPLOAD
# ---------------------------------------------------
uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
if uploaded_file:
try:
col1, col2 = st.columns(2)
# -----------------------
# Image Preprocessing
# -----------------------
image = Image.open(uploaded_file).convert("RGB")
resized_image = image.resize((224, 224))
with col1:
st.subheader("Uploaded Image")
st.image(image, width="stretch")
img_array = np.array(resized_image) / 255.0
img_array = np.expand_dims(img_array, axis=0)
# -----------------------
# Predictions
# -----------------------
with col2:
st.subheader("Model Predictions")
for model_name, path in MODEL_PATHS.items():
model = load_model(path)
predictions = model.predict(img_array, verbose=0)[0]
top10_indices = np.argsort(predictions)[-10:][::-1]
st.markdown(f"### {model_name} - Top 10 Predictions")
for idx in top10_indices:
class_name = CLASS_NAMES[idx]
confidence = predictions[idx]
st.write(f"{class_name} — {confidence:.3f}")
st.divider()
except Exception as e:
st.error(f"Error during classification: {e}") |