Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,67 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+from tensorflow.keras.models import load_model
 
-"""
-# Welcome to Streamlit!
+st.set_page_config(page_title="Rice Classification", page_icon="🍚", layout="centered")
+
+
+model_path = "rice_efficientnet_feature_extractor.keras"
 
-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).
+CLASS_NAMES = ["Arborio", "Basmati", "Ipsala", "Jasmine", "Karacadag"]
 
-In the meantime, below is an example of what you can do with just a few lines of code:
+@st.cache_resource
+def load_cached_model():
+    return load_model(model_path)
+
+model = load_cached_model()
+
+"""@st.cache_resource
+def load_model():
+    model = tf.keras.models.load_model(MODEL_PATH)
+    return model"""
+
+def preprocess(File):
+    img = Image.open(File).convert("RGB")
+    img = img.resize((224,224))
+    x = np.array(img)
+    x = np.expand_dims(x,axis=0)
+    return img, x
+
+"""def topk(prob, k=3):
+    idx = np.argsort(prob)[::-1][:k]
+    return idx, prob[idx]
 """
 
-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.title("🍚 Rice Classification (Transfer Learning)")
+st.write("Upload an image and get the predicted rice type.")
+
+
+"""try:
+    model = load_model()
+except Exception as e:
+    st.error(f"Model load failed. Check MODEL_PATH.\n\nError: {e}")
+    st.stop()"""
+
+file = st.file_uploader("Upload an image", type=["jpg", "jpeg"])
+
+if file:
+    pil_img, x = preprocess(file)
+    preds = model.predict(x, verbose=0)
+    preds = np.array(preds)
+
+    st.image(pil_img, caption="Uploaded image", use_container_width=True)
+
+    prob = preds[0]
+
+    best_idx = np.argmax(prob)
+    best_label = CLASS_NAMES[best_idx]
+    best_conf = prob[best_idx]
+
+    st.subheader("Prediction")
+    st.success(f"{best_label} | confidence: {best_conf}")
+else:
+    st.caption("No image uploaded yet.")
+
+