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import os
import gradio as gr
import base64
from typing import List
from io import BytesIO
from PIL import Image
import zipfile
from openai import OpenAI
def encode_image(image_path):
"""Encodes an image file to base64."""
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
def generate_prompt(question, marking_scheme, student_response):
"""Generates the grading prompt for the OpenAI API."""
prompt = f"""
Question: {question}
Marking Scheme: {marking_scheme}
Student Response: {student_response}
As an expert in this field, please grade the student's response based on the marking scheme provided. Provide detailed scores and feedback, and a well-tabulated breakdown of scores.
"""
return prompt
def read_file_content(file):
"""Reads the content of a file."""
with open(file.name, 'r') as f:
return f.read()
def grade_student_answers(client, marking_scheme, student_answers):
"""Grades student answers using the OpenAI API."""
prompt = f"""
Marking Scheme:
{marking_scheme}
Student Answers:
{student_answers}
Grade the student answers based on the marking scheme. Use appropriate Checkmark (✓) and (X). Provide a detailed feedback and score as a percentage.
The output should resemble that of a Professor!
"""
response = client.chat.completions.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are an expert Quiz grader."},
{"role": "user", "content": prompt}
],
max_tokens=1048
)
return response.choices[0].message.content.strip()
def grade_explanatory_test_text(api_key, question_file, marking_scheme_file, student_responses):
"""Grades explanatory test text files."""
client = OpenAI(api_key=api_key)
output_files = []
try:
question = read_file_content(question_file)
marking_scheme = read_file_content(marking_scheme_file)
for student_file in student_responses:
student_name = os.path.splitext(os.path.basename(student_file.name))[0]
student_response = read_file_content(student_file)
prompt = generate_prompt(question, marking_scheme, student_response)
response = client.chat.completions.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are a helpful assistant"},
{"role": "user", "content": prompt}
],
max_tokens=3500,
temperature=0.0
)
grade = response.choices[0].message.content.strip()
output_filename = f"{student_name}_grade.txt"
with open(output_filename, 'w') as out_f:
out_f.write(grade)
output_files.append(output_filename)
zip_filename = "graded_results.zip"
with zipfile.ZipFile(zip_filename, 'w') as zip_file:
for file in output_files:
zip_file.write(file)
return zip_filename
except Exception as e:
return f"An error occurred: {e}"
def extract_text_from_image(api_key, image_file):
"""Extracts text from an image using the OpenAI API."""
try:
client = OpenAI(api_key=api_key)
with Image.open(image_file) as img:
img = img.convert("RGB")
img.thumbnail((1280, 1280))
buffer = BytesIO()
img.save(buffer, format="JPEG")
base64_image = base64.b64encode(buffer.getvalue()).decode('utf-8')
response = client.chat.completions.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are a helpful assistant"},
{"role": "user", "content": [
{"type":"text", "text": "Extract the text from this image. It is a student exam script, where the student is answering multiple choice questions. Write out the text in the image. Don't include any other text in your output."},
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}
]}
],
max_tokens=1048
)
return response.choices[0].message.content.strip()
except Exception as e:
return f"An error occurred while extracting text from the image: {e}"
def grade_explanatory_test_image(api_key, question_file, marking_scheme_file, student_responses):
"""Grades explanatory test image files."""
client = OpenAI(api_key=api_key)
output_files = []
try:
question = read_file_content(question_file)
marking_scheme = read_file_content(marking_scheme_file)
for image_file in student_responses:
student_name = os.path.splitext(os.path.basename(image_file.name))[0]
student_response = extract_text_from_image(api_key, image_file)
if "An error occurred" in student_response:
return student_response
prompt = generate_prompt(question, marking_scheme, student_response)
response = client.chat.completions.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are a helpful assistant"},
{"role": "user", "content": prompt}
],
max_tokens=3500,
temperature=0.0
)
grade = response.choices[0].message.content.strip()
output_filename = f"{student_name}_grade.txt"
with open(output_filename, 'w') as out_f:
out_f.write(grade)
output_files.append(output_filename)
zip_filename = "graded_results.zip"
with zipfile.ZipFile(zip_filename, 'w') as zip_file:
for file in output_files:
zip_file.write(file)
return zip_filename
except Exception as e:
return f"An error occurred: {e}"
def grade_multiple_choice_test(api_key, marking_scheme_file, images):
"""Grades multiple choice test image files."""
