Update app.py

app.py

@@ -1,14 +1,47 @@
+import pandas as pd
+import numpy as np
 import gradio as gr
+import torch
+from transformers import AutoModelForMultipleChoice, AutoTokenizer
 
-def greet(name, is_morning, temperature):
-    salutation = "Good morning" if is_morning else "Good evening"
-    greeting = f"{salutation} {name}. It is {temperature} degrees today"
-    celsius = (temperature - 32) * 5 / 9
-    return greeting, round(celsius, 2)
+model_id = "deepset/deberta-v3-large-squad2"
 
-demo = gr.Interface(
-    fn=greet,
-    inputs=["text", "checkbox", gr.Slider(0, 100)],
-    outputs=["text", "number"],
+# Load the model and tokenizer
+model = AutoModelForMultipleChoice.from_pretrained(model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# 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]]
+text=gr.Textbox(placeholder="paste multiple choice questions.....")
+label=gr.Label(num_top_classes=3)
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=text  # Use the correct class with type="json"
+    outputs=label,
+    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.",
 )
-demo.launch()
+
+# Run the interface
+iface.launch()