9c09944c96
- Analytics/Similar: expandable question preview with KaTeX rendering - KaTeXRenderer: auto markdown-to-HTML (code blocks, tables, bold), auto Unicode→LaTeX - ErrorBook: full question text rendering instead of truncated preview - Variant: remove hint/solution from generation (faster), async, fix null crash - Grading: add max_tokens limit - JSON parser: robust multi-layer repair + JSONDecodeError retry - Extraction prompt: enforce LaTeX notation for math - Upload: redirect to home instead of blank paper page - ProcessingBanner: add ETA time estimate + percentage - Batch import script + handoff guide for team Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
149 lines
5.0 KiB
Python
149 lines
5.0 KiB
Python
"""OCR, grading, and variant generation prompts"""
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import json
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import base64
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from app.services.llm_clients import get_vision_client, get_deepseek_client
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OCR_PROMPT = """You are an expert at recognizing handwritten answers. Analyze this photo of a student's handwritten answer and extract the text and mathematical formulas.
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Requirements:
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- Faithfully extract what the student wrote, do not modify or correct
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- Use LaTeX format for math formulas (e.g. $x^2 + 1$)
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- If there are multiple steps, list them in original order
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- If some handwriting is unclear, mark with [unclear]
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Return only the extracted text, no additional explanation."""
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GRADING_PROMPT = """You are an expert academic grader. Grade the following student answer. ALL output must be in English.
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Question info:
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- Number: {question_number}
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- Type: {question_type}
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- Question: {question_text}
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- Score: {score}
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Reference answer / solution:
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{reference_answer}
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Student answer:
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{student_answer}
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Grade and return JSON:
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{{
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"is_correct": true/false,
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"score_given": 0-{score},
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"feedback": "<HTML> Step-by-step analysis of the student's answer, pointing out correct parts and errors, using KaTeX formulas </HTML>",
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"error_at_step": null or the step number where errors begin (integer)
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}}
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Grading rules:
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- MC / fill-blank: only correct if answer matches exactly
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- Long questions: give partial credit for correct steps even if the final answer is wrong
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- feedback in HTML format, supports KaTeX ($..$ inline, $$...$$ block)
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- Mark errors with <div class="common-error">...</div>
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- Identify exactly which step the error starts"""
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VARIANT_PROMPT = """You are an expert exam question creator. Generate a similar but different variant question based on the original below. ALL output must be in English.
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Original question info:
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- Type: {question_type}
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- Question: {question_text}
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- Topics: {topics}
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- Difficulty: {difficulty}
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- Reference answer: {answer}
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Requirements:
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- Variant must test the same knowledge points at similar difficulty
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- Data/scenario/wording must differ — don't just change numbers
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- Must provide a complete correct answer
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Format requirements (CRITICAL):
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- All text in HTML format, absolutely NO markdown syntax
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- Code: <pre><code class="language-xxx">...</code></pre>, NOT ```
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- Math: $...$ (inline) or $$...$$ (block), KaTeX compatible
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- Line breaks: <br>, paragraphs: <p>
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Return JSON:
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{{
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"question_text": "HTML formatted variant question",
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"question_type": "{question_type}",
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"options": [MC only, format {{"label":"A","text":"..."}}, ...] or null,
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"correct_answer": "Correct answer (plain text)"
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}}"""
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def ocr_photo(photo_bytes: bytes) -> str:
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"""Gemini Vision OCR for handwritten answers"""
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client = get_vision_client()
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b64 = base64.b64encode(photo_bytes).decode("utf-8")
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resp = client.chat.completions.create(
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model="gemini-2.5-flash",
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messages=[
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{"role": "system", "content": OCR_PROMPT},
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{"role": "user", "content": [
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{"type": "image_url", "image_url": {
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"url": f"data:image/jpeg;base64,{b64}",
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}},
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]},
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],
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temperature=0,
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max_tokens=1500,
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)
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return resp.choices[0].message.content or ""
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def grade_answer(question: dict, student_answer: str) -> dict:
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"""Qwen grades student answer"""
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reference = question.get("raw_answer_text") or question.get("solution") or "No reference answer"
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score = question.get("score") or "unknown"
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ds = get_deepseek_client()
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resp = ds.chat.completions.create(
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model="deepseek-chat",
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messages=[
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{"role": "system", "content": GRADING_PROMPT.format(
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question_number=question["question_number"],
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question_type=question["question_type"],
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question_text=question["question_text"],
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score=score,
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reference_answer=reference,
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student_answer=student_answer,
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)},
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],
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temperature=0.2,
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max_tokens=2048,
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response_format={"type": "json_object"},
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)
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return json.loads(resp.choices[0].message.content)
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async def generate_variant(question: dict) -> dict:
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"""DeepSeek generates a variant question (async)"""
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import asyncio
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answer = (
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question.get("correct_option")
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or question.get("correct_answer")
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or question.get("raw_answer_text")
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or "N/A"
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)
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ds = get_deepseek_client()
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prompt = VARIANT_PROMPT.format(
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question_type=question["question_type"],
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question_text=question["question_text"],
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topics=", ".join(question.get("topics", [])),
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difficulty=question.get("difficulty", "medium"),
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answer=answer,
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)
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resp = await asyncio.to_thread(
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ds.chat.completions.create,
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model="deepseek-chat",
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messages=[{"role": "system", "content": prompt}],
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temperature=0.5,
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max_tokens=2048,
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response_format={"type": "json_object"},
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)
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return json.loads(resp.choices[0].message.content)
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