Update app.py

app.py

@@ -1,431 +1,431 @@
-# app.py
-
-from __future__ import print_function, division, absolute_import
-import streamlit as st
-import torch
-import torch.nn as nn
-from torchvision import transforms
-from PIL import Image, ImageDraw
-from ultralytics import YOLO
-from streamlit_drawable_canvas import st_canvas
-import os
-
-# --- Define Basic Components for InceptionResNetV2 ---
-class BasicConv2d(nn.Module):
-    def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
-        super(BasicConv2d, self).__init__()
-        self.conv = nn.Conv2d(in_planes, out_planes,
-                              kernel_size=kernel_size, stride=stride,
-                              padding=padding, bias=False)
-        self.bn = nn.BatchNorm2d(out_planes)
-        self.relu = nn.ReLU(inplace=False)
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.bn(x)
-        x = self.relu(x)
-        return x
-
-# --- Define InceptionResNetV2 Architecture ---
-class Mixed_5b(nn.Module):
-    def __init__(self):
-        super(Mixed_5b, self).__init__()
-        self.branch0 = BasicConv2d(192, 96, kernel_size=1, stride=1)
-
-        self.branch1 = nn.Sequential(
-            BasicConv2d(192, 48, kernel_size=1, stride=1),
-            BasicConv2d(48, 64, kernel_size=5, stride=1, padding=2)
-        )
-
-        self.branch2 = nn.Sequential(
-            BasicConv2d(192, 64, kernel_size=1, stride=1),
-            BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1),
-            BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1)
-        )
-
-        self.branch3 = nn.Sequential(
-            nn.AvgPool2d(3, stride=1, padding=1),
-            BasicConv2d(192, 64, kernel_size=1, stride=1)
-        )
-
-    def forward(self, x):
-        x0 = self.branch0(x)
-        x1 = self.branch1(x)
-        x2 = self.branch2(x)
-        x3 = self.branch3(x)
-        out = torch.cat((x0, x1, x2, x3), 1)
-        return out
-
-class Block35(nn.Module):
-    def __init__(self, scale=1.0):
-        super(Block35, self).__init__()
-        self.scale = scale
-
-        self.branch0 = BasicConv2d(320, 32, kernel_size=1, stride=1)
-
-        self.branch1 = nn.Sequential(
-            BasicConv2d(320, 32, kernel_size=1, stride=1),
-            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
-        )
-
-        self.branch2 = nn.Sequential(
-            BasicConv2d(320, 32, kernel_size=1, stride=1),
-            BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1),
-            BasicConv2d(48, 64, kernel_size=3, stride=1, padding=1)
-        )
-
-        self.conv2d = nn.Conv2d(128, 320, kernel_size=1, stride=1)
-        self.relu = nn.ReLU(inplace=False)
-
-    def forward(self, x):
-        x0 = self.branch0(x)
-        x1 = self.branch1(x)
-        x2 = self.branch2(x)
-        out = torch.cat((x0, x1, x2), 1)
-        out = self.conv2d(out)
-        out = out * self.scale + x
-        out = self.relu(out)
-        return out
-
-class Mixed_6a(nn.Module):
-    def __init__(self):
-        super(Mixed_6a, self).__init__()
-
-        self.branch0 = BasicConv2d(320, 384, kernel_size=3, stride=2)
-
-        self.branch1 = nn.Sequential(
-            BasicConv2d(320, 256, kernel_size=1, stride=1),
-            BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1),
-            BasicConv2d(256, 384, kernel_size=3, stride=2)
-        )
-
-        self.branch2 = nn.MaxPool2d(3, stride=2)
-
-    def forward(self, x):
-        x0 = self.branch0(x)
-        x1 = self.branch1(x)
-        x2 = self.branch2(x)
-        out = torch.cat((x0, x1, x2), 1)
-        return out
-
-class Block17(nn.Module):
-    def __init__(self, scale=1.0):
-        super(Block17, self).__init__()
-        self.scale = scale
-
-        self.branch0 = BasicConv2d(1088, 192, kernel_size=1, stride=1)
-
-        self.branch1 = nn.Sequential(
-            BasicConv2d(1088, 128, kernel_size=1, stride=1),
-            BasicConv2d(128, 160, kernel_size=(1, 7), stride=1, padding=(0, 3)),
