import torch
import torch.nn as nn
from efficientnet_pytorch import EfficientNet
import gradio as gr



# Define the custom model architecture
class CustomModel(nn.Module):
    def __init__(self):
        super(CustomModel, self).__init__()
        self.fc = nn.Linear(6, 50176)
        self.fc_bn = nn.BatchNorm1d(50176)
        self.pretrained_model = EfficientNet.from_pretrained('efficientnet-b0', num_classes=32)
        self.classification_head = nn.Sequential(
            nn.Linear(32, 1),
            nn.Sigmoid()
        )

    def forward(self, x):
        x = self.fc(x)
        x = self.fc_bn(x)
        x = x.view(-1, 224, 224)
        x = torch.stack([x] * 3, dim=1)
        x = self.pretrained_model(x)
        x = self.classification_head(x)
        return x

# Load the trained model
model = CustomModel()
model.load_state_dict(torch.load('best_model_efficientnet_b0.pth'))
model.eval()

# Load the validation dataset
#val_dataset = CustomDataset('outside.csv')
#val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)

# Function to make prediction
def predict(feature1, feature2, feature3, feature4, feature5, feature6):
    features = torch.tensor([[feature1, feature2, feature3, feature4, feature5, feature6]], dtype=torch.float32)
    output = model(features)
    prediction = output.round().item()
    return "Kidney Stone Detected" if prediction == 1 else "No Stone Detected"
light_blue = "#ADD8E6"
# Create a Gradio interface
inputs = [
    gr.Slider(minimum=0.8, maximum=1.5, label="gravity: Specific Gravity"),  # Using gr.Slider for each feature
    gr.Slider(minimum=3, maximum=8, label="ph: pH (Potential of Hydrogen)"),
    gr.Slider(minimum=200, maximum=1200, label="osmo: Osmolality"),
    gr.Slider(minimum=5, maximum=30, label="cond: Conductivity"),
    gr.Slider(minimum=50, maximum=700, label="urea: Urea"),
    gr.Slider(minimum=0, maximum=20, label="calc: Calcium")
]

output = gr.Label()  # Output label for the prediction

interface = gr.Interface(predict, inputs, output, title="Kidney Stone Detection NOTE- FOR RESEARCH PURPOSE ONLY-",
                         description="Enter the values for each feature",
                         css=f".gradio-container {{ background-color: {light_blue} }}"  # Inline CSS injection
                         )  # Customize interface details

interface.launch()  # Launch the Gradio interface