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import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load the tokenizer and model for CPU without bitsandbytes
tokenizer = AutoTokenizer.from_pretrained("MohamedMotaz/Examination-llama-8b-4bit")
# Load the model in full precision, explicitly avoiding 8-bit quantization
model = AutoModelForCausalLM.from_pretrained(
"MohamedMotaz/Examination-llama-8b-4bit",
torch_dtype=torch.float32, # Ensure it uses full precision (float32)
device_map="cpu", # Force the model to run on the CPU
)
# App Title
st.title("Exam Corrector: Automated Grading with LLama 8b Model (CPU)")
# Instructions
st.markdown("""
### Instructions:
- Enter both the **Model Answer** and the **Student Answer**.
- Click on the **Grade Answer** button to get the grade and explanation.
""")
# Input fields for Model Answer and Student Answer
model_answer = st.text_area("Model Answer", "The process of photosynthesis involves converting light energy into chemical energy.")
student_answer = st.text_area("Student Answer", "Photosynthesis is when plants turn light into energy.")
# Button to trigger grading
if st.button("Grade Answer"):
# Combine inputs into the required prompt format
inputs = f"Model Answer: {model_answer}\n\nStudent Answer: {student_answer}\n\nResponse:"
# Tokenize the inputs using PyTorch tensors
input_ids = tokenizer(inputs, return_tensors="pt").input_ids
# Generate the response using the model (PyTorch, CPU-based)
with torch.no_grad():
outputs = model.generate(input_ids, max_length=200)
# Decode the output
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Display the grade and explanation
st.subheader("Grading Results")
st.write(response)
# Footer and app creator details
st.markdown("""
---
**App created by [Engr. Hamesh Raj](https://www.linkedin.com/in/hamesh-raj)**
""") |