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app.py

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+#!/usr/bin/env python
+# coding: utf-8
+# dosyayı py olarak kaydet ve komut satırını kullanarak streamlit run streamlit.py
+
+import streamlit as st
+import torch
+from PIL import Image
+import numpy as np
+import torchvision.transforms as transforms
+
+# Modeli yükle
+model = torch.load('model-CNN.pt')  # veya torch.load('model-CNN.pth')
+model.eval()
+
+# Resmi işlemek için dönüşüm fonksiyonu
+def process_image(img):
+    transform = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+    img = transform(img)
+    img = img.unsqueeze(0)  # Batch boyutunu ekle
+    return img
+
+st.title('Animal Classification')
+st.write('Please choose an image so that the AI model can predict the type of animal.')
+file = st.file_uploader('Pick an image', type=['jpg', 'jpeg', 'png'])
+
+# Hayvan isimlerini yükle
+with open("name of the animals.txt") as f:
+    class_names = [x.strip() for x in f.readlines()]
+
+if file is not None:
+    img = Image.open(file)
+    st.image(img, caption='The image: ')
+    image = process_image(img)
+    
+    # Model ile tahmin yap
+    with torch.no_grad():
+        prediction = model(image)
+    
+    predicted_class = torch.argmax(prediction, dim=1).item()
+    st.write('Probability Distribution')
+    st.write(prediction.numpy())
+    st.write("Prediction: ", class_names[predicted_class])

model-CNN.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4db37c5b1d5889ebbeb820a052fc051977db06932cb236d9b32026b2416da2d2
+size 1412513