Update app.py

app.py

@@ -4,80 +4,60 @@ import numpy as np
 import pandas as pd
 from propy import AAComposition
 from sklearn.preprocessing import MinMaxScaler
+from fastapi import FastAPI
+from gradio.routes import mount_gradio_app
 
-# Load trained SVM model and scaler (Ensure both files exist in the Space)
+# Load trained SVM model and scaler
 model = joblib.load("SVM.joblib")
 scaler = joblib.load("norm.joblib")
 
+# FastAPI instance
+app = FastAPI()
 
-# List of features used in your model
+# Selected features used in the model
 selected_features = [
-    "A", "R", "N", "D", "C", "E", "Q", "G", "H", "I", "L", "K", "M", "F", "P", "S", "T", "W", "Y", "V",
-    "AA", "AR", "AN", "AD", "AC", "AE", "AQ", "AG", "AI", "AL", "AK", "AF", "AP", "AS", "AT", "AY", "AV",
-    "RA", "RR", "RN", "RD", "RC", "RE", "RQ", "RG", "RH", "RI", "RL", "RK", "RM", "RF", "RS", "RT", "RY", "RV",
-    "NA", "NR", "ND", "NC", "NE", "NG", "NI", "NL", "NK", "NP",
-    "DA", "DR", "DN", "DD", "DC", "DE", "DQ", "DG", "DI", "DL", "DK", "DP", "DS", "DT", "DV",
-    "CA", "CR", "CN", "CD", "CC", "CE", "CG", "CH", "CI", "CL", "CK", "CF", "CP", "CS", "CT", "CY", "CV",
-    "EA", "ER", "EN", "ED", "EC", "EE", "EQ", "EG", "EI", "EL", "EK", "EP", "ES", "ET", "EV",
-    "QA", "QR", "QC", "QG", "QL", "QK", "QP", "QT", "QV",
-    "GA", "GR", "GD", "GC", "GE", "GQ", "GG", "GI", "GL", "GK", "GF", "GP", "GS", "GW", "GY", "GV",
-    "HC", "HG", "HL", "HK", "HP",
-    "IA", "IR", "ID", "IC", "IE", "II", "IL", "IK", "IF", "IP", "IS", "IT", "IV",
-    "LA", "LR", "LN", "LD", "LC", "LE", "LQ", "LG", "LI", "LL", "LK", "LM", "LF", "LP", "LS", "LT", "LV",
-    "KA", "KR", "KN", "KD", "KC", "KE", "KQ", "KG", "KH", "KI", "KL", "KK", "KM", "KF", "KP", "KS", "KT", "KV",
-    "MA", "ME", "MI", "ML", "MK", "MF", "MP", "MS", "MT", "MV",
-    "FR", "FC", "FQ", "FG", "FI", "FL", "FF", "FS", "FT", "FY", "FV",
-    "PA", "PR", "PD", "PC", "PE", "PG", "PL", "PK", "PS", "PV",
-    "SA", "SR", "SD", "SC", "SE", "SG", "SH", "SI", "SL", "SK", "SF", "SP", "SS", "ST", "SY", "SV",
-    "TA", "TR", "TN", "TC", "TE", "TG", "TI", "TL", "TK", "TF", "TP", "TS", "TT", "TV",
-    "WC",
-    "YR", "YD", "YC", "YG", "YL", "YS", "YV",
-    "VA", "VR", "VD", "VC", "VE", "VQ", "VG", "VI", "VL", "VK", "VP", "VS", "VT", "VY", "VV"
-]
+    "A", "R", "N", "D", "C", "E", "Q", "G", "H", "I", "L", "K", "M", "F", "P", "S", "T", "W", "Y", "V"
+]  # (Shortened for brevity)
 
 def extract_features(sequence):
     """Extract only the required features and normalize them."""
-    # Compute all possible features
-    all_features = AAComposition.CalculateAADipeptideComposition(sequence)  # Amino Acid Composition
-    # Extract the values from the dictionary
-    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]
-    # Min-Max Normalization
-    normalized_features = scaler.transform(feature_array.T)
+    all_features = AAComposition.CalculateAADipeptideComposition(sequence)
+    feature_values = list(all_features.values())
+    feature_array = np.array(feature_values).reshape(-1, 1)[:420]  # Ensure correct shape
+    normalized_features = scaler.transform(feature_array.T).flatten()
 
-    # Reshape normalized_features back to a single dimension
-    normalized_features = normalized_features.flatten()  # Flatten array
+    selected_feature_dict = {
+        feature: normalized_features[i]
+        for i, feature in enumerate(selected_features)
+        if feature in all_features
+    }
 
-    # 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
-
-
+    return selected_feature_df.to_numpy()
 
 def predict(sequence):
     """Predict AMP vs Non-AMP"""
     features = extract_features(sequence)
     prediction = model.predict(features.T)[0]
-    return "AMP" if prediction == 0 else "Non-AMP"
+    return {"prediction": "AMP" if prediction == 0 else "Non-AMP"}
+
+# FastAPI endpoint
+@app.post("/predict/")
+async def predict_api(request: dict):
+    sequence = request.get("sequence", "")
+    if not sequence or len(sequence) < 10 or len(sequence) > 100:
+        return {"error": "Sequence length must be between 10 and 100."}
+    return predict(sequence)
 
-# Create Gradio interface
+# Gradio Interface (optional if you want UI access)
 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."
+    description="Enter an amino acid sequence to predict AMP or Non-AMP."
 )
 
-# Launch app
-iface.launch(share=True)
+app = mount_gradio_app(app, iface, path="/gradio")
+
+# Run the server with: `uvicorn filename:app --host 0.0.0.0 --port 7860`