-            BasicConv2d(160, 192, kernel_size=(7, 1), stride=1, padding=(3, 0))
-        )
-
-        self.conv2d = nn.Conv2d(384, 1088, kernel_size=1, stride=1)
-        self.relu = nn.ReLU(inplace=False)
-
-    def forward(self, x):
-        x0 = self.branch0(x)
-        x1 = self.branch1(x)
-        out = torch.cat((x0, x1), 1)
-        out = self.conv2d(out)
-        out = out * self.scale + x
-        out = self.relu(out)
-        return out
-
-class Mixed_7a(nn.Module):
-    def __init__(self):
-        super(Mixed_7a, self).__init__()
-
-        self.branch0 = nn.Sequential(
-            BasicConv2d(1088, 256, kernel_size=1, stride=1),
-            BasicConv2d(256, 384, kernel_size=3, stride=2)
-        )
-
-        self.branch1 = nn.Sequential(
-            BasicConv2d(1088, 256, kernel_size=1, stride=1),
-            BasicConv2d(256, 288, kernel_size=3, stride=2)
-        )
-
-        self.branch2 = nn.Sequential(
-            BasicConv2d(1088, 256, kernel_size=1, stride=1),
-            BasicConv2d(256, 288, kernel_size=3, stride=1, padding=1),
-            BasicConv2d(288, 320, kernel_size=3, stride=2)
-        )
-
-        self.branch3 = nn.MaxPool2d(3, stride=2)
-
-    def forward(self, x):
-        x0 = self.branch0(x)
-        x1 = self.branch1(x)
-        x2 = self.branch2(x)
-        x3 = self.branch3(x)
-        out = torch.cat((x0, x1, x2, x3), 1)
-        return out
-
-class Block8(nn.Module):
-    def __init__(self, scale=1.0, noReLU=False):
-        super(Block8, self).__init__()
-        self.scale = scale
-        self.noReLU = noReLU
-
-        self.branch0 = BasicConv2d(2080, 192, kernel_size=1, stride=1)
-
-        self.branch1 = nn.Sequential(
-            BasicConv2d(2080, 192, kernel_size=1, stride=1),
-            BasicConv2d(192, 224, kernel_size=(1, 3), stride=1, padding=(0, 1)),
-            BasicConv2d(224, 256, kernel_size=(3, 1), stride=1, padding=(1, 0))
-        )
-
-        self.conv2d = nn.Conv2d(448, 2080, kernel_size=1, stride=1)
-        if not self.noReLU:
-            self.relu = nn.ReLU(inplace=False)
-
-    def forward(self, x):
-        x0 = self.branch0(x)
-        x1 = self.branch1(x)
-        out = torch.cat((x0, x1), 1)
-        out = self.conv2d(out)
-        out = out * self.scale + x
-        if not self.noReLU:
-            out = self.relu(out)
-        return out
-
-class InceptionResNetV2(nn.Module):
-    def __init__(self, num_classes=1001):
-        super(InceptionResNetV2, self).__init__()
-        # Define all your layers here
-        self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2)
-        self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1)
-        self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1)
-        self.maxpool_3a = nn.MaxPool2d(3, stride=2)
-        self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1)
-        self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1)
-        self.maxpool_5a = nn.MaxPool2d(3, stride=2)
-        self.mixed_5b = Mixed_5b()
-        self.repeat = nn.Sequential(
-            Block35(scale=0.17),
-            Block35(scale=0.17),
-            Block35(scale=0.17),
-            Block35(scale=0.17),
-            Block35(scale=0.17),
-            Block35(scale=0.17),
-            Block35(scale=0.17),
-            Block35(scale=0.17),
-            Block35(scale=0.17),
-            Block35(scale=0.17)
-        )
-        self.mixed_6a = Mixed_6a()
-        self.repeat_1 = nn.Sequential(
-            Block17(scale=0.10),
-            Block17(scale=0.10),
-            Block17(scale=0.10),
-            Block17(scale=0.10),
-            Block17(scale=0.10),
-            Block17(scale=0.10),
-            Block17(scale=0.10),
-            Block17(scale=0.10),
-            Block17(scale=0.10),
-            Block17(scale=0.10),
-            Block17(scale=0.10),
-            Block17(scale=0.10),
-            Block17(scale=0.10),
-            Block17(scale=0.10),
-            Block17(scale=0.10),
-            Block17(scale=0.10),
-            Block17(scale=0.10),
