app.py

import gradio as gr
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModelForMultipleChoice

# 1. Load fine-tuned model and tokenizer from Hugging Face Hub
MODEL_ID = "udaypratap/smart-mcq-solver"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForMultipleChoice.from_pretrained(MODEL_ID)
model.eval()

# 2. Define prediction function
def predict_mcq(prompt, option_a, option_b, option_c, option_d, option_e):
    options = [option_a, option_b, option_c, option_d, option_e]
    labels = ["A", "B", "C", "D", "E"]
    
    # Format inputs for AutoModelForMultipleChoice
    first_sentences = [prompt] * 5
    second_sentences = options
    
    inputs = tokenizer(
        first_sentences,
        second_sentences,
        truncation=True,
        padding=True,
        max_length=256,
        return_tensors="pt"
    )
    
    # Reshape input tensors for multiple choice model: (batch_size=1, num_choices=5, seq_len)
    input_ids = inputs["input_ids"].unsqueeze(0)
    attention_mask = inputs["attention_mask"].unsqueeze(0)
    
    with torch.no_grad():
        outputs = model(input_ids=input_ids, attention_mask=attention_mask)
        logits = outputs.logits.squeeze(0)
        probs = F.softmax(logits, dim=-1)
        
    # Get top 3 predicted choices
    top3_indices = torch.topk(probs, k=3).indices.tolist()
    top3_choices = [f"{labels[idx]} ({probs[idx].item():.2%})" for idx in top3_indices]
    
    return f"Top 3 Predicted Answers: {', '.join(top3_choices)}"

# 3. Create Gradio Interface
demo = gr.Interface(
    fn=predict_mcq,
    inputs=[
        gr.Textbox(label="Question Prompt", placeholder="Enter your question here..."),
        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="Predictions"),
    title="Smart MCQ Solver",
    description="Enter a question prompt along with 5 options to get the top predicted answers."
)

if __name__ == "__main__":
    demo.launch()