Update app.py

app.py

@@ -1,13 +1,42 @@
 import gradio as gr
 import joblib
+from fastapi import FastAPI
+import joblib
+import numpy as np
+from pydantic import BaseModel
+import propy
+from sklearn.preprocessing import MinMaxScaler
+
+# Load trained SVM model
+model = joblib.load("SVM.joblib")
 
-# Load your trained model
-model = joblib.load("SVM.joblib")  # Ensure this file is in the correct directory
+# Define request model
+class SequenceInput(BaseModel):
+    sequence: str
 
-def predict(sequence):
-    prediction = model.predict([len(sequence)])  # Adjust based on your features
-    return "AMP" if prediction[0] else "Non-AMP"
+def extract_features(sequence):
+    """Calculate AAC, Dipeptide Composition and normalize features."""
+    # Calculate Amino Acid Composition (AAC)
+    aac = propy.AAComposition.CalculateAAC(sequence)
+    
+    # Calculate Dipeptide Composition
+    dipeptide_comp = propy.AAComposition.CalculateAADipeptideComposition(sequence)
+    
+    # Combine both features (AAC and Dipeptide Composition)
+    features = np.concatenate((aac, dipeptide_comp))
+    
+    # Min-Max Normalization
+    scaler = MinMaxScaler()
+    normalized_features = scaler.fit_transform(features.reshape(-1, 1)).flatten()
+    
+    return normalized_features
 
-iface = gr.Interface(fn=predict, inputs="text", outputs="text")
+@app.post("/predict/")
+def predict(sequence_input: SequenceInput):
+    """Predict AMP vs Non-AMP"""
+    sequence = sequence_input.sequence
+    features = extract_features(sequence)
+    prediction = model.predict([features])[0]
+    
+    return {"sequence": sequence, "prediction": "AMP" if prediction == 1 else "Non-AMP"}
 
-iface.launch()