app.py

import gradio as gr
import pandas as pd
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn

class LaserPredictions(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(LaserPredictions, self).__init__()
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.relu1 = nn.ReLU()
        self.fc2 = nn.Linear(hidden_dim, output_dim)
        self.batch_norm1 = nn.BatchNorm1d(hidden_dim)

    def forward(self, x2):
        out = self.fc1(x2)
        out = self.relu1(out)
        out = self.batch_norm1(out)
        out = self.fc2(out)
        return out

# Load the saved model state dictionary
model = LaserPredictions(6, 32, 3)
model.load_state_dict(torch.load('laser_prescription_model.pt'))
model.eval()  # Set the model to evaluation mode

import gradio as gr
import pandas as pd
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn

class LaserPredictions(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(LaserPredictions, self).__init__()
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.relu1 = nn.ReLU()
        self.fc2 = nn.Linear(hidden_dim, output_dim)
        self.batch_norm1 = nn.BatchNorm1d(hidden_dim)

    def forward(self, x2):
        out = self.fc1(x2)
        out = self.relu1(out)
        out = self.batch_norm1(out)
        out = self.fc2(out)
        return out

# Load the saved model state dictionary
model = LaserPredictions(6, 32, 3)
model.load_state_dict(torch.load('laser_prescription_model.pt'))
model.eval()  # Set the model to evaluation mode

def predict(pre_op_sphere, pre_op_cylinder, pre_op_axis):
    # Use zero values for post-op features, as the target prescription is set to 0
    post_op_values = [0.0, 0.0, 0.0]

    # Combine pre-op and post-op values
    input_data = [pre_op_sphere, pre_op_cylinder, pre_op_axis] + post_op_values
    input_tensor = torch.tensor([input_data], dtype=torch.float32)

    with torch.no_grad():
        predicted_prescription = model(input_tensor)

    predicted_sphere = predicted_prescription[0][0].item()
    predicted_cylinder = predicted_prescription[0][1].item()
    predicted_axis = predicted_prescription[0][2].item()

    return f"Predicted Laser Prescription:\nSphere: {predicted_sphere:.2f}\nCylinder: {predicted_cylinder:.2f}\nAxis: {predicted_axis:.2f}"

css = """
.gradio-container {
    background-color: #131517;
}

button {
  background: #102534;
  border: 1px solid #0c538c;
  outline: none;
  border-radius: 5px;
  padding: 10px 20px;
  font-size: 16px;
  color: #fff;
}


button:hover {
    cursor: pointer;
    opacity: .7;
}

input {
    background-color: #202428;
    color: #fff;
    border: 1px solid #2d353c;
    border-radius: 5px;
    padding: 10px 20px;
    outline: none;
    font-size: 16px;
}

::-webkit-input-placeholder {
    color: #7a848f;
}

input:focus::placeholder {
    color: transparent;
}

#component-0 {
    background-color: #131517;
}

#component-1 {
    background-color: #131517;
}

#component-2 {
    background-color: #131517;
}

#component-3 {
    background-color: #131517;
}

#component-4 {
    background-color: #131517;
}

#component-5 {
    background-color: #131517;
}

#component-6 {
    background-color: #131517;
}

#component-7 {
    background-color: #131517;
}

#component-8 {
    background-color: #131517;
}

#component-9 {
    background-color: #131517;
}

#component-10 {
    background-color: #131517;
}

#component-13 {
    background-color: #131517;
}

textarea {
    background-color: #131517;
    resize: none;
}

footer {
    visibility: hidden;
}
"""

inputs = [
    gr.Number(label="Pre-Op Sphere"),
    gr.Number(label="Pre-Op Cylinder"),
    gr.Number(label="Pre-Op Axis"),
]
output = gr.Textbox(label="Predicted Laser Prescription")

gr.Interface(fn=predict, inputs=inputs, outputs=output, title="Laser Treatment Prediction", css=css).launch(share=True)