Upload 3 files

app.py

@@ -0,0 +1,185 @@
+import streamlit as st
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision import transforms
+from PIL import Image
+import os
+import time
+
+########################
+#     MODEL DEFINITION
+########################
+class MelanomaModel(nn.Module):
+    def __init__(self, out_size, dropout_prob=0.5):
+        super(MelanomaModel, self).__init__()
+        from efficientnet_pytorch import EfficientNet
+        self.efficient_net = EfficientNet.from_pretrained('efficientnet-b0')
+        # Remove the original FC layer
+        self.efficient_net._fc = nn.Identity()
+        
+        self.fc1 = nn.Linear(1280, 512)
+        self.fc2 = nn.Linear(512, 256)
+        self.fc3 = nn.Linear(256, out_size)
+        
+        self.dropout = nn.Dropout(dropout_prob)
+
+    def forward(self, x):
+        x = self.efficient_net(x)
+        x = x.view(x.size(0), -1)
+        x = F.relu(self.fc1(x))
+        x = self.dropout(x)
+        x = F.relu(self.fc2(x))
+        x = self.dropout(x)
+        x = self.fc3(x)
+        return x
+
+
+########################
+#     DIAGNOSIS MAP
+########################
+DIAGNOSIS_MAP = {
+    0: 'Melanoma',
+    1: 'Melanocytic nevus',
+    2: 'Basal cell carcinoma',
+    3: 'Actinic keratosis',
+    4: 'Benign keratosis',
+    5: 'Dermatofibroma',
+    6: 'Vascular lesion',
+    7: 'Squamous cell carcinoma',
+    8: 'Unknown'
+}
+
+########################
+#   LOAD MODEL FUNCTION
+########################
+@st.cache_resource
+def load_model():
+    """
+    Loads the model checkpoint.
+    Using weights_only=False (if you trust the .pth file).
+    If you prefer a more secure approach, re-save your checkpoint 
+    to only contain raw state_dict and set weights_only=True.
+    """
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = MelanomaModel(out_size=9)
+
+    # Path to your model file
+    model_path = os.path.join("model", "multi_weight.pth")
+
+    # If you trust the checkpoint file, set weights_only=False
+    checkpoint = torch.load(
+        model_path, 
+        map_location=device,
+        weights_only=False  # if you have a purely raw state_dict, you can use True
+    )
+    model.load_state_dict(checkpoint["model_state_dict"])
+
+    model.to(device)
+    model.eval()
+    
+    return model, device
+
+########################
+#   IMAGE TRANSFORM
+########################
+transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406],
+                         [0.229, 0.224, 0.225])
+])
+
+########################
+#   PREDICTION UTILS
+########################
+def predict_skin_lesion(img: Image.Image, model: nn.Module, device: torch.device):
+    # Transform and move image to device
+    img_tensor = transform(img).unsqueeze(0).to(device)
+    
+    with torch.no_grad():
+        outputs = model(img_tensor)
+        probs = F.softmax(outputs, dim=1)
+        top_probs, top_idxs = torch.topk(probs, 3, dim=1)  # top 3 predictions
+
+    predictions = []
+    for prob, idx in zip(top_probs[0], top_idxs[0]):
+        label = DIAGNOSIS_MAP.get(idx.item(), "Unknown")
+        confidence = prob.item() * 100
+        predictions.append((label, confidence))
+
+    return predictions
+
+########################
+#   PAGE CONFIG & STYLE
+########################
+st.set_page_config(
+    page_title="Skin Lesion Classifier",
+    page_icon=":microscope:",
+    layout="centered",
+    initial_sidebar_state="expanded"
+)
+
+def set_background_color():
+    st.markdown(
+        """
+        <style>
+        .stApp {
+            background-color: #FDEAE0;  /* A pale peach/light skin tone */
+        }
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+
+set_background_color()
+
+########################
+#   STREAMLIT APP
+########################
+def main():
+    st.title("Skin Lesion Classifier")
+    st.write("Upload an image of a skin lesion to see the top-3 predicted diagnoses.")
+
+    # Create a stylish sidebar
+    st.sidebar.title("Possible Diagnoses")
+    st.sidebar.markdown("Here are the categories the model can distinguish:")
+    for idx, diag in DIAGNOSIS_MAP.items():
+        st.sidebar.markdown(f"- **{diag}**")
+
+    # Add the names to the sidebar in a new section
+    st.sidebar.title("Team Members")
+    st.sidebar.markdown(
+        """
+        - **PRATHUSH MON**
+        - **PRATIK J**
+        - **RAYAN NASAR**
+        - **R HARIMURALI**
+        - **WASEEM AHAMMED**
+        """
+    )
+
+    # Load the model once (cached)
+    model, device = load_model()
+
+    # File uploader
+    uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])
+
+    if uploaded_file is not None:
+        # Display the image
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+        # Predict on button click
+        if st.button("Classify"):
+            with st.spinner("Analyzing..."):
+                time.sleep(3)  # 3-second spinner
+                results = predict_skin_lesion(image, model, device)
+            
+            st.subheader("Top-3 Predictions")
+            for i, (diagnosis, confidence) in enumerate(results, start=1):
+                st.write(f"{i}. **{diagnosis}**: {confidence:.2f}%")
+
+if __name__ == "__main__":
+    main()

multi_weight.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:79a52c72fc2442a3e5a178c2b47b307b701206f75226b1f0aa6241478a745a66
+size 19614586

requirements.txt

@@ -0,0 +1,5 @@
+streamlit==1.25.0
+torch==2.0.1
+torchvision==0.15.2
+efficientnet-pytorch==0.7.1
+Pillow==9.5.0