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app.py.txt

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+import gradio as gr
+import numpy as np
+from tensorflow.keras.models import load_model
+
+# Load model
+model = load_model("ldh_predictor.h5")
+
+# Encoding function
+aa_to_int = {aa:i+1 for i, aa in enumerate('ACDEFGHIKLMNPQRSTVWY')}
+max_length = 500
+
+def predict_ldh(sequence):
+    encoded = [aa_to_int.get(aa, 0) for aa in sequence[:max_length]]
+    if len(encoded) < max_length:
+        encoded += [0] * (max_length - len(encoded))
+    prob = model.predict(np.array([encoded]))[0][0]
+    return {"LDH": float(prob), "Not LDH": float(1-prob)}
+
+# Gradio interface
+interface = gr.Interface(
+    fn=predict_ldh,
+    inputs=gr.Textbox(lines=5, placeholder="Paste protein sequence...", label="Protein Sequence"),
+    outputs=gr.Label(label="Prediction"),
+    title="🧬 DeepEnzyme: LDH Classifier",
+    examples=[
+        ["MATLKDQLIYNLLKEEQTPQNKITVVGVGAVGMACAISILMKDLADELALVDVIEDKLKGEMMDLQHGSLFLRTPKIVSGKDYNVTANSKLVIITAGARQQEGESRLNLVQRNVNIFKFIIPNVVKYSPNCKLLIVSNPVDILTYVAWKISGFPKNRVIGSGCNLDSARFRYLMGERLGVHPLSCHGWVLGEHGDSSVPVWSGMNVAGVSLKTLHPDLGTDKDKEQWKEVHKQVVESAYEVIKLKGYTSWAIGLSVADLAESIMKNLRRVHPVSTMIKGLYGIKDDVFLSVPCILGQNGISDLVKVTLTSEEEARLKKSADTLWGIQKELQF"],
+        ["MSEPIRVLVTGAAGQIAYSLLYSIGNGSVFGKDQPIILVLLDITPMMGVLDGVLMELQDCALPLLKDVIATDKEDVAFKDLDVAILVGSMPRREGMERKDLLKANVKIFKSQGAALDKYAKKSVKVIVVGNPANTNCLTASKSAPSIPKENFSCLTRLDHNRAKAQIALKLGVTANDVKNVIIWGNHSSTQYPDVNHAKVKLQGKEVGVYEALKDDSWLKGEFVTTVQQRGAAVIKARKLSSAMSAAKAICDHVRDIWFGTPEGEFVSMGVISDGNSYGVPDDLLYSFPVVIKNKTWKFVEGLPINDFSREKMDLTAKELTEEKESAFEFLSSA"]
+    ],
+    description="Predict whether a protein is Lactate Dehydrogenase (LDH) using deep learning."
+)
+
+interface.launch()

requirements.txt

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+tensorflow
+gradio
+numpy