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import streamlit as st
import torch.nn as nn
import torch
from torchvision import models, transforms
from PIL import Image
CATEGORIES = ["AIHOLE", "BILLESHWAR_TEMPLE", "CHENNAKESHWARA_TEMPLE", "HAMPI_CHARIOT", "IBRAHIM_ROZA", "JAIN_BASADI", "KAMAL_BASTI", "KEDARESHWARA_TEMPLE", "KESHAVA_TEMPLE", "LOTUS_MAHAL"]
IMG_SIZE = 224
# Load the trained model
model = models.resnet50(pretrained=False)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, len(CATEGORIES))
model.load_state_dict(torch.load("trained_model.pt", map_location=torch.device('cpu')))
model.eval()
# Define the image transform
transform = transforms.Compose([
transforms.Resize((IMG_SIZE, IMG_SIZE)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# Define the prediction function
def classify_image(image):
image = transform(image).unsqueeze(0)
# Make prediction
with torch.no_grad():
outputs = model(image)
_, predicted = torch.max(outputs.data, 1)
return predicted.item()
# Streamlit app
def main():
st.title("Temple Image Classification")
# File uploader
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_column_width=True)
# Classify image on button click
if st.button("Classify"):
prediction = classify_image(image)
st.write(f"Predicted Category: {CATEGORIES[prediction]}")
if __name__ == "__main__":
main()
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