Utils and such

app.py

@@ -1,4 +1,20 @@
 import streamlit as st
+from utils import validate_sequence, predict
+from model import model
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+def main():
+    st.title("AminoAnalytica - ML Experience")
+    
+    # User input
+    sequence = st.text_input("Enter your amino acid sequence:")
+
+    if st.button("Analyze Sequence"):
+        if validate_sequence(sequence):
+            prediction = predict(model, sequence)
+            st.write("### Results")
+            st.table({"Property": ["Solubility"], "Model Output": [prediction]})
+        else:
+            st.error("Invalid sequence. Please enter a valid amino acid sequence of up to 200 characters.")
+
+if __name__ == "__main__":
+    main()

model.py

@@ -0,0 +1,5 @@
+# This file could be used for more complex model handling if needed.
+# For simplicity, it's not used extensively in this basic example.
+from utils import load_model
+
+model = load_model()

requirements.txt

@@ -0,0 +1,2 @@
+streamlit
+torch

utils.py

@@ -0,0 +1,17 @@
+import torch
+
+def validate_sequence(sequence):
+    valid_amino_acids = set("ACDEFGHIKLMNPQRSTVWY")  # 20 standard amino acids
+    return all(aa in valid_amino_acids for aa in sequence) and len(sequence) <= 200
+
+def load_model():
+    # Assuming the model is a simple PyTorch model, adjust the path as needed
+    model = torch.load('model.pth')
+    model.eval()
+    return model
+
+def predict(model, sequence):
+    # Dummy tensor conversion, replace with your actual model's input handling
+    tensor = torch.tensor([ord(char) for char in sequence], dtype=torch.float32)
+    output = model(tensor)
+    return output.item()