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app2.py

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+import streamlit as st
+from tensorflow.keras.models import load_model
+from PIL import Image
+import numpy as np
+
+model=load_model('my_fish_model.h5')
+
+def process_image(img):
+    img=img.resize((170,170))  #boyutunu 170*170 pixel yaptık
+    img=np.array(img)
+    img=img/255.0  #Normalize ettik
+    img=np.expand_dims(img,axis=0)
+    return img
+
+st.title('Large Scale Fish :fish:')
+st.write('Resim seç ve hangi balik türü olduğunu tahmin etsin')
+
+file=st.file_uploader('Bir Resim Sec', type=['jpg','jpeg','png'])
+
+if file is not None:
+    img=Image.open(file)
+    st.image(img,caption='yuklenen resim')
+    image=process_image(img)
+    prediction=model.predict(image)
+    predicted_class=np.argmax(prediction)
+
+    class_names=['Black Sea Sprat','Gilt-Head Bream','Hourse Mackerel','Red Mullet',
+                 'Red Sea Bream','Sea Bass','Shrimp','Striped Red Mullet','Trout']
+    st.write(class_names[predicted_class])

requierements.txt

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+streamlit 
+tensorflow