-            Block17(scale=0.10),
-            Block17(scale=0.10),
-            Block17(scale=0.10)
-        )
-        self.mixed_7a = Mixed_7a()
-        self.repeat_2 = nn.Sequential(
-            Block8(scale=0.20),
-            Block8(scale=0.20),
-            Block8(scale=0.20),
-            Block8(scale=0.20),
-            Block8(scale=0.20),
-            Block8(scale=0.20),
-            Block8(scale=0.20),
-            Block8(scale=0.20),
-            Block8(scale=0.20)
-        )
-        self.block8 = Block8(noReLU=True)
-        self.conv2d_7b = BasicConv2d(2080, 1536, kernel_size=1, stride=1)
-        self.avgpool_1a = nn.AvgPool2d(8, stride=1, padding=0)
-        self.last_linear = nn.Linear(1536, num_classes)
-
-    def features(self, input):
-        x = self.conv2d_1a(input)
-        x = self.conv2d_2a(x)
-        x = self.conv2d_2b(x)
-        x = self.maxpool_3a(x)
-        x = self.conv2d_3b(x)
-        x = self.conv2d_4a(x)
-        x = self.maxpool_5a(x)
-        x = self.mixed_5b(x)
-        x = self.repeat(x)
-        x = self.mixed_6a(x)
-        x = self.repeat_1(x)
-        x = self.mixed_7a(x)
-        x = self.repeat_2(x)
-        x = self.block8(x)
-        x = self.conv2d_7b(x)
-        return x
-
-    def logits(self, features):
-        x = self.avgpool_1a(features)
-        x = x.view(x.size(0), -1)
-        x = self.last_linear(x)
-        return x
-
-    def forward(self, input):
-        x = self.features(input)
-        x = self.logits(x)
-        return x
-
-def inceptionresnetv2(num_classes=1000):
-    return InceptionResNetV2(num_classes=num_classes)
-
-# --- Load Models ---
-@st.cache_resource
-def load_inception_model(model_path):
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model = inceptionresnetv2(num_classes=2).to(device)  # Adjust num_classes as needed
-    model.load_state_dict(torch.load(model_path, map_location=device))
-    model.eval()
-    return model, device
-
-@st.cache_resource
-def load_yolo_model(yolo_model_path="yolov8n.pt"):
-    model = YOLO(yolo_model_path)  # You can specify a custom YOLOv8 model path if needed
-    return model
-
-# --- Image Preprocessing ---
-data_transforms = transforms.Compose([
-    transforms.Resize(342),
-    transforms.CenterCrop(299),
-    transforms.ToTensor(),
-    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-])
-
-# --- Streamlit App ---
-def main():
-    st.title("Image Anomaly Detection and Object Detection")
-    st.write("Upload an image to analyze for anomalies.")
-
-    # Load models
-    inception_model, device = load_inception_model(r'X:\mowito\Inception-ResNetV2-Weights\anamoly30.pth')  # Ensure 'anamoly30.pth' is in the same directory
-    yolo_model = load_yolo_model(r'X:\mowito\mowito.pt')  # Ensure 'yolov8n.pt' is in the same directory or specify the path
-
-    # Upload the image
-    uploaded_file = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"])
-    
-    # User input for confidence threshold
-    threshold = st.slider("Set Confidence Threshold", 0.0, 1.0, 0.5, 0.01)
-    
-    if uploaded_file is not None:
-        # Display the uploaded image
-        image = Image.open(uploaded_file).convert("RGB")
-        st.image(image, caption="Uploaded Image", width=400)
-
-        # Preprocess the image
-        transformed_image = data_transforms(image).unsqueeze(0).to(device)
-
-        # InceptionResNetV2 Prediction
-        with torch.no_grad():
-            outputs = inception_model(transformed_image)
-            _, predicted = torch.max(outputs, 1)
-            predicted_class = ['bad', 'good'][predicted.item()]
-            confidence = torch.nn.functional.softmax(outputs, dim=1)[0][predicted.item()].item()
-
-        st.write(f"**Prediction:** {predicted_class}")
-        st.write(f"**Confidence:** {confidence:.4f}")
-
-        # Check if confidence is above the threshold
-        if confidence >= threshold:
-            if predicted_class == "bad":
-                st.warning("Anomalies detected in the image. Processing further analysis...")
