Create app.py

app.py

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+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()