client = OpenAI(api_key=api_key)
output_files = []
try:
marking_scheme = read_file_content(marking_scheme_file)
for image_file in images:
student_name = os.path.splitext(os.path.basename(image_file.name))[0]
student_answers = extract_text_from_image(api_key, image_file)
if "An error occurred" in student_answers:
return student_answers
grade = grade_student_answers(client, marking_scheme, student_answers)
output_filename = f"{student_name}_grade.txt"
with open(output_filename, 'w') as out_f:
out_f.write(grade)
output_files.append(output_filename)
zip_filename = "graded_results.zip"
with zipfile.ZipFile(zip_filename, 'w') as zip_file:
for file in output_files:
zip_file.write(file)
return zip_filename
except Exception as e:
return f"An error occurred: {e}"
def handle_choice(choice):
"""Handles the user choice for test type and updates the UI accordingly."""
if choice == "Explanatory Test":
return [
gr.update(visible=True), # question_file
gr.update(visible=True), # marking_scheme_explanatory_file
gr.update(visible=True), # explanatory_type
gr.update(visible=False), # marking_scheme_mcq_file
gr.update(visible=False), # image_input_mcq
gr.update(visible=False), # student_responses_text
gr.update(visible=False) # student_responses_image
]
else:
return [
gr.update(visible=False), # question_file
gr.update(visible=False), # marking_scheme_explanatory_file
gr.update(visible=False), # explanatory_type
gr.update(visible=True), # marking_scheme_mcq_file
gr.update(visible=True), # image_input_mcq
gr.update(visible=False), # student_responses_text
gr.update(visible=False) # student_responses_image
]
def handle_explanatory_type(explanatory_type):
"""Handles the explanatory test type and updates the UI accordingly."""
if explanatory_type == "Text Files":
return [
gr.update(visible=True), # student_responses_text
gr.update(visible=False) # student_responses_image
]
else:
return [
gr.update(visible=False), # student_responses_text
gr.update(visible=True) # student_responses_image
]
def clear_inputs():
"""Clears all input fields."""
return [
"", # API key
None, # choice
gr.update(value=None, visible=False), # explanatory_type
gr.update(visible=False), # question_file
gr.update(visible=False), # marking_scheme_explanatory_file
gr.update(visible=False), # student_responses_text
gr.update(visible=False), # student_responses_image
gr.update(visible=False), # marking_scheme_mcq_file
gr.update(visible=False), # image_input_mcq
None # output_file
]
# Gradio Interface
with gr.Blocks() as demo:
api_key = gr.Textbox(label="OpenAI API Key", type="password")
choice = gr.Radio(["Explanatory Test", "Multiple Choice Test"], label="Choose the type of test to grade")
explanatory_type = gr.Radio(["Text Files", "Image Files"], label="Choose the type of explanatory test", visible=False)
# Explanatory test inputs
question_file = gr.File(label="Upload Question File", visible=False)
marking_scheme_explanatory_file = gr.File(label="Upload Marking Scheme File", visible=False)
student_responses_text = gr.File(label="Upload Student Response Text Files", file_count='multiple', visible=False)
student_responses_image = gr.File(label="Upload Student Response Image Files", file_count='multiple', visible=False)
# Multiple choice test inputs
marking_scheme_mcq_file = gr.File(label="Upload Marking Scheme File", visible=False)
image_input_mcq = gr.File(label="Upload Student Answer Images", file_count='multiple', visible=False)
output_file = gr.File(label="Download Graded Results")
# Handle choice to show/hide appropriate inputs
choice.change(fn=handle_choice, inputs=choice, outputs=[question_file, marking_scheme_explanatory_file, explanatory_type, marking_scheme_mcq_file, image_input_mcq])
explanatory_type.change(fn=handle_explanatory_type, inputs=explanatory_type, outputs=[student_responses_text, student_responses_image])
# Submit and Clear buttons
submit_btn = gr.Button("Submit")
clear_btn = gr.Button("Clear")
submit_btn.click(
fn=lambda api_key, choice, explanatory_type, question_file, marking_scheme_explanatory_file, student_responses_text, student_responses_image, marking_scheme_mcq_file, image_input_mcq:
grade_explanatory_test_text(api_key, question_file, marking_scheme_explanatory_file, student_responses_text) if explanatory_type == "Text Files" else
grade_explanatory_test_image(api_key, question_file, marking_scheme_explanatory_file, student_responses_image) if choice == "Explanatory Test" else
grade_multiple_choice_test(api_key, marking_scheme_mcq_file, image_input_mcq),
inputs=[api_key, choice, explanatory_type, question_file, marking_scheme_explanatory_file, student_responses_text, student_responses_image, marking_scheme_mcq_file, image_input_mcq],
outputs=output_file
)
clear_btn.click(
fn=clear_inputs,
inputs=[],
outputs=[api_key, choice, explanatory_type, question_file, marking_scheme_explanatory_file,
student_responses_text, student_responses_image, marking_scheme_mcq_file,
image_input_mcq, output_file]
)
if __name__ == "__main__":
demo.launch() |