Update app.py

app.py

@@ -32,53 +32,7 @@ def predict(data):
 # Create the Gradio interface
 iface = gr.Interface(
     fn=predict,
-    inputs=gr.inputs.JSON(),
-    outputs=gr.outputs.Label(num_top_classes=3),
-    live=True,
-    examples=[
-        {"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"}
-    ],
-    title="LLM Science Exam Demo",
-    description="Enter the prompt and options (A to E) below and get predictions.",
-)
-
-# Run the interface
-iface.launch()
-import pandas as pd
-import numpy as np
-import gradio as gr
-import torch
-from transformers import AutoModelForMultipleChoice, AutoTokenizer
-from huggingface_hub import hf_hub_url, Repository
-
-# Load the model and tokenizer
-model_path = "my_model"
-model = AutoModelForMultipleChoice.from_pretrained(model_path)
-tokenizer = AutoTokenizer.from_pretrained(model_path)
-
-# Define the preprocessing function
-def preprocess(sample):
-    first_sentences = [sample["prompt"]] * 5
-    second_sentences = [sample[option] for option in "ABCDE"]
-    tokenized_sentences = tokenizer(first_sentences, second_sentences, truncation=True, padding=True, return_tensors="pt")
-    sample["input_ids"] = tokenized_sentences["input_ids"]
-    sample["attention_mask"] = tokenized_sentences["attention_mask"]
-    return sample
-
-# Define the prediction function
-def predict(data):
-    inputs = torch.stack(data["input_ids"])
-    masks = torch.stack(data["attention_mask"])
-    with torch.no_grad():
-        logits = model(inputs, attention_mask=masks).logits
-    predictions_as_ids = torch.argsort(-logits, dim=1)
-    answers = np.array(list("ABCDE"))[predictions_as_ids.tolist()]
-    return ["".join(i) for i in answers[:, :3]]
-
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=predict,
-    inputs=gr.inputs.JSON(),
+    inputs=gr.inputs.Input(type="json"),
     outputs=gr.outputs.Label(num_top_classes=3),
     live=True,
     examples=[