Create app.py

app.py

@@ -0,0 +1,49 @@
+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, three_month_sphere, three_month_cylinder, three_month_axis):
+    input_data = torch.tensor([[pre_op_sphere, pre_op_cylinder, pre_op_axis, three_month_sphere, three_month_cylinder, three_month_axis]], dtype=torch.float32)
+    
+    with torch.no_grad():
+        predicted_prescription = model(input_data)
+    
+    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}"
+
+inputs = [
+    gr.inputs.Number(label="Pre-Op Sphere"),
+    gr.inputs.Number(label="Pre-Op Cylinder"),
+    gr.inputs.Number(label="Pre-Op Axis"),
+    gr.inputs.Number(label="3-Month Sphere"),
+    gr.inputs.Number(label="3-Month Cylinder"),
+    gr.inputs.Number(label="3-Month Axis")
+]
+output = gr.outputs.Textbox(label="Predicted Laser Prescription")
+
+gr.Interface(fn=predict, inputs=inputs, outputs=output, title="Laser Prescription Prediction").launch()