app.py

# 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'best.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()