Update app.py

app.py

@@ -1,36 +1,38 @@
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
+import gradio as gr
 import joblib
 import numpy as np
 from propy import AAComposition
 from sklearn.preprocessing import MinMaxScaler
 
-# Initialize FastAPI app
-app = FastAPI()
-
-# Load trained SVM model and scaler
+# Load trained SVM model and scaler (Ensure both files exist in the Space)
 model = joblib.load("SVM.joblib")
 scaler = MinMaxScaler()
 
-# Define request schema
-class SequenceInput(BaseModel):
-    sequence: str
+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
 
-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)}")
 
-@app.post("/predict/")
-def predict(input_data: SequenceInput):
-    """Predict AMP vs Non-AMP from protein sequence."""
-    features = extract_features(input_data.sequence)
+def predict(sequence):
+    """Predict AMP vs Non-AMP"""
+    features = extract_features(sequence)
     prediction = model.predict(features)[0]
-    result = "AMP" if prediction == 1 else "Non-AMP"
-    return {"prediction": result}
+    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."
+)
 
-# Run using: uvicorn script_name:app --host 0.0.0.0 --port 8000 --reload
+# Launch app
+iface.launch(share=True)