import streamlit as st
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import cv2
import numpy as np
from PIL import Image

# Define the VanillaCNN_SE class
class SEBlock(nn.Module):
    def __init__(self, channels, reduction_ratio=16):
        super(SEBlock, self).__init__()
        self.global_avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc1 = nn.Linear(channels, channels // reduction_ratio)
        self.fc2 = nn.Linear(channels // reduction_ratio, channels)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        batch_size, channels, _, _ = x.size()
        y = self.global_avg_pool(x).view(batch_size, channels)
        y = torch.relu(self.fc1(y))
        y = self.sigmoid(self.fc2(y)).view(batch_size, channels, 1, 1)
        return x * y

class VanillaCNN_SE(nn.Module):
    def __init__(self, num_classes):
        super(VanillaCNN_SE, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.se1 = SEBlock(64)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(128)
        self.se2 = SEBlock(128)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.bn3 = nn.BatchNorm2d(256)
        self.se3 = SEBlock(256)
        self.conv4 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
        self.bn4 = nn.BatchNorm2d(512)
        self.se4 = SEBlock(512)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(512 * 14 * 14, 1024)
        self.fc2 = nn.Linear(1024, num_classes)

    def forward(self, x):
        x = self.pool(torch.relu(self.bn1(self.conv1(x))))
        x = self.se1(x)
        x = self.pool(torch.relu(self.bn2(self.conv2(x))))
        x = self.se2(x)
        x = self.pool(torch.relu(self.bn3(self.conv3(x))))
        x = self.se3(x)
        x = self.pool(torch.relu(self.bn4(self.conv4(x))))
        x = self.se4(x)
        x = x.view(x.size(0), -1)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# Load the model
@st.cache_resource

def load_model():
    model = VanillaCNN_SE(num_classes=12)  # Update num_classes as per your dataset
    model.load_state_dict(torch.load("vanilla_cnn_se.pth", map_location=torch.device('cpu')))
    model.eval()
    return model

model = load_model()

# Define class names
class_names = [
    "Maize", "Common wheat", "Common Chickweed", "Loose Silky-bent",
    "Charlock", "Cleavers", "Sugar beet", "Fat Hen", "Scentless Mayweed",
    "Small-flowered Cranesbill", "Shepherd’s Purse", "Black-grass"
]

# Define transformations
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor()
])

def mask_image(image):
    # Convert PIL image to OpenCV format
    image_np = np.array(image)
    hsv_img = cv2.cvtColor(image_np, cv2.COLOR_RGB2HSV)

    # Define green color range
    lower_green = np.array([30, 40, 40])
    upper_green = np.array([90, 255, 255])

    # Create a mask for the green area
    mask = cv2.inRange(hsv_img, lower_green, upper_green)
    masked_img = cv2.bitwise_and(image_np, image_np, mask=mask)

    # Convert back to PIL image
    return Image.fromarray(masked_img)

def predict_class(image):
    # Transform the image for the model
    image_tensor = transform(image).unsqueeze(0)

    # Predict the class
    with torch.no_grad():
        outputs = model(image_tensor)
        _, predicted = torch.max(outputs, 1)
        return class_names[predicted.item()]

# Streamlit UI
st.title("Plant Seedling Classification")

st.write("Upload an image to classify the plant seedling and view the masked image.")

# File uploader
uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    # Load the image
    image = Image.open(uploaded_file).convert("RGB")

    # Mask the image
    masked_image = mask_image(image)

    # Predict the class
    predicted_class = predict_class(image)

    # Display results
    st.image(image, caption="Original Image", use_column_width=True)
    st.image(masked_image, caption="Masked Image", use_column_width=True)
    st.write(f"### Predicted Class: {predicted_class}")