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import gradio as gr
import spaces
from transformers import (
AutoTokenizer,
AutoModelForMultipleChoice
)
# -------------------------
# Model
# -------------------------
MODEL_PATH = "./best_model_fold_1"
# -------------------------
# Device
# -------------------------
device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu"
)
print("Device:", device)
# -------------------------
# Load tokenizer
# -------------------------
tokenizer = AutoTokenizer.from_pretrained(
MODEL_PATH
)
print("Tokenizer loaded successfully!")
# -------------------------
# Load model
# -------------------------
model = AutoModelForMultipleChoice.from_pretrained(
MODEL_PATH
)
model.to(device)
model.eval()
print("Model loaded successfully!")
# -------------------------
# Prediction
# -------------------------
@spaces.GPU(duration=30)
def predict(question, A, B, C, D, E):
choices = [A, B, C, D, E]
encoding = tokenizer(
[question] * 5,
choices,
max_length=256,
truncation=True,
padding="max_length",
return_tensors="pt"
)
input_ids = encoding["input_ids"].unsqueeze(0).to(device)
attention_mask = encoding[
"attention_mask"
].unsqueeze(0).to(device)
with torch.no_grad():
outputs = model(
input_ids=input_ids,
attention_mask=attention_mask
)
logits = outputs.logits
probabilities = torch.softmax(
logits,
dim=1
)[0]
letters = ["A", "B", "C", "D", "E"]
prediction = torch.argmax(
probabilities
).item()
answer = letters[prediction]
result = (
f"Predicted Answer: {answer}\n\n"
"Scores\n\n"
)
for letter, score in zip(
letters,
probabilities
):
result += (
f"{letter}: "
f"{score.item():.4f}\n"
)
return result
# -------------------------
# Gradio
# -------------------------
demo = gr.Interface(
fn=predict,
inputs=[
gr.Textbox(
lines=4,
label="Question"
),
gr.Textbox(
label="Option A"
),
gr.Textbox(
label="Option B"
),
gr.Textbox(
label="Option C"
),
gr.Textbox(
label="Option D"
),
gr.Textbox(
label="Option E"
),
],
outputs=gr.Textbox(
label="Prediction"
),
title="DeBERTa MCQ Solver",
description=(
"Enter a question and five options."
),
examples=[
[
"Which planet is known as the Red Planet?",
"Earth",
"Mars",
"Venus",
"Jupiter",
"Saturn"
]
]
)
if __name__ == "__main__":
demo.launch() |