Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,80 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+"""Fish Species Classification — Transfer Learning (standalone Streamlit app)."""
+
+import sys
+from pathlib import Path
+
 import streamlit as st
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+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"
+        "Asagidaki komutu kullanin:\n\n"
+        f"```bash\ncd \"{Path(__file__).resolve().parent}\"\n"
+        "~/venvs/tensorflow/bin/streamlit run "
+        f"{Path(__file__).name}\n```"
+    )
+    st.stop()
+
+import json
+
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+
+ROOT = Path(__file__).resolve().parent
+
+MODEL_PATH = Path('src/fish_cnn.h5')
+META_PATH = Path('src/fish_cnn_meta.json')
+
+def get_preprocess(backbone):
+    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_model():
+    if not MODEL_PATH.is_file():
+        raise FileNotFoundError(f"Model not found: {MODEL_PATH}. Run fish_TL.ipynb first.")
+    return tf.keras.models.load_model(MODEL_PATH)
+
+
+def load_meta():
+    return json.loads(META_PATH.read_text(encoding="utf-8"))
+
+
+def prepare_image(img, meta):
+    size = tuple(meta["img_size"])
+    arr = np.array(img.convert("RGB").resize(size), dtype=np.float32)
+    arr = np.expand_dims(arr, axis=0)
+    preprocess = get_preprocess(meta.get('backbone', 'VGG16'))
+    arr = preprocess(arr)
+    return arr
+
+
+st.set_page_config(page_title="Fish Species Classification", page_icon="🧠")
+st.title("Fish Species Classification")
+st.caption("Transfer Learning — train with `fish_TL.ipynb`")
+
+try:
+    model = load_model()
+    meta = load_meta()
+except FileNotFoundError as e:
+    st.error(str(e))
+    st.stop()
+
+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)
+    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%}")