-
-                # Automatically run YOLOv8 on the uploaded image
-                st.write("Analyzing anomalies using YOLOv8...")
-                yolo_results = yolo_model.predict(source=image, conf=0.25, show=False)
-
-                # Display YOLOv8 predictions
-                st.write("### YOLOv8 Predictions:")
-                for result in yolo_results:
-                    # Plot the results on the image
-                    annotated_yolo_image = result.plot()
-                    st.image(annotated_yolo_image, caption="YOLOv8 Detection", width=400)
-
-                # Optionally, display detailed results
-                st.write("### Detection Details:")
-                for result in yolo_results:
-                    for box in result.boxes:
-                        cls = int(box.cls)
-                        conf = box.conf
-                        label = yolo_model.names[cls] if cls < len(yolo_model.names) else "Unknown"
-                        st.write(f"- **Label**: {label}, **Confidence**: {conf.item():.2f}")
-
-                # Provide interactive feedback option
-                st.info("You can annotate the image to refine analysis.")
-
-                # Initialize canvas for manual annotation
-                canvas_result = st_canvas(
-                    fill_color="rgba(255, 165, 0, 0.3)",  # Semi-transparent orange
-                    stroke_width=2,
-                    stroke_color="#FF0000",  # Red
-                    background_color="#FFFFFF",
-                    background_image=image,
-                    update_streamlit=True,
-                    height=image.height,
-                    width=image.width,
-                    drawing_mode="rect",  # Allow drawing rectangles
-                    key="canvas",
-                )
-
-                if canvas_result.json_data is not None:
-                    objects = canvas_result.json_data["objects"]
-                    if len(objects) > 0:
-                        st.success("Bounding boxes drawn. Click the button below to analyze with YOLOv8.")
-                        if st.button("Analyze Manual Annotations"):
-                            # Draw the bounding boxes on the image
-                            annotated_image = image.copy()
-                            draw = ImageDraw.Draw(annotated_image)
-                            for obj in objects:
-                                if obj["type"] == "rect":
-                                    left = obj["left"]
-                                    top = obj["top"]
-                                    width = obj["width"]
-                                    height = obj["height"]
-                                    draw.rectangle([left, top, left + width, top + height], outline="red", width=3)
-
-                            st.image(annotated_image, caption="Annotated Image", width=400)
-
-                            # Pass the manually annotated image to YOLOv8
-                            yolo_results_manual = yolo_model.predict(source=annotated_image, conf=0.25, show=False)
-
-                            # Display YOLOv8 predictions for annotated image
-                            st.write("### YOLOv8 Predictions (Manual Annotations):")
-                            for result in yolo_results_manual:
-                                # Plot the results on the image
-                                annotated_yolo_image_manual = result.plot()
-                                st.image(annotated_yolo_image_manual, caption="YOLOv8 Detection (Manual)", width=400)
-
-                            # Display detection details
-                            st.write("### Detection Details (Manual Annotations):")
-                            for result in yolo_results_manual:
-                                for box in result.boxes:
-                                    cls = int(box.cls)
-                                    conf = box.conf
-                                    label = yolo_model.names[cls] if cls < len(yolo_model.names) else "Unknown"
-                                    st.write(f"- **Label**: {label}, **Confidence**: {conf.item():.2f}")
-                    else:
-                        st.info("Draw bounding boxes around the anomalies and press the button to analyze.")
-        else:
-            st.warning(f"The confidence level ({confidence:.4f}) is below the threshold of {threshold}. No further analysis will be performed.")
-    else:
-        st.info("Please upload an image to get started.")
