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Update app.py
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app.py
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import gradio as gr
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salutation = "Good morning" if is_morning else "Good evening"
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greeting = f"{salutation} {name}. It is {temperature} degrees today"
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celsius = (temperature - 32) * 5 / 9
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return greeting, round(celsius, 2)
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import pandas as pd
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import numpy as np
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import gradio as gr
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import torch
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from transformers import AutoModelForMultipleChoice, AutoTokenizer
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model_id = "deepset/deberta-v3-large-squad2"
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# Load the model and tokenizer
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model = AutoModelForMultipleChoice.from_pretrained(model_id)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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# Define the preprocessing function
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def preprocess(sample):
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first_sentences = [sample["prompt"]] * 5
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second_sentences = [sample[option] for option in "ABCDE"]
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tokenized_sentences = tokenizer(first_sentences, second_sentences, truncation=True, padding=True, return_tensors="pt")
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sample["input_ids"] = tokenized_sentences["input_ids"]
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sample["attention_mask"] = tokenized_sentences["attention_mask"]
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return sample
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# Define the prediction function
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def predict(data):
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inputs = torch.stack(data["input_ids"])
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masks = torch.stack(data["attention_mask"])
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with torch.no_grad():
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logits = model(inputs, attention_mask=masks).logits
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predictions_as_ids = torch.argsort(-logits, dim=1)
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answers = np.array(list("ABCDE"))[predictions_as_ids.tolist()]
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return ["".join(i) for i in answers[:, :3]]
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text=gr.Textbox(placeholder="paste multiple choice questions.....")
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label=gr.Label(num_top_classes=3)
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# Create the Gradio interface
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iface = gr.Interface(
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fn=predict,
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inputs=text # Use the correct class with type="json"
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outputs=label,
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live=True,
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examples=[
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{"prompt": "This is the prompt", "A": "Option A text", "B": "Option B text", "C": "Option C text", "D": "Option D text", "E": "Option E text"}
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],
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title="LLM Science Exam Demo",
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description="Enter the prompt and options (A to E) below and get predictions.",
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# Run the interface
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iface.launch()
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