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app.py

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+import streamlit as st
+import cv2
+import numpy as np
+import torch
+import torch.nn as nn
+import joblib
+import tensorflow as tf
+from PIL import Image, ImageDraw
+import os
+
+# Load the saved models
+# --- YOLOv5 Model ---
+class YOLOv5(nn.Module):
+    def __init__(self):
+        super(YOLOv5, self).__init__()
+        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
+        self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
+        self.fc1 = nn.Linear(64 * 16 * 16, 512)
+        self.fc2 = nn.Linear(512, 128)
+        self.fc3 = nn.Linear(128, 7)
+    
+    def forward(self, x):
+        x = self.pool(torch.relu(self.conv1(x)))
+        x = self.pool(torch.relu(self.conv2(x)))
+        x = self.pool(torch.relu(self.conv3(x)))
+        x = x.reshape(-1, 64 * 16 * 16)
+        x = torch.relu(self.fc1(x))
+        x = torch.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+yolo_model = YOLOv5()
+yolo_model.load_state_dict(torch.load('yolo_model.pth'))
+yolo_model.eval()
+
+# --- CNN Model ---
+cnn_model = tf.keras.models.load_model('cnn_model.h5')
+
+# --- ElasticNet Model ---
+elastic_net_model = joblib.load('elastic_net_model.joblib')
+
+# --- Hybrid YOLO-CNN Model ---
+class HybridYOLOCNN(nn.Module):
+    def __init__(self):
+        super(HybridYOLOCNN, self).__init__()
+        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
+        self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
+        self.fc1 = nn.Linear(64 * 16 * 16, 512)
+        self.fc2 = nn.Linear(512, 128)
+        self.fc3 = nn.Linear(128, 7)
+    
+    def forward(self, x):
+        x = self.pool(torch.relu(self.conv1(x)))
+        x = self.pool(torch.relu(self.conv2(x)))
+        x = self.pool(torch.relu(self.conv3(x)))
+        x = x.reshape(-1, 64 * 16 * 16)
+        x = torch.relu(self.fc1(x))
+        x = torch.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+hybrid_model = HybridYOLOCNN()
+hybrid_model.load_state_dict(torch.load('hybrid_yolo_cnn_model.pth'))
+hybrid_model.eval()
+
+# Define prediction functions with bounding boxes for each model
+def predict_with_box(img, model_type="yolo"):
+    img_tensor = torch.tensor(img).unsqueeze(0).permute(0, 3, 1, 2).float()
+    
+    # Make predictions based on the model type
+    if model_type == "yolo":
+        output = yolo_model(img_tensor)
+        pred_class = torch.argmax(output, dim=1).item()
+        x_center, y_center = 64, 64  # Center position
+    elif model_type == "hybrid":
+        output = hybrid_model(img_tensor)
+        pred_class = torch.argmax(output, dim=1).item()
+        x_center, y_center = 64, 64  # Center position
+    elif model_type == "cnn":
+        img_array = np.expand_dims(img, axis=0) / 255.0
+        output = cnn_model.predict(img_array)
+        pred_class = np.argmax(output, axis=1)[0]
+        x_center, y_center = 64, 64  # Center position
+    elif model_type == "elastic_net":
+        img_flattened = img.flatten().reshape(1, -1)
+        pred_class = int(np.clip(np.round(elastic_net_model.predict(img_flattened)), 0, 3)[0])
+        x_center, y_center = 84, 84  # Slightly offset from center
+
+    # Draw red square on the image
+    img_with_box = Image.fromarray(img)
+    draw = ImageDraw.Draw(img_with_box)
+    box_size = 20  # Example box size
+    box = (x_center - box_size, y_center - box_size, x_center + box_size, y_center + box_size)
+    draw.rectangle(box, outline="red", width=3)
+    
+    return img_with_box, pred_class
+
+# Streamlit app
+st.title("Skin Lesion Classification with Multiple Models")
+st.write("Upload up to 10 images, and get predictions from each model with highlighted areas.")
+
+uploaded_files = st.file_uploader("Choose images", accept_multiple_files=True, type=["jpg", "jpeg", "png"])
+if uploaded_files:
+    for file in uploaded_files[:10]:  # Limit to 10 images
+        # Load and resize image
+        img = Image.open(file).convert("RGB")
+        img = img.resize((128, 128))
+        img_np = np.array(img)
+
+        st.image(img, caption="Uploaded Image", use_column_width=True)
+        
+        # Model Predictions with bounding boxes
+        yolo_img, yolo_pred = predict_with_box(img_np, model_type="yolo")
+        cnn_img, cnn_pred = predict_with_box(img_np, model_type="cnn")
+        elastic_net_img, elastic_net_pred = predict_with_box(img_np, model_type="elastic_net")
+        hybrid_img, hybrid_pred = predict_with_box(img_np, model_type="hybrid")
+        
+        # Display results in columns with images
+        col1, col2, col3, col4 = st.columns(4)
+        with col1:
+            st.image(yolo_img, caption=f"YOLOv5 Prediction: {yolo_pred}", use_column_width=True)
+        with col2:
+            st.image(cnn_img, caption=f"CNN Prediction: {cnn_pred}", use_column_width=True)
+        with col3:
+            st.image(elastic_net_img, caption=f"ElasticNet Prediction: {elastic_net_pred}", use_column_width=True)
+        with col4:
+            st.image(hybrid_img, caption=f"Hybrid YOLO-CNN Prediction: {hybrid_pred}", use_column_width=True)

cnn_model.h5

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+oid sha256:1e41a9d0fb11c52ee7c1ced99b5ed120fdcdcca261bd27d736606c15fde6d740
+size 88749880

elastic_net_model.joblib

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+size 393863

hybrid_yolo_cnn_model.pth

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+size 33921554

yolo_model.pth

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