Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,72 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+import os
+os.environ["TF_USE_LEGACY_KERAS"] = "1"
+
 import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.applications.efficientnet import preprocess_input
+from PIL import Image
+import numpy as np
+
+# -----------------------------------
+# CONFIG
+# -----------------------------------
+IMG_SIZE = 300
+CLASS_NAMES = ['cup', 'fork', 'glass', 'knife', 'plate', 'spoon']
+
+# -----------------------------------
+# MODEL LOADING
+# -----------------------------------
+@st.cache_resource
+def load_model_file():
+    return tf.keras.models.load_model(
+        "src/kitchen1.keras",
+        compile=False
+    )
+
+model = load_model_file()
+
+# -----------------------------------
+# IMAGE PREPROCESS
+# -----------------------------------
+def process_image(img):
+    img = img.resize((IMG_SIZE, IMG_SIZE))
+    img = np.array(img)
+
+    if img.ndim == 2:
+        img = np.stack([img]*3, axis=-1)
+    elif img.shape[-1] == 4:
+        img = img[..., :3]
+
+    img = np.expand_dims(img, 0)
+    img = preprocess_input(img)
+    return img
+
+# -----------------------------------
+# UI LAYOUT
+# -----------------------------------
+st.title("🍽️ Kitchenware Classifier")
+st.write("Upload an image to classify kitchen items using EfficientNetB3.")
+
+uploaded = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+if uploaded:
+    img = Image.open(uploaded).convert("RGB")
+
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.image(img, caption="Uploaded Image", use_container_width=True)
+
+    with col2:
+        st.write("Classifying...")
+
+        image_tensor = process_image(img)
+        preds = model.predict(image_tensor)
+
+        predicted_index = np.argmax(preds)
+        predicted_class = CLASS_NAMES[predicted_index]
+        confidence = float(np.max(preds))
 
-"""
-# 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])),
-    ))
+        st.success(f"Prediction: **{predicted_class.upper()}**")
+        st.metric("Confidence", f"{confidence * 100:.2f}%")
+        st.progress(int(confidence * 100))