Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,96 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+
 import streamlit as st
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+import os
+
+# --- SAYFA AYARLARI / PAGE CONFIG ---
+st.set_page_config(page_title="Fruit & Veg Classifier", layout="wide", page_icon="🍎")
+
+# --- MODEL YÜKLEME / LOAD MODEL ---
+@st.cache_resource
+def load_my_model():
+    # Model dosya adının 'cnn_model.h5' olduğundan emin olun
+    model_path = "cnn_model.h5"
+    model = tf.keras.models.load_model(model_path)
+    return model
+
+try:
+    model = load_my_model()
+except Exception as e:
+    st.error(f"Model dosyası bulunamadı! / Model file not found!: {e}")
+
+# --- SINIF İSİMLERİ / CLASS NAMES ---
+# Veri setindeki klasör sırasına göre burayı güncelleyin
+class_names = [
+    'Apple', 'Banana', 'Beetroot', 'Bell Pepper', 'Cabbage', 'Capsicum', 
+    'Carrot', 'Cauliflower', 'Chilli Pepper', 'Corn', 'Cucumber', 'Eggplant', 
+    'Garlic', 'Ginger', 'Grapes', 'Jalepeno', 'Kiwi', 'Lemon', 'Lettuce', 
+    'Mango', 'Onion', 'Orange', 'Paprika', 'Pear', 'Peas', 'Pineapple', 
+    'Pomegranate', 'Potato', 'Raddish', 'Soy Beans', 'Spinach', 'Sweetcorn', 
+    'Sweetpotato', 'Tomato', 'Turnip', 'Watermelon'
+]
+
+# --- ARAYÜZ / UI ---
+st.title("🍎 Fruit & Veg Classifier / Meyve ve Sebze Sınıflandırıcı")
+st.write("Identify your produce using Deep Learning / Derin Öğrenme ile ürünlerinizi tanımlayın.")
+st.divider()
+
+# Yan Panel / Sidebar
+with st.sidebar:
+    st.header("Project Info / Proje Bilgisi")
+    st.info("""
+    **EN:** This model uses a custom 5-layer CNN architecture trained on fruit and vegetable images.
+    \n**TR:** Bu model, meyve ve sebze görüntüleri üzerinde eğitilmiş 5 katmanlı özel bir CNN mimarisi kullanır.
+    """)
+    st.subheader("Species / Türler")
+    st.write(", ".join(class_names))
+
+# Ana İçerik / Main Content
+col1, col2 = st.columns([1, 1])
+
+with col1:
+    st.subheader("Upload / Yükle 📤")
+    uploaded_file = st.file_uploader("Choose a photo / Bir fotoğraf seçin...", type=["jpg", "jpeg", "png"])
+    
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image / Yüklenen Resim", use_container_width=True)
+
+with col2:
+    st.subheader("Result / Sonuç 🔍")
+    
+    if uploaded_file is not None:
+        if st.button("Predict / Tahmin Et"):
+            with st.spinner("Processing... / İşleniyor..."):
+                # Ön İşleme / Preprocessing (Modelin eğitildiği boyuta göre: 128x128)
+                img = image.resize((128, 128))
+                img_array = np.array(img)
+                
+                # Eğer model 0-1 arası normalizasyon istiyorsa:
+                if img_array.max() > 1:
+                    img_array = img_array.astype('float32') / 255.0
+                
+                img_array = np.expand_dims(img_array, axis=0)
+                
+                # Tahmin / Prediction
+                predictions = model.predict(img_array)
+                score = np.max(predictions[0]) * 100
+                class_idx = np.argmax(predictions[0])
+                result = class_names[class_idx]
+                
+                # Sonuç Ekranı / Result Display
+                st.success(f"### {result}")
+                st.write(f"**Confidence / Güven:** %{score:.2f}")
+                
+                # İlerleme Çubuğu / Progress Bar
+                st.progress(int(score))
+                
+                # Kutlama / Celebration
+                st.balloons()
+    else:
+        st.write("Please upload an image to see the result. / Sonucu görmek için lütfen bir resim yükleyin.")
 
-"""
-# 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.divider()
+st.caption("Deep Learning Course Project / Derin Öğrenme Kurs Projesi")