Upload 3 files

app.py

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+import streamlit as st
+import torch
+import torch.nn as nn
+import timm
+from PIL import Image
+from torchvision import transforms
+
+# Configuration and model definition
+CONFIG = dict(
+    seed = 42,
+    model_name = 'tf_efficientnet_b4_ns',
+    train_batch_size = 16,
+    valid_batch_size = 32,
+    img_size = 256,
+    epochs = 5,
+    learning_rate = 1e-4,
+    scheduler = 'CosineAnnealingLR',
+    min_lr = 1e-6,
+    T_max = 100,
+    T_0 = 25,
+    warmup_epochs = 0,
+    weight_decay = 1e-6,
+    n_accumulate = 1,
+    n_fold = 5,
+    num_classes = 1,
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu"),
+    competition = 'PetFinder',
+    _wandb_kernel = 'deb'
+)
+
+class PawpularityModel(nn.Module):
+    def __init__(self, model_name, pretrained=True):
+        super(PawpularityModel, self).__init__()
+        self.model = timm.create_model(model_name, pretrained=pretrained, num_classes=0)
+        self.fc = nn.LazyLinear(CONFIG['num_classes'])
+        self.dropout = nn.Dropout(p=0.3)
+
+    def forward(self, images, meta):
+        features = self.model(images)                 # Extract features
+        features = self.dropout(features)
+        features = torch.cat([features, meta], dim=1) # Concatenate metadata
+        output = self.fc(features)                    # Predict Pawpularity
+        return output
+
+
+
+# Load the model
+model = PawpularityModel(CONFIG['model_name'])
+model.load_state_dict(torch.load('model_new.pth', map_location=CONFIG['device']))
+model.to(CONFIG['device'])
+model.eval()
+
+# Define image transformation
+transform = transforms.Compose([
+    transforms.Resize((256, 256)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+
+st.title("Pawpularity Score Prediction 🐾")
+st.write("Project by Shreya Sivakumar-20BCE1794")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).convert('RGB')
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+    
+    # Preprocess the image and prepare dummy metadata (replace with actual metadata handling)
+    image = transform(image).unsqueeze(0).to(CONFIG['device'])
+    meta = torch.zeros((1, 12)).to(CONFIG['device'])  
+
+
+    with torch.no_grad():
+        output = model(image, meta)
+        pawpularity_score = output.item()
+
+    st.markdown(f"<h2 style='text-align: center; color: black;'>🐾 Pawpularity Score: {pawpularity_score}</h1>", unsafe_allow_html=True)
+    st.markdown("""
+    ---
+    Copyright © 2024 Shreya Sivakumar. All rights reserved.
+    """)

model_new.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d15b049e176bee74a9480ad9208dc43d7cb7bf0ecc2721f803a53ec37fadc11d
+size 70948090

requirements.txt

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+absl-py==2.1.0
+altair==5.2.0
+astunparse==1.6.3
+attrs==23.2.0
+blinker==1.7.0
+cachetools==5.3.3
+certifi==2024.2.2
+charset-normalizer==3.3.2
+click==8.1.7
+contourpy==1.2.0
+cycler==0.12.1
+filelock==3.13.1
+flatbuffers==24.3.7
+fonttools==4.50.0
+fsspec==2024.3.1
+gast==0.5.4
+gitdb==4.0.11
+GitPython==3.1.42
+google-pasta==0.2.0
+grpcio==1.62.1
+h5py==3.10.0
+huggingface-hub==0.21.4
+idna==3.6
+Jinja2==3.1.3
+jsonschema==4.21.1
+jsonschema-specifications==2023.12.1
+keras==3.1.1
+kiwisolver==1.4.5
+libclang==18.1.1
+Markdown==3.6
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.8.3
+mdurl==0.1.2
+ml-dtypes==0.3.2
+mpmath==1.3.0
+namex==0.0.7
+networkx==3.2.1
+numpy==1.26.4
+nvidia-cublas-cu12==12.1.3.1
+nvidia-cuda-cupti-cu12==12.1.105
+nvidia-cuda-nvrtc-cu12==12.1.105
+nvidia-cuda-runtime-cu12==12.1.105
+nvidia-cudnn-cu12==8.9.2.26
+nvidia-cufft-cu12==11.0.2.54
+nvidia-curand-cu12==10.3.2.106
+nvidia-cusolver-cu12==11.4.5.107
+nvidia-cusparse-cu12==12.1.0.106
+nvidia-nccl-cu12==2.19.3
+nvidia-nvjitlink-cu12==12.4.99
+nvidia-nvtx-cu12==12.1.105
+opt-einsum==3.3.0
+optree==0.10.0
+packaging==23.2
+pandas==2.2.1
+pillow==10.2.0
+protobuf==4.25.3
+pyarrow==15.0.2
+pydeck==0.8.1b0
+Pygments==2.17.2
+pyparsing==3.1.2
+python-dateutil==2.9.0.post0
+pytz==2024.1
+PyYAML==6.0.1
+referencing==0.34.0
+requests==2.31.0
+rich==13.7.1
+rpds-py==0.18.0
+safetensors==0.4.2
+six==1.16.0
+smmap==5.0.1
+streamlit==1.32.2
+sympy==1.12
+tenacity==8.2.3
+tensorboard==2.16.2
+tensorboard-data-server==0.7.2
+tensorflow==2.16.1
+tensorflow-io-gcs-filesystem==0.36.0
+termcolor==2.4.0
+timm==0.9.16
+toml==0.10.2
+toolz==0.12.1
+torch==2.2.1
+torchvision==0.17.1
+tornado==6.4
+tqdm==4.66.2
+triton==2.2.0
+typing_extensions==4.10.0
+tzdata==2024.1
+urllib3==2.2.1
+watchdog==4.0.0
+Werkzeug==3.0.1
+wrapt==1.16.0