app.py created

a brand new app.py

app.py

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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()