Deploy Gradio app

README.md

@@ -1,14 +1,107 @@
 ---
-title: Roberta Mcq Solver
-emoji: 🐨
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 6.22.0
-python_version: '3.12'
-app_file: app.py
-pinned: false
 license: apache-2.0
 ---
+# 🧠 Smart MCQ Solver
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+A Deep Learning based Multiple Choice Question Solver built using **RoBERTa-Base** and Hugging Face Transformers.
+
+---
+
+## Project Overview
+
+This project predicts the correct answer among five options for a multiple-choice question.
+
+The model is fine-tuned using the Hugging Face `AutoModelForMultipleChoice` architecture.
+
+---
+
+## Features
+
+- Fine-tuned RoBERTa-base
+- Multiple Choice Question Answering
+- Gradio Web Interface
+- Hugging Face Transformers
+- PyTorch Implementation
+
+---
+
+## Model
+
+RoBERTa-base
+
+Task:
+
+Multiple Choice Question Answering
+
+---
+
+## Performance
+
+| Metric | Score |
+|---------|-------|
+| MAP@3 | **0.9893** |
+| F1 Score | **0.99** |
+
+Cross Validation
+
+| Fold | MAP@3 |
+|------|--------|
+|1|0.9871|
+|2|0.9854|
+|3|0.9963|
+|4|0.9933|
+|5|0.9846|
+
+Mean MAP@3
+
+0.9893
+
+---
+
+## Installation
+
+```bash
+git clone https://github.com/YOUR_USERNAME/smart-mcq-solver.git
+
+cd smart-mcq-solver
+
+pip install -r requirements.txt
+```
+
+---
+
+## Run
+
+```bash
+python app.py
+```
+
+---
+
+## Screenshots
+
+### Home Page
+
+![Loading](image.png)
+
+### Prediction
+
+![Loading](image-1.png)
+
+---
+
+## Tech Stack
+
+- Python
+- PyTorch
+- Transformers
+- Hugging Face
+- Gradio
+
+---
+
+## Author
+
+Rohit Kumar
+
+IIT Madras BS Degree

app.py

@@ -0,0 +1,107 @@
+import torch
+import gradio as gr
+
+from transformers import (
+    AutoTokenizer,
+    AutoModelForMultipleChoice
+)
+
+# -------------------------
+# Device
+# -------------------------
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# -------------------------
+# Load tokenizer
+# -------------------------
+MODEL_NAME = "rohitk123/roberta-base-mcq-solver"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+# -------------------------
+# Load model
+# -------------------------
+model = AutoModelForMultipleChoice.from_pretrained(
+    MODEL_NAME
+)
+
+model.to(device)
+model.eval()
+
+
+# -------------------------
+# Prediction Function
+# -------------------------
+def predict(question, A, B, C, D, E):
+
+    choices = [A, B, C, D, E]
+
+    encoding = tokenizer(
+        [question] * 5,
+        choices,
+        max_length=256,
+        truncation=True,
+        padding="max_length",
+        return_tensors="pt"
+    )
+
+    input_ids = encoding["input_ids"].unsqueeze(0).to(device)
+    attention_mask = encoding["attention_mask"].unsqueeze(0).to(device)
+
+    with torch.no_grad():
+
+        outputs = model(
+            input_ids=input_ids,
+            attention_mask=attention_mask
+        )
+
+    logits = outputs.logits
+
+    probs = torch.softmax(logits, dim=1)[0]
+
+    letters = ["A", "B", "C", "D", "E"]
+
+    pred = torch.argmax(probs).item()
+
+    answer = letters[pred]
+
+    result = f"Predicted Answer: {answer}\n\n"
+
+    result += "Scores\n\n"
+
+    for letter, score in zip(letters, probs):
+
+        result += f"{letter} : {score.item():.4f}\n"
+
+    return result
+
+
+# -------------------------
+# Gradio Interface
+# -------------------------
+demo = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Textbox(lines=4, label="Question"),
+        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="Prediction"),
+    title="RoBERTa-Base MCQ Solver",
+    description="Enter a question and five options.",
+    examples=[
+        [
+            "Which planet is known as the Red Planet?",
+            "Earth",
+            "Mars",
+            "Venus",
+            "Jupiter",
+            "Saturn"
+        ]
+    ]
+)
+
+if __name__ == "__main__":
+      demo.launch(server_name="0.0.0.0", server_port=7860)

requirements.txt

@@ -0,0 +1,5 @@
+torch
+transformers
+gradio
+sentencepiece
+accelerate