Update app.py

app.py

@@ -1,29 +1,28 @@
-import streamlit as st
-from tensorflow.keras.models import load_model
-from PIL import Image
-import numpy as np
-import cv2
-import pandas as pd
-
-model = load_model('Bulut_CNN_model.keras')
-
-def process_image(img):
-    img = img.resize((64, 64))
-    img = img.convert('RGB')
-    img = np.array(img)
-    img = img / 255.0
-    img = np.expand_dims(img, axis=0)
-    return img
-
-df = pd.read_csv('train.csv') 
-class_names = df['label'].unique()
-st.write("Resim seç ve model ne olduğunu tahmin etsin")
-file = st.file_uploader('Bir resim seç', type=['jpg', 'jpeg', 'png'])
-
-if file is not None:
-    img = Image.open(file)
-    st.image(img, caption='Yüklenen resim', use_column_width=True)
-    image = process_image(img)
-    prediction = model.predict(image)
-    predicted_class = np.argmax(prediction, axis=1)[0]
-    st.write(f"Tahmin edilen sınıf: {class_names[predicted_class]}")
+import streamlit as st
+from tensorflow.keras.models import load_model
+from PIL import Image
+import numpy as np
+import pandas as pd
+
+model = load_model('Bulut_CNN_model.keras')
+
+def process_image(img):
+    img = img.resize((64, 64))
+    img = img.convert('RGB')
+    img = np.array(img)
+    img = img / 255.0
+    img = np.expand_dims(img, axis=0)
+    return img
+
+df = pd.read_csv('train.csv') 
+class_names = df['label'].unique()
+st.write("Resim seç ve model ne olduğunu tahmin etsin")
+file = st.file_uploader('Bir resim seç', type=['jpg', 'jpeg', 'png'])
+
+if file is not None:
+    img = Image.open(file)
+    st.image(img, caption='Yüklenen resim', use_column_width=True)
+    image = process_image(img)
+    prediction = model.predict(image)
+    predicted_class = np.argmax(prediction, axis=1)[0]
+    st.write(f"Tahmin edilen sınıf: {class_names[predicted_class]}")