Fruits and Vegetables Image Detection

app.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_fv_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('Meyze Sebze Siniflandirmasi :tomato:')
+st.write('Resim seç ve hangi meyve/sebze 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=['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']
+    st.write(class_names[predicted_class])

requierements.txt

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+streamlit 
+tensorflow