Upload 3 files

app.py

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+import streamlit as st
+import torch
+from PIL import Image
+from torchvision import transforms
+from model import ResNet50  # Assuming your model architecture is defined in a separate file called model.py
+
+# Load the model
+model = ResNet50()
+model.load_state_dict(torch.load('best_modelv2.pth', map_location=torch.device('cpu')))
+model.eval()
+
+# Define transform for input images
+data_transforms = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# Function to predict image label
+def predict_image_label(image):
+    # Preprocess the image
+    image = data_transforms(image).unsqueeze(0)
+
+    # Make prediction
+    with torch.no_grad():
+        output = model(image)
+        _, predicted = torch.max(output, 1)
+    
+    return predicted.item()
+
+# Streamlit app
+st.title("Leaf or Plant Classifier")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+
+    # Classify the image
+    prediction = predict_image_label(image)
+    label = 'Leaf' if prediction == 0 else 'Plant'
+    st.write(f"Prediction: {label}")

best_modelv2.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9bd76196b9333f2dac3eccbb8ca6d1e0a4cf5d3daf898e53a608fc796addc99a
+size 94370430

model.py

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+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision.transforms as transforms
+from torchvision import models
+from PIL import Image
+import os
+import random
+
+
+class ResNet50(nn.Module):
+    def __init__(self):
+        super(ResNet50, self).__init__()
+        self.resnet = models.resnet50(pretrained=True)
+        for param in self.resnet.parameters():
+            param.requires_grad = False
+        self.resnet.fc = nn.Sequential(
+            nn.Linear(2048, 2)
+        )
+
+    def forward(self, x):
+        x = self.resnet(x)
+        return x