Update app.py

app.py

@@ -1,18 +1,15 @@
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
+import gradio as gr
 import joblib
 import numpy as np
 import pandas as pd
 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 = joblib.load("norm.joblib")
 
+
 # List of features used in your model
 selected_features = [
     "A", "R", "N", "D", "C", "E", "Q", "G", "H", "I", "L", "K", "M", "F", "P", "S", "T", "W", "Y", "V",
@@ -38,48 +35,51 @@ selected_features = [
     "VA", "VR", "VD", "VC", "VE", "VQ", "VG", "VI", "VL", "VK", "VP", "VS", "VT", "VY", "VV"
 ]
 
-# Define request model
-class SequenceRequest(BaseModel):
-    sequence: str
-
-# Feature extraction function
 def extract_features(sequence):
     """Extract only the required features and normalize them."""
     # Compute all possible features
-    all_features = AAComposition.CalculateAADipeptideComposition(sequence)
+    all_features = AAComposition.CalculateAADipeptideComposition(sequence)  # Amino Acid Composition
     # Extract the values from the dictionary
-    feature_values = list(all_features.values())
+    feature_values = list(all_features.values())  # Extract values only
     # Convert to NumPy array for normalization
     feature_array = np.array(feature_values).reshape(-1, 1)
-    feature_array = feature_array[:420]
+    feature_array = feature_array[: 420]
     # Min-Max Normalization
     normalized_features = scaler.transform(feature_array.T)
+
     # Reshape normalized_features back to a single dimension
-    normalized_features = normalized_features.flatten()
+    normalized_features = normalized_features.flatten()  # Flatten array
+
     # Create a dictionary with selected features
     selected_feature_dict = {feature: normalized_features[i] for i, feature in enumerate(selected_features)
                                if feature in all_features}
+
     # Convert dictionary to dataframe
     selected_feature_df = pd.DataFrame([selected_feature_dict])
+
     # Convert dataframe to numpy array
     selected_feature_array = selected_feature_df.T.to_numpy()
+
     return selected_feature_array
 
-# Prediction endpoint
-@app.post("/predict")
-def predict(request: SequenceRequest):
+
+
+def predict(sequence):
     """Predict AMP vs Non-AMP"""
-    try:
-        # Extract features
-        features = extract_features(request.sequence)
-        # Make prediction
-        prediction = model.predict(features.T)[0]
-        # Return the result
-        return {"prediction": "AMP" if prediction == 0 else "Non-AMP"}
-    except Exception as e:
-        raise HTTPException(status_code=400, detail=str(e))
+    features = extract_features(sequence)
+    prediction = model.predict(features.T)[0]
+    return "AMP" if prediction == 0 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(share=True)
 
-# Root endpoint for health check
-@app.get("/")
-def read_root():
-    return {"status": "OK"}
+I neeed to use the same code for fastapi