Update app.py

app.py

@@ -1,44 +1,52 @@
+import streamlit as st
 import torch
-from transformers import AutoModelForSequenceClassification
-
-# Update this variable with your model name from Hugging Face Hub
-MODEL_NAME = "model"
-
-# Load the model (no tokenizer needed)
-model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
-
-# Function to make predictions (replace with your actual prediction logic)
-def predict(text):
+import torch.nn as nn
+
+# Define your model architecture here (same as before)
+class RegressionModel2(nn.Module):
+    def __init__(self, input_dim2, hidden_dim2, output_dim2):
+        super(RegressionModel2, self).__init__()
+        self.fc1 = nn.Linear(input_dim2, hidden_dim2)
+        self.relu1 = nn.ReLU()  # ReLU activation function
+        self.fc2 = nn.Linear(hidden_dim2, output_dim2)
+        self.batch_norm1 = nn.BatchNorm1d(hidden_dim2)  # Batch normalization
+
+    def forward(self, x2):
+        out = self.fc1(x2)
+        out = self.relu1(out)
+        out = self.batch_norm1(out)
+        out = self.fc2(out)
+        return out
+
+# Load your saved model state dictionary (assuming 'model.pt' is uploaded)
+model2 = RegressionModel2(input_dim2, hidden_dim2, output_dim2)
+model2.load_state_dict(torch.load('model.pt'))
+model2.eval()  # Set the model to evaluation mode
+
+def predict(age, aca, axis):
   """
-  This function takes a text input, preprocesses it using the tokenizer,
-  makes a prediction using the loaded model, and returns the predicted output.
-  **Replace this function with your actual prediction logic.**
+  This function takes three arguments (age, axis, aca) as input,
+  prepares the data, makes a prediction using the loaded model,
+  and returns the predicted value.
   """
-  # Modify this part to handle input processing if your model requires it
-  # (assuming your model doesn't need a tokenizer in this example)
-  inputs = text  # Replace with preprocessing steps if necessary
+  # Prepare the input data
+  data = torch.tensor([[age, aca, axis]], dtype=torch.float32)
 
+  # Make prediction
   with torch.no_grad():
-    outputs = model(**inputs)
-    predictions = torch.argmax(outputs.logits, dim=-1)
-  return predictions.item()
+    prediction = model2(data)
 
-# Function to handle user input and make predictions (modify for your UI framework)
-def handle_request(data):
-  """
-  This function takes user input data (modify based on your UI framework),
-  extracts the relevant text, and calls the predict function to make a prediction.
-  """
-  text = data["text"]  # Assuming "text" is the key in your data dictionary
-  prediction = predict(text)
-  return {"prediction": prediction}
+  # Return the predicted value
+  return prediction.item()
 
-if __name__ == "__main__":
-  from fastapi import FastAPI
+# Streamlit App
+st.title("Astigmatism Prediction App")
+st.write("Enter the patient's information:")
 
-  app = FastAPI()
+age = st.number_input("Age", min_value=0)
+aca = st.number_input("ACA Magnitude", min_value=0)
+axis = st.number_input("ACA Axis", min_value=0)
 
-  @app.post("/predict")
-  async def predict_from_text(data: dict):
-    response = handle_request(data)
-    return response
+if st.button("Predict"):
+  predicted_value = predict(age, aca, axis)
+  st.write(f"Predicted Astigmatism Value: {predicted_value}")