app.py

# app.py (Updated Version)

import gradio as gr
from transformers import pipeline
import pickle

# =============================================================================
# 1. LOAD YOUR MODEL AND THE SAVED LABEL ENCODER
# =============================================================================
# Define the path to your model repository
model_path = "Tarive/esm2_t12_35M_UR50D-finetuned-pfam-1k" # Make sure this is correct

# Load the classification pipeline
classifier = pipeline("text-classification", model=model_path)

# Load the label encoder from the file you uploaded
with open("label_encoder.pkl", "rb") as f:
    label_encoder = pickle.load(f)


# =============================================================================
# 2. DEFINE THE PREDICTION FUNCTION WITH LABEL DECODING
# =============================================================================
# This function now decodes the labels before displaying them.
def predict_family(sequence):
    # Get the top 5 predictions from the model
    predictions = classifier(sequence, top_k=5)
    
    # The model outputs labels like "LABEL_455". We need to extract the number.
    results = {}
    for p in predictions:
        # Extract the number from the label string (e.g., "LABEL_455" -> 455)
        label_index = int(p['label'].split('_')[1])
        
        # Use the label_encoder to find the original family name
        original_label = label_encoder.inverse_transform([label_index])[0]
        
        # Store the real name and score
        results[original_label] = p['score']
    
    return results

# =============================================================================
# 3. CREATE THE GRADIO INTERFACE (No changes here)
# =============================================================================
iface = gr.Interface(
    fn=predict_family,
    inputs=gr.Textbox(
        lines=10, 
        label="Protein Amino Acid Sequence", 
        placeholder="Paste your protein sequence here..."
    ),
    outputs=gr.Label(
        num_top_classes=5,
        label="Predicted Families"
    ),
    title="Protein Family Classifier",
    description="This demo uses a fine-tuned ESM-2 model to predict the protein family from its amino acid sequence. Enter a sequence to see the top 5 predictions and their confidence scores.",
    examples=[
        ["MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPAVHASLDKFLASVSTVLTSKYR"],
        ["MTEYKLVVVGAGDVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVEVDCQQCMILDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHQYREQIKRVKDSDDVPMVLVGNKCDLAARTVESRQAQDLARSYGIPYIETSAKTRQGVEDAFYTLVREIRQHKLRKLNPPDESGGCMS"]
    ],
    allow_flagging="never"
)

# Launch the interface!
iface.launch()