-
-if __name__ == "__main__":
+# app.py
+
+from __future__ import print_function, division, absolute_import
+import streamlit as st
+import torch
+import torch.nn as nn
+from torchvision import transforms
+from PIL import Image, ImageDraw
+from ultralytics import YOLO
+from streamlit_drawable_canvas import st_canvas
+import os
+
+# --- Define Basic Components for InceptionResNetV2 ---
+class BasicConv2d(nn.Module):
+    def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
+        super(BasicConv2d, self).__init__()
+        self.conv = nn.Conv2d(in_planes, out_planes,
+                              kernel_size=kernel_size, stride=stride,
+                              padding=padding, bias=False)
+        self.bn = nn.BatchNorm2d(out_planes)
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+# --- Define InceptionResNetV2 Architecture ---
+class Mixed_5b(nn.Module):
+    def __init__(self):
+        super(Mixed_5b, self).__init__()
+        self.branch0 = BasicConv2d(192, 96, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(192, 48, kernel_size=1, stride=1),
+            BasicConv2d(48, 64, kernel_size=5, stride=1, padding=2)
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(192, 64, kernel_size=1, stride=1),
+            BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.branch3 = nn.Sequential(
+            nn.AvgPool2d(3, stride=1, padding=1),
+            BasicConv2d(192, 64, kernel_size=1, stride=1)
+        )
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        x3 = self.branch3(x)
+        out = torch.cat((x0, x1, x2, x3), 1)
+        return out
+
+class Block35(nn.Module):
+    def __init__(self, scale=1.0):
+        super(Block35, self).__init__()
+        self.scale = scale
+
+        self.branch0 = BasicConv2d(320, 32, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(320, 32, kernel_size=1, stride=1),
+            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(320, 32, kernel_size=1, stride=1),
+            BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(48, 64, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.conv2d = nn.Conv2d(128, 320, kernel_size=1, stride=1)
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        out = torch.cat((x0, x1, x2), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        out = self.relu(out)
+        return out
+
+class Mixed_6a(nn.Module):
+    def __init__(self):
+        super(Mixed_6a, self).__init__()
+
+        self.branch0 = BasicConv2d(320, 384, kernel_size=3, stride=2)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(320, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(256, 384, kernel_size=3, stride=2)
+        )
+
+        self.branch2 = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        out = torch.cat((x0, x1, x2), 1)
+        return out
+
+class Block17(nn.Module):
+    def __init__(self, scale=1.0):
+        super(Block17, self).__init__()
+        self.scale = scale
+
+        self.branch0 = BasicConv2d(1088, 192, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(1088, 128, kernel_size=1, stride=1),
+            BasicConv2d(128, 160, kernel_size=(1, 7), stride=1, padding=(0, 3)),
+            BasicConv2d(160, 192, kernel_size=(7, 1), stride=1, padding=(3, 0))
+        )
+
+        self.conv2d = nn.Conv2d(384, 1088, kernel_size=1, stride=1)
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        out = torch.cat((x0, x1), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        out = self.relu(out)
+        return out
+
+class Mixed_7a(nn.Module):
+    def __init__(self):
+        super(Mixed_7a, self).__init__()
+
+        self.branch0 = nn.Sequential(
+            BasicConv2d(1088, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 384, kernel_size=3, stride=2)
+        )
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(1088, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 288, kernel_size=3, stride=2)
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(1088, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 288, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(288, 320, kernel_size=3, stride=2)
+        )
+
+        self.branch3 = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        x3 = self.branch3(x)
+        out = torch.cat((x0, x1, x2, x3), 1)
+        return out
+
+class Block8(nn.Module):
+    def __init__(self, scale=1.0, noReLU=False):
+        super(Block8, self).__init__()
+        self.scale = scale
+        self.noReLU = noReLU
+
+        self.branch0 = BasicConv2d(2080, 192, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(2080, 192, kernel_size=1, stride=1),
+            BasicConv2d(192, 224, kernel_size=(1, 3), stride=1, padding=(0, 1)),
+            BasicConv2d(224, 256, kernel_size=(3, 1), stride=1, padding=(1, 0))
+        )
+
