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import json
import sys
from pathlib import Path
import streamlit as st
if "/opt/anaconda3" in sys.executable:
st.set_page_config(page_title="Python ortam hatasi", page_icon="⚠️")
st.error(
"Bu uygulama Anaconda Python ile calismaz (segmentation fault).\n\n"
f"```bash\ncd \"{Path(__file__).resolve().parent}\"\n"
f"~/venvs/tensorflow/bin/streamlit run {Path(__file__).name}\n```"
)
st.stop()
import numpy as np
import tensorflow as tf
from PIL import Image
ROOT = Path(__file__).resolve().parent
MODEL_CONFIG = {
"Convolutional Neural Networks": {
"stem": "fish_cnn",
"notebook": "FishClassificationNew.ipynb",
},
"Transfer Learning": {
"stem": "fish_tl",
"notebook": "FishClassificationNew.ipynb",
},
}
def get_preprocess(backbone: str):
if backbone == "VGG16":
from tensorflow.keras.applications.vgg16 import preprocess_input
return preprocess_input
if backbone == "ResNet50":
from tensorflow.keras.applications.resnet50 import preprocess_input
return preprocess_input
from tensorflow.keras.applications.xception import preprocess_input
return preprocess_input
@st.cache_resource
def load_artifacts(model_key: str):
stem = MODEL_CONFIG[model_key]["stem"]
models_dir = ROOT
for ext in (".h5", ".keras"):
path = models_dir / f"{stem}{ext}"
if path.is_file():
model = tf.keras.models.load_model(path)
break
else:
raise FileNotFoundError(
f"Model not found for {model_key}. Run {MODEL_CONFIG[model_key]['notebook']} first."
)
meta_path = models_dir / f"{stem}_meta.json"
meta = json.loads(meta_path.read_text(encoding="utf-8"))
return model, meta
def prepare_image(img: Image.Image, meta: dict, model_key: str) -> np.ndarray:
size = tuple(meta["img_size"])
arr = np.array(img.convert("RGB").resize(size), dtype=np.float32)
arr = np.expand_dims(arr, axis=0)
if model_key == "Convolutional Neural Networks":
arr = arr / 255.0
else:
arr = get_preprocess(meta.get("backbone", "Xception"))(arr)
return arr
st.set_page_config(page_title="Fish Species Classification", page_icon="🐟")
st.title("Fish Species Classification")
model_type = st.radio(
"Model",
list(MODEL_CONFIG.keys()),
horizontal=True,
)
try:
model, meta = load_artifacts(model_type)
except FileNotFoundError as e:
st.error(str(e))
st.stop()
if model_type == "Transfer Learning":
st.caption(f"Backbone: {meta.get('backbone', 'Xception')}")
else:
st.caption("Custom CNN")
uploaded = st.file_uploader("Upload image (jpg/png)", type=["jpg", "jpeg", "png"])
if uploaded:
img = Image.open(uploaded)
st.image(img, use_container_width=True)
batch = prepare_image(img, meta, model_type)
probs = model.predict(batch, verbose=0)[0]
idx = int(np.argmax(probs))
label = meta["class_names"][idx]
st.success(f"Prediction: **{label}**")
st.write(f"Confidence: {probs[idx]:.2%}")
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