app.py

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}")