Delete src/fruit_1.py

src/fruit_1.py

@@ -1,32 +0,0 @@
-import streamlit as st
-from tensorflow.keras.models import load_model
-from PIL import Image
-import numpy as np
-import cv2
-
-model=load_model('fruit_vegetable.h5')
-
-def process_image(img):
-    img=img.resize((240,240))
-    img=np.array(img)
-    img=img/255.0
-    img=np.expand_dims(img,axis=0)
-    return img
-
-st.title("Meyve/Sebze sınıflandırma")
-st.write("Resmini yükle ve model meyve/sebze ismini 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='Meyve/Sebze')
-    image= process_image(img)
-    prediction=model.predict(image)
-    predicted_class=np.argmax(prediction)
-    
-    class_names=['capsicum','sweetcorn','orange','tomato','turnip','ginger','raddish','pomegranate','pineapple',
-        'jalepeno','apple','carrot','lettuce','bell pepper','eggplant','beetroot','kiwi','pear',
-        'cabbage','cauliflower','paprika','lemon','sweetpotato','grapes','cucumber','corn','banana',
-        'garlic','chilli pepper','watermelon','mango','peas','onion','potato','spinach','soy beans']
-    st.write(class_names[predicted_class])