Update app.py

app.py

@@ -1,20 +1,15 @@
 import gradio as gr
 import joblib
-import joblib
 import numpy as np
-from pydantic import BaseModel
 import propy
 from sklearn.preprocessing import MinMaxScaler
 
-# Load trained SVM model
+# Load trained SVM model and scaler (Ensure both files exist in the Space)
 model = joblib.load("SVM.joblib")
-
-# Define request model
-class SequenceInput(BaseModel):
-    sequence: str
+scaler = MinMaxScaler()
 
 def extract_features(sequence):
-    """Calculate AAC, Dipeptide Composition and normalize features."""
+    """Calculate AAC, Dipeptide Composition, and normalize features."""
     # Calculate Amino Acid Composition (AAC)
     aac = propy.AAComposition.CalculateAAC(sequence)
     
@@ -24,18 +19,25 @@ def extract_features(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()
+    # Normalize with pre-trained scaler (avoid fitting new data)
+    normalized_features = scaler.transform([features])
     
     return normalized_features
 
-@app.post("/predict/")
-def predict(sequence_input: SequenceInput):
+def predict(sequence):
     """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"}
+    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."
+)
 
+# Launch app
+iface.launch()