Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,68 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# -----------------------------
+# 1. Instellingen
+# -----------------------------
+# Gebruik dezelfde image size als bij training!
+IMG_SIZE = (224, 224)   # of (128, 128) als je model daarop is getraind
+
+CLASS_NAMES = ['Karacadag', 'Basmati', 'Jasmine', 'Arborio', 'Ipsala']
+
+st.set_page_config(page_title="Rice Classifier", page_icon="🌾")
+st.title("🌾 Rice Classifier")
+st.write("Upload een rijstkorrel-afbeelding om het type te laten voorspellen.")
+
+# -----------------------------
+# 2. Laad TFLite model
+# -----------------------------
+@st.cache_resource
+def load_interpreter():
+    interpreter = tf.lite.Interpreter(model_path="rice_model.tflite")
+    interpreter.allocate_tensors()
+    input_details = interpreter.get_input_details()
+    output_details = interpreter.get_output_details()
+    return interpreter, input_details, output_details
+
+try:
+    interpreter, input_details, output_details = load_interpreter()
+except Exception as e:
+    st.error(f"Kon TFLite-model niet laden: {e}")
+    st.stop()
+
+# -----------------------------
+# 3. Upload afbeelding
+# -----------------------------
+uploaded = st.file_uploader("Upload een afbeelding", type=["jpg", "jpeg", "png"])
+
+if uploaded is None:
+    st.info("👆 Kies hierboven een afbeelding.")
+else:
+    # Toon de geüploade afbeelding
+    img = Image.open(uploaded)
+    st.image(img, caption="Geüploade afbeelding", use_container_width=True)
+
+    # Zorg dat het echt RGB is
+    if img.mode != "RGB":
+        img = img.convert("RGB")
+
+    # Resize + normaliseer
+    img = img.resize(IMG_SIZE)
+    img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0).astype(np.float32)
+
+    # -----------------------------
+    # 4. Voorspelling
+    # -----------------------------
+    interpreter.set_tensor(input_details[0]['index'], img_array)
+    interpreter.invoke()
+    prediction = interpreter.get_tensor(output_details[0]['index'])
+
+    idx = int(np.argmax(prediction))
+    confidence = float(prediction[0][idx])
 
-"""
-# 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.subheader("🔍 Resultaat")
+    st.write(f"**Predicted class:** {CLASS_NAMES[idx]}")
+    st.write(f"**Confidence:** {confidence:.4f}")