Upload 5 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+logo/logo.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,89 @@
+import streamlit as st
+from ultralytics import YOLO
+from PIL import Image
+import torchvision.transforms as transforms
+import base64
+
+# Set Streamlit Page Configuration
+st.set_page_config(
+    page_title="Lung Cancer Detection",
+    page_icon="logo/logo.png",
+    layout="centered"
+)
+
+# Cache model loaders for each detection type
+@st.cache_resource()
+def load_lung_model():
+    return YOLO("weights/Lung Cancer Detection.pt")  # Path for lung cancer detection model
+
+# Load lung model only
+lung_model = load_lung_model()
+
+# Define image transformation pipeline
+transform = transforms.Compose([
+    transforms.Resize((640, 640)),
+    transforms.ToTensor()
+])
+
+# Update prediction function to accept a model parameter
+def predict_tumor(image: Image.Image, model):
+    try:
+        image_tensor = transform(image).unsqueeze(0)  # Add batch dimension
+        results = model.predict(image_tensor)
+        output_image = results[0].plot()  # Overlay segmentation mask
+        return Image.fromarray(output_image)
+    except Exception as e:
+        st.error(f"Prediction Error: {e}")
+        return None
+
+# Function to encode image to base64 for embedding
+def get_base64_image(image_path):
+    with open(image_path, "rb") as img_file:
+        return base64.b64encode(img_file.read()).decode()
+
+# Display logo
+image_base64 = get_base64_image("logo/logo.png")
+st.markdown(
+    f'<div style="text-align: center;"><img src="data:image/png;base64,{image_base64}" width="100"></div>',
+    unsafe_allow_html=True
+)
+
+# --- UI Customization ---
+st.markdown("""
+    <style>
+        [data-testid="stSidebar"] { background-color: #1E1E2F; }
+        [data-testid="stSidebar"] h1, [data-testid="stSidebar"] h2 { color: white; }
+        h1 { text-align: center; font-size: 36px; font-weight: bold; color: #2C3E50; }
+        div.stButton > button { background-color: #3498DB; color: white; font-weight: bold; }
+        div.stButton > button:hover { background-color: #2980B9; }
+    </style>
+""", unsafe_allow_html=True)
+
+# --- Sidebar ---
+st.sidebar.header("📤 Upload a CT Image")
+uploaded_file = st.sidebar.file_uploader("Drag and drop or browse", type=['jpg', 'png', 'jpeg'])
+
+# Updated: remove detection option since only lung cancer is supported now
+detection_option = "Lung Cancer"
+
+# --- Main Page ---
+st.title("Lung Cancer Detection")
+st.markdown("<p style='text-align: center;'>Detect and segment lung cancer from CT scans.</p>", unsafe_allow_html=True)
+
+if uploaded_file:
+    image = Image.open(uploaded_file).convert("RGB")
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.image(image, caption="📷 Uploaded Image", use_container_width=True)
+    
+    if st.sidebar.button("🔍 Predict " + detection_option):
+        segmented_image = predict_tumor(image, lung_model)
+        if segmented_image:
+            with col2:
+                st.image(segmented_image, caption="🎯 Segmented Lung Cancer", use_container_width=True)
+        else:
+            st.error("Segmentation failed. Please try again.")
+
+st.markdown("---")
+st.info("This app uses **YOLO-Seg** for real-time lung cancer detection. Upload a CT image to get started.")

requirements.txt

@@ -0,0 +1,6 @@
+Flask
+ultralytics
+Pillow
+torch
+torchvision
+numpy

weights/Lung Cancer Detection.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dbf3cf58b36f47888ee0120bae72b9583ac7af5cb8cc814313693201e9c24e6e
+size 5996957