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Historical places detection for ten class of temples

ancientdetection.py

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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()

trained_model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:efa64c02a8e1261be97bd81c1567faba65d94c7a30c61083e92b77b42e0df3c5
+size 94433165