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# app.py (Final, Robust Version)
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import pickle
from huggingface_hub import hf_hub_download
# =============================================================================
# 1. LOAD MODEL, TOKENIZER, AND LABEL ENCODER
# =============================================================================
# Define the path to your model repository
model_path = "Tarive/esm2_t12_35M_UR50D-5k-families-balanced-augmented-weighted_optimized"
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(model_path)
print("Loading model...")
model = AutoModelForSequenceClassification.from_pretrained(model_path)
# Move model to GPU if available for faster inference
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
print(f"Model loaded on device: {device}")
# Download and load the label encoder
print("Downloading and loading label encoder...")
encoder_path = hf_hub_download(repo_id=model_path, filename="label_encoder_5k-2.pkl")
with open(encoder_path, "rb") as f:
label_encoder = pickle.load(f)
print("Label encoder loaded.")
# =============================================================================
# 2. DEFINE THE LOW-LEVEL PREDICTION FUNCTION
# =============================================================================
# This function manually replicates the training data processing steps.
def predict_family(sequence):
# 1. Tokenize the input sequence with the exact same settings as training
inputs = tokenizer(
sequence,
return_tensors="pt", # Return PyTorch tensors
truncation=True,
padding=True,
max_length=256 # Ensure this matches your training max_length
).to(device) # Move tokenized inputs to the same device as the model
# 2. Get model predictions (logits)
with torch.no_grad(): # Disable gradient calculation for efficiency
logits = model(**inputs).logits
# 3. Get the top 5 predictions
top_k_indices = torch.topk(logits, 5, dim=-1).indices.squeeze().tolist()
# 4. Convert logits to probabilities (softmax)
probabilities = torch.nn.functional.softmax(logits, dim=-1).squeeze().tolist()
# 5. Decode the numerical labels back to family names
results = {}
for index in top_k_indices:
family_name = label_encoder.inverse_transform([index])[0]
confidence_score = probabilities[index]
results[family_name] = confidence_score
return results
# =============================================================================
# 3. CREATE THE GRADIO INTERFACE (No changes here)
# =============================================================================
print("Creating Gradio interface...")
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=[
["LAAARMRPQDIDRFVPHQANARIFDAVGRNLGIADEAIVKTIAEYGNSSAATIPLSLSLAHRAAPFRPGEKVLLAAAGAGLSGGALVVGI"],
["MSLPDMRLPIQNAIFYPEMVNYTFNRLDLTSISCLTFEKPKRDLFRAIDVCEWVASMGNPYVSVLLGADDKAVELFLEGKIGFLDIPVLIESVLSSVNFHIEENLEDILRAV"],
["VSYISSQYPHHPDVFSVVRQACVRSLSCEVCPGREGPIFFGDEHRSHVFSHTFFLKDSQARGFQRWYSIVMVMMDKVFLLNSWPFLVKQIRNFIDQLQAKANKVYFSEQTDCPQRALRLKSSFTMTPANFRRQRSNISVRGLYELTNDKQVFYTAHVWFTWILKAC"]
],
allow_flagging="never"
)
# Launch the interface!
print("Launching app...")
iface.launch() |