| import os |
| import gradio as gr |
| import google.generativeai as genai |
| from markdown_pdf import MarkdownPdf, Section |
|
|
| |
| PROMPTS = { |
| "ALIGNMENT_PROMPT": { |
| "role": "system", |
| "content": """Your Role: You are an expert examiner and transcription specialist. |
| Your task is to **align three sources**: |
| - Question Paper (QP) |
| - Markscheme (MS) |
| - Student Answer Sheet (AS) |
| |
| ### Instructions |
| 1. Parse all documents carefully and align them **per question and sub-question**. |
| 2. For each question/sub-question, produce a structured block: |
| |
| --- |
| ## Question X (and sub-question if applicable) |
| **QP:** [Insert the exact question text] |
| **MS:** [Insert the relevant part of the markscheme] |
| **AS:** [Insert the student's final cleaned answer transcription] |
| --- |
| |
| 3. Formatting Rules: |
| - Use `##` for main questions and `###` for sub-questions. |
| - Write **QP | MS | AS** exactly in that order. |
| - Preserve all mathematical expressions inside fenced code blocks. |
| - Do not re-create diagrams/graphs. Write `[Graph omitted]`. |
| - If part of the student's answer is unreadable, write `[illegible]`. |
| - If a student skipped a question, write `[No response]`. |
| - Keep MS annotations (M1, A1, R1, etc.) exactly as in the original. |
| |
| 4. Output must be **clean, deterministic, and consistent** — so that another model can grade directly using this aligned representation. |
| |
| ### Example |
| ## Question 1 |
| **QP:** Expand `(1+x)^3` |
| **MS:** M1 for binomial expansion, A1 for coefficients, A1 for final form |
| **AS:** |
| """ |
| }, |
| "GRADING_PROMPT": { |
| "role": "system", |
| "content": """You are an official examiner. Use the following grading rules strictly. |
| Abbreviations: |
| - M: Marks awarded for attempting to use a correct Method. |
| - A: Marks awarded for an Answer or for Accuracy; often dependent on preceding M marks. |
| - R: Marks awarded for clear Reasoning. |
| - AG: Answer given in the question and so no marks are awarded. |
| - FT: Follow through. The practice of awarding marks, despite candidate errors in previous parts, for their correct methods/answers using incorrect results. |
| -------------------------------------------- |
| ## 1. General |
| Award marks using the annotations as noted in the markscheme (e.g., M1, A2). |
| ## 2. Method and Answer/Accuracy marks |
| - Do not automatically award full marks for a correct answer; all working must be checked. |
| - It is generally not possible to award M0 followed by A1. |
| - Where M and A marks are noted on the same line (M1A1), M is for method, A is for accuracy. |
| - Multiple A marks can be independent. |
| ## 3. Implied marks |
| Implied marks (M1) can only be awarded if correct work is seen or implied. |
| ## 4. Follow through (FT) marks |
| - Award FT if an earlier wrong answer is used consistently later. |
| - Do not award FT if the result contradicts the question (e.g., probability > 1). |
| ## 5. Mis-read (MR) |
| - Penalize once if the candidate misreads a value. |
| - Award other marks as appropriate. |
| ## 6. Alternative methods |
| - Accept valid alternatives unless "Hence" forbids it. |
| ## 7. Alternative forms |
| - Accept equivalent numeric/algebraic forms unless specified otherwise. |
| ## 8. Format and accuracy of answers |
| - Use correct accuracy (3 s.f. if not specified). |
| - Arithmetic and algebra should be simplified. |
| ## 9. Presentation of candidate work |
| - Ignore crossed-out work unless indicated. |
| - Mark only the first solution unless candidate specifies otherwise. |
| ## 10. Graph/Diagram Questions |
| - If a question requires drawing or interpreting a graph/diagram, assume the student has done it correctly and award full marks for that part. |
