Upload app.py

app.py

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+import streamlit as st
+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+from PIL import Image
+from resnet import Resnet50Flower102
+import pandas as pd
+from dataloader import transform
+st.title("Flower Image Classification")
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+model = Resnet50Flower102(device)
+flowers_data = pd.read_csv("flowerdata.csv")
+uploaded_file=st.file_uploader("Choose your file", type=["jpg", "png", "jpeg"])
+
+model.load_state_dict(torch.load("model.pth", map_location=torch.device(device)))
+
+transform_val = transforms.Compose([
+transforms.Resize((224, 224)),
+transforms.ToTensor(),
+transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    img = transform_val(image)
+    img = img.type(torch.FloatTensor).to(device)
+    print(img.shape)
+    img = img.unsqueeze(0)
+    print(img.shape)
+
+    with torch.no_grad():
+        model.eval()
+        flower = model(img)
+        _, flower = flower.max(1)
+    flower = flower[0].detach().cpu().numpy()
+    flower_name = flowers_data["Name"][flower]
+    st.header("Input Image")
+    st.image(image=image, use_column_width=True)
+    st.write("##", flower_name)