+        self.conv2d = nn.Conv2d(448, 2080, kernel_size=1, stride=1)
+        if not self.noReLU:
+            self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        out = torch.cat((x0, x1), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        if not self.noReLU:
+            out = self.relu(out)
+        return out
+
+class InceptionResNetV2(nn.Module):
+    def __init__(self, num_classes=1001):
+        super(InceptionResNetV2, self).__init__()
+        # Define all your layers here
+        self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2)
+        self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1)
+        self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1)
+        self.maxpool_3a = nn.MaxPool2d(3, stride=2)
+        self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1)
+        self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1)
+        self.maxpool_5a = nn.MaxPool2d(3, stride=2)
+        self.mixed_5b = Mixed_5b()
+        self.repeat = nn.Sequential(
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17)
+        )
+        self.mixed_6a = Mixed_6a()
+        self.repeat_1 = nn.Sequential(
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10)
+        )
+        self.mixed_7a = Mixed_7a()
+        self.repeat_2 = nn.Sequential(
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20)
+        )
+        self.block8 = Block8(noReLU=True)
+        self.conv2d_7b = BasicConv2d(2080, 1536, kernel_size=1, stride=1)
+        self.avgpool_1a = nn.AvgPool2d(8, stride=1, padding=0)
+        self.last_linear = nn.Linear(1536, num_classes)
+
+    def features(self, input):
+        x = self.conv2d_1a(input)
+        x = self.conv2d_2a(x)
+        x = self.conv2d_2b(x)
+        x = self.maxpool_3a(x)
+        x = self.conv2d_3b(x)
+        x = self.conv2d_4a(x)
+        x = self.maxpool_5a(x)
+        x = self.mixed_5b(x)
+        x = self.repeat(x)
+        x = self.mixed_6a(x)
+        x = self.repeat_1(x)
+        x = self.mixed_7a(x)
+        x = self.repeat_2(x)
+        x = self.block8(x)
+        x = self.conv2d_7b(x)
+        return x
+
+    def logits(self, features):
+        x = self.avgpool_1a(features)
+        x = x.view(x.size(0), -1)
+        x = self.last_linear(x)
+        return x
+
+    def forward(self, input):
+        x = self.features(input)
+        x = self.logits(x)
+        return x
+
+def inceptionresnetv2(num_classes=1000):
+    return InceptionResNetV2(num_classes=num_classes)
+
+# --- Load Models ---
+@st.cache_resource
+def load_inception_model(model_path):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = inceptionresnetv2(num_classes=2).to(device)  # Adjust num_classes as needed
+    model.load_state_dict(torch.load(model_path, map_location=device))
+    model.eval()
+    return model, device
+
+@st.cache_resource
+def load_yolo_model(yolo_model_path="yolov8n.pt"):
+    model = YOLO(yolo_model_path)  # You can specify a custom YOLOv8 model path if needed
+    return model
+
+# --- Image Preprocessing ---
+data_transforms = transforms.Compose([
+    transforms.Resize(342),
+    transforms.CenterCrop(299),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+# --- Streamlit App ---
+def main():
+    st.title("Image Anomaly Detection and Object Detection")
+    st.write("Upload an image to analyze for anomalies.")
+
+    # Load models
+    inception_model, device = load_inception_model(r'anamoly30.pth')  # Ensure 'anamoly30.pth' is in the same directory
+    yolo_model = load_yolo_model(r'X:\mowito\mowito.pt')  # Ensure 'yolov8n.pt' is in the same directory or specify the path
+
+    # Upload the image
+    uploaded_file = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"])
+    
+    # User input for confidence threshold
+    threshold = st.slider("Set Confidence Threshold", 0.0, 1.0, 0.5, 0.01)
+    
+    if uploaded_file is not None:
+        # Display the uploaded image
+        image = Image.open(uploaded_file).convert("RGB")
+        st.image(image, caption="Uploaded Image", width=400)
+
+        # Preprocess the image
+        transformed_image = data_transforms(image).unsqueeze(0).to(device)
+
+        # InceptionResNetV2 Prediction
+        with torch.no_grad():
+            outputs = inception_model(transformed_image)
+            _, predicted = torch.max(outputs, 1)
+            predicted_class = ['bad', 'good'][predicted.item()]
+            confidence = torch.nn.functional.softmax(outputs, dim=1)[0][predicted.item()].item()
+
+        st.write(f"**Prediction:** {predicted_class}")
+        st.write(f"**Confidence:** {confidence:.4f}")
+
+        # Check if confidence is above the threshold
+        if confidence >= threshold:
+            if predicted_class == "bad":
+                st.warning("Anomalies detected in the image. Processing further analysis...")
