Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,52 @@
-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
+
+model = tf.keras.models.load_model("hurma_cnn_model.h5")
+
+# Sınıf isimlerini yazıyoruz
+
+class_names = [
+    "Ajwa",
+    "Galaxy",
+    "Medjool",
+    "Meneifi",
+    "Nabtat Ali",
+    "Rutab",
+    "Shaishe",
+    "Sokari",
+    "Sugaey"
+]
+
+st.title("Hurma Sınıflandırma Uygulaması")
+st.write("Bir hurma görseli yükleyin, model hurma türünü tahmin etsin.")
+
+# kullanıcıdan görsel alıyoruz
+uploaded_file = st.file_uploader("Bir hurma resmi yükleyin", type=["jpg", "jpeg", "png"])
+
+# Şimdi yüklenen resmi işleyip tahmin yapıyoruz
+
+if uploaded_file is not None:
+
+    image = Image.open(uploaded_file).convert("RGB")
+
+    st.image(image, caption="Yüklenen Görsel", use_container_width=True)
+
+    image = image.resize((128, 128))
+
+    image_array = np.array(image) / 255.0
+
+    image_array = np.expand_dims(image_array, axis=0)
+
+    prediction = model.predict(image_array)
+
+    predicted_index = np.argmax(prediction)
+
+    predicted_class = class_names[predicted_index]
+
+    confidence = np.max(prediction) * 100
+
+    st.success(f"Tahmin Edilen Hurma Türü: {predicted_class}")
 
-"""
-# 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.write(f"Güven Oranı: %{confidence:.2f}")