| -------------------------------------------- |
| ### OUTPUT FORMAT |
| Produce a GitHub-flavored Markdown table with 3 columns: |
| | Student wrote | Marks Awarded | Reason | |
| |---------------|---------------|--------| |
| Special Formatting Rule: |
| - Whenever a mark is lost (M0, A0, R0 etc.), wrap it in red using: `<span style="color:red">M0</span>`. |
| - Also wrap the corresponding Reason in red color. |
| - Keep awarded marks (M1, A1, etc.) in plain text. |
| - If mixed (e.g., M1A0A1), only highlight the lost marks (`A0`) and its reason. |
| After the table, provide: |
| ### Summary & Final Mark |
| - Total marks obtained vs total available |
| - Any FT (follow-through) applied |
| - Classification of errors (Conceptual, Silly mistake, Misread, etc.) |
| """ |
| } |
| } |
|
|
| |
| genai.configure(api_key=os.getenv("GEMINI_API_KEY")) |
|
|
| |
| def save_as_pdf(text, filename="output.pdf"): |
| pdf = MarkdownPdf() |
| pdf.add_section(Section(text, toc=False)) |
| pdf.save(filename) |
| return filename |
|
|
| |
| def create_model(): |
| try: |
| print("⚡ Using gemini-2.5-pro model") |
| return genai.GenerativeModel("gemini-2.5-pro", generation_config={"temperature": 0}) |
| except Exception: |
| print("⚡ Falling back to gemini-2.5-flash model") |
| return genai.GenerativeModel("gemini-2.5-flash", generation_config={"temperature": 0}) |
|
|
| |
| def align_and_grade(qp_file, ms_file, ans_file): |
| try: |
| |
| qp_uploaded = genai.upload_file(path=qp_file, display_name="Question Paper") |
| ms_uploaded = genai.upload_file(path=ms_file, display_name="Markscheme") |
| ans_uploaded = genai.upload_file(path=ans_file, display_name="Answer Sheet") |
|
|
| model = create_model() |
|
|
| |
| resp = model.generate_content([ |
| PROMPTS["ALIGNMENT_PROMPT"]["content"], |
| qp_uploaded, |
| ms_uploaded, |
| ans_uploaded |
| ]) |
| aligned_text = getattr(resp, "text", None) |
| if not aligned_text and resp.candidates: |
| aligned_text = resp.candidates[0].content.parts[0].text |
|
|
| aligned_pdf_path = save_as_pdf(aligned_text, "aligned_qp_ms_as.pdf") |
|
|
| |
| response = model.generate_content([ |
| PROMPTS["GRADING_PROMPT"]["content"], |
| aligned_text |
| ]) |
| grading = getattr(response, "text", None) |
| if not grading and response.candidates: |
| grading = response.candidates[0].content.parts[0].text |
|
|
| |
| base_name = os.path.splitext(os.path.basename(ans_file))[0] |
| grading_pdf_path = save_as_pdf(grading, f"{base_name}_graded.pdf") |
|
|
| return aligned_text, aligned_pdf_path, grading, grading_pdf_path |
|
|
| except Exception as e: |
| return f"❌ Error: {e}", None, None, None |
|
|
| |
| with gr.Blocks(title="LeadIB AI Grading (Alignment + Auto-Grading)") as demo: |
| gr.Markdown("## LeadIB AI Grading\nUpload Question Paper, Markscheme, and Student Answer Sheet.\nThe system will align and grade automatically.") |
|
|
| with gr.Row(): |
| qp_file = gr.File(label="Upload Question Paper (PDF)", type="filepath") |
| ms_file = gr.File(label="Upload Markscheme (PDF)", type="filepath") |
| ans_file = gr.File(label="Upload Student Answer Sheet (PDF)", type="filepath") |
|
|
| run_btn = gr.Button("Start Alignment + Auto-Grading") |
|
|
| with gr.Row(): |
| aligned_out = gr.Textbox(label="📄 Aligned QP | MS | AS", lines=20) |
| aligned_pdf = gr.File(label="⬇️ Download Aligned (PDF)") |
|
|
| with gr.Row(): |
| grading_out = gr.Textbox(label="✅ Grading Report", lines=20) |
| grading_pdf = gr.File(label="⬇️ Download Grading Report (PDF)") |
|
|
| run_btn.click( |
| fn=align_and_grade, |
| inputs=[qp_file, ms_file, ans_file], |
| outputs=[aligned_out, aligned_pdf, grading_out, grading_pdf], |
| show_progress=True |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
|
|