Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,33 @@
-import altair as alt
 import numpy as np
-import pandas as pd
+from tensorflow.keras.models import load_model
+from PIL import Image
 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])),
-    ))
+# Modeli yükle (src klasörünün içindeyse)
+model = load_model('src/dates_classifier_model.h5')
+
+def process_image(img):
+    img = img.resize((224, 224))
+    img = np.array(img) / 255.0
+    img = np.expand_dims(img, axis=0)
+    return img
+
+st.title('Hurma Resmi Sınıflandırma')
+st.write('Bir hurma resmi yükleyin, hangi tür olduğunu tahmin edelim.')
+
+file = st.file_uploader('Bir Resim Seçin', type=['jpg', 'jpeg', 'png'])
+
+if file is not None:
+    img = Image.open(file)
+    st.image(img, caption='Yüklenen Resim', use_column_width=True)
+
+    processed_image = process_image(img)
+    prediction = model.predict(processed_image)
+    predicted_class = np.argmax(prediction)
+
+    class_names = [
+        'Rutab', 'Meneifi', 'Sokari', 'Galaxy', 'Shaishe',
+        'Medjool', 'Ajwa', 'Nabtat Ali', 'Sugaey'
+    ]
+
+    st.write(f'Tahmin Edilen Sınıf: **{class_names[predicted_class]}**')