Update app.py

app.py

@@ -1,38 +1,36 @@
-import gradio as gr
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
 import joblib
 import numpy as np
 from propy import AAComposition
 from sklearn.preprocessing import MinMaxScaler
 
-# Load trained SVM model and scaler (Ensure both files exist in the Space)
+# Initialize FastAPI app
+app = FastAPI()
+
+# Load trained SVM model and scaler
 model = joblib.load("SVM.joblib")
 scaler = MinMaxScaler()
 
-def extract_features(sequence):
-    """Calculate AAC, Dipeptide Composition, and normalize features."""
-    # Calculate Amino Acid Composition (AAC) and convert to array
-    aac = np.array(list(AAComposition.CalculateAADipeptideComposition(sequence)), dtype=float)
-    
-    # Normalize using the pre-trained scaler (Ensure the scaler is loaded correctly)
-    normalized_features = scaler.fit_transform([aac])  # Don't use fit_transform(), only transform()
-
-    return normalized_features
+# Define request schema
+class SequenceInput(BaseModel):
+    sequence: str
 
+def extract_features(sequence: str):
+    """Extract Amino Acid Composition (AAC) features and normalize them."""
+    try:
+        aac = np.array(list(AAComposition.CalculateAADipeptideComposition(sequence)), dtype=float)
+        normalized_features = scaler.fit_transform([aac])  # Don't use fit_transform(), only transform()
+        return normalized_features
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Feature extraction failed: {str(e)}")
 
-def predict(sequence):
-    """Predict AMP vs Non-AMP"""
-    features = extract_features(sequence)
+@app.post("/predict/")
+def predict(input_data: SequenceInput):
+    """Predict AMP vs Non-AMP from protein sequence."""
+    features = extract_features(input_data.sequence)
     prediction = model.predict(features)[0]
-    return "AMP" if prediction == 1 else "Non-AMP"
-
-# Create Gradio interface
-iface = gr.Interface(
-    fn=predict,
-    inputs=gr.Textbox(label="Enter Protein Sequence"),
-    outputs=gr.Label(label="Prediction"),
-    title="AMP Classifier",
-    description="Enter an amino acid sequence to predict whether it's an antimicrobial peptide (AMP) or not."
-)
+    result = "AMP" if prediction == 1 else "Non-AMP"
+    return {"prediction": result}
 
-# Launch app
-iface.launch(share=True)
+# Run using: uvicorn script_name:app --host 0.0.0.0 --port 8000 --reload