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61d090f a42fb24 61d090f a42fb24 61d090f a42fb24 61d090f a42fb24 61d090f ebf07e0 61d090f ebf07e0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | import torch
import gradio as gr
from transformers import (
AutoTokenizer,
AutoModelForMultipleChoice
)
# -------------------------
# Device
# -------------------------
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# -------------------------
# Load tokenizer
# -------------------------
MODEL_NAME = "rohitk123/roberta-base-mcq-solver"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
# -------------------------
# Load model
# -------------------------
model = AutoModelForMultipleChoice.from_pretrained(
MODEL_NAME
)
model.to(device)
model.eval()
# -------------------------
# Prediction Function
# -------------------------
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
probs = torch.softmax(logits, dim=1)[0]
letters = ["A", "B", "C", "D", "E"]
pred = torch.argmax(probs).item()
answer = letters[pred]
result = f"Predicted Answer: {answer}\n\n"
result += "Scores\n\n"
for letter, score in zip(letters, probs):
result += f"{letter} : {score.item():.4f}\n"
return result
# -------------------------
# Gradio Interface
# -------------------------
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="RoBERTa-Base 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(server_name="0.0.0.0", server_port=7860) |