+
+                # Automatically run YOLOv8 on the uploaded image
+                st.write("Analyzing anomalies using YOLOv8...")
+                yolo_results = yolo_model.predict(source=image, conf=0.25, show=False)
+
+                # Display YOLOv8 predictions
+                st.write("### YOLOv8 Predictions:")
+                for result in yolo_results:
+                    # Plot the results on the image
+                    annotated_yolo_image = result.plot()
+                    st.image(annotated_yolo_image, caption="YOLOv8 Detection", width=400)
+
+                # Optionally, display detailed results
+                st.write("### Detection Details:")
+                for result in yolo_results:
+                    for box in result.boxes:
+                        cls = int(box.cls)
+                        conf = box.conf
+                        label = yolo_model.names[cls] if cls < len(yolo_model.names) else "Unknown"
+                        st.write(f"- **Label**: {label}, **Confidence**: {conf.item():.2f}")
+
+                # Provide interactive feedback option
+                st.info("You can annotate the image to refine analysis.")
+
+                # Initialize canvas for manual annotation
+                canvas_result = st_canvas(
+                    fill_color="rgba(255, 165, 0, 0.3)",  # Semi-transparent orange
+                    stroke_width=2,
+                    stroke_color="#FF0000",  # Red
+                    background_color="#FFFFFF",
+                    background_image=image,
+                    update_streamlit=True,
+                    height=image.height,
+                    width=image.width,
+                    drawing_mode="rect",  # Allow drawing rectangles
+                    key="canvas",
+                )
+
+                if canvas_result.json_data is not None:
+                    objects = canvas_result.json_data["objects"]
+                    if len(objects) > 0:
+                        st.success("Bounding boxes drawn. Click the button below to analyze with YOLOv8.")
+                        if st.button("Analyze Manual Annotations"):
+                            # Draw the bounding boxes on the image
+                            annotated_image = image.copy()
+                            draw = ImageDraw.Draw(annotated_image)
+                            for obj in objects:
+                                if obj["type"] == "rect":
+                                    left = obj["left"]
+                                    top = obj["top"]
+                                    width = obj["width"]
+                                    height = obj["height"]
+                                    draw.rectangle([left, top, left + width, top + height], outline="red", width=3)
+
+                            st.image(annotated_image, caption="Annotated Image", width=400)
+
+                            # Pass the manually annotated image to YOLOv8
+                            yolo_results_manual = yolo_model.predict(source=annotated_image, conf=0.25, show=False)
+
+                            # Display YOLOv8 predictions for annotated image
+                            st.write("### YOLOv8 Predictions (Manual Annotations):")
+                            for result in yolo_results_manual:
+                                # Plot the results on the image
+                                annotated_yolo_image_manual = result.plot()
+                                st.image(annotated_yolo_image_manual, caption="YOLOv8 Detection (Manual)", width=400)
+
+                            # Display detection details
+                            st.write("### Detection Details (Manual Annotations):")
+                            for result in yolo_results_manual:
+                                for box in result.boxes:
+                                    cls = int(box.cls)
+                                    conf = box.conf
+                                    label = yolo_model.names[cls] if cls < len(yolo_model.names) else "Unknown"
+                                    st.write(f"- **Label**: {label}, **Confidence**: {conf.item():.2f}")
+                    else:
+                        st.info("Draw bounding boxes around the anomalies and press the button to analyze.")
+        else:
+            st.warning(f"The confidence level ({confidence:.4f}) is below the threshold of {threshold}. No further analysis will be performed.")
+    else:
+        st.info("Please upload an image to get started.")
+
+if __name__ == "__main__":
     main()