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import streamlit as st
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the tokenizer and model using PyTorch
tokenizer = AutoTokenizer.from_pretrained("MohamedMotaz/Examination-llama-8b-4bit")
model = AutoModelForCausalLM.from_pretrained("MohamedMotaz/Examination-llama-8b-4bit", torch_dtype=torch.float16).to("cuda" if torch.cuda.is_available() else "cpu")
# App Title
st.title("Exam Corrector: Automated Grading with LLama 8b Model (PyTorch)")
# 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.")
# Display documentation in the app
with st.expander("Click to View Documentation"):
st.markdown("""
## Exam-Corrector: A Fine-tuned LLama 8b Model
Exam-corrector is a fine-tuned version of the LLama 8b model, specifically adapted to function as a written question corrector. This model grades student answers by comparing them against model answers using predefined instructions.
### Model Description:
The model ensures consistent and fair grading for written answers. Full marks are given to student answers that convey the complete meaning of the model answer, even with different wording.
### Grading Instructions:
- Model Answer is only used as a reference and does not receive marks.
- Full marks are awarded when student answers convey the full meaning of the model answer.
- Partial marks are deducted for incomplete or irrelevant information.
### Input Format:
- **Model Answer**: {model_answer}
- **Student Answer**: {student_answer}
### Output Format:
- **Grade**: {grade}
- **Explanation**: {explanation}
### Training Details:
- Fine-tuned with LoRA (Low-Rank Adaptation).
- Percentage of trainable model parameters: 3.56%.
""")
# 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.to(model.device)
# Generate the response using the model (PyTorch)
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)**
""") |