2e8c2c11d0
Vocabulary study with FSRS spaced repetition, AI tutoring (Ollama/Claude), essay marking, idioms browser, Anki export, and dashboard. 918 vocabulary entries across 39 categories. 41 tests passing. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
79 lines
2.2 KiB
Python
79 lines
2.2 KiB
Python
"""Essay writing and AI marking."""
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import db
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from ai import ask
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MARKING_SYSTEM_PROMPT = """You are an expert Persian (Farsi) language teacher marking a GCSE-level essay.
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You write in English but can read and correct Persian text.
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Always provide constructive, encouraging feedback suitable for a language learner."""
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MARKING_PROMPT_TEMPLATE = """Please mark this Persian essay written by a GCSE student.
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Theme: {theme}
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Student's essay:
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{essay_text}
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Please provide your response in this exact format:
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**Grade:** [Give a grade from 1-9 matching GCSE grading, or a descriptive level like A2/B1]
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**Summary:** [1-2 sentence overview of the essay quality]
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**Corrections:**
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[List specific errors with corrections. For each error, show the original text and the corrected version in Persian, with an English explanation]
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**Improved version:**
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[Rewrite the essay in corrected Persian]
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**Tips for improvement:**
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[3-5 specific, actionable tips for the student]"""
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GCSE_THEMES = [
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"Identity and culture",
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"Local area and environment",
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"School and work",
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"Travel and tourism",
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"International and global dimension",
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]
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def mark_essay(essay_text, theme="General"):
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"""Send essay to AI for marking. Returns structured feedback."""
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if not essay_text or not essay_text.strip():
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return "Please write an essay first."
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prompt = MARKING_PROMPT_TEMPLATE.format(
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theme=theme,
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essay_text=essay_text.strip(),
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)
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feedback = ask(prompt, system=MARKING_SYSTEM_PROMPT, quality="smart")
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# Extract grade from feedback (best-effort)
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grade = ""
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for line in feedback.split("\n"):
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if line.strip().startswith("**Grade:**"):
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grade = line.replace("**Grade:**", "").strip()
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break
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# Save to database
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db.save_essay(essay_text.strip(), grade, feedback, theme)
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return feedback
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def get_essay_history(limit=10):
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"""Return recent essays for the history view."""
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essays = db.get_recent_essays(limit)
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result = []
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for e in essays:
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result.append({
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"Date": e["timestamp"],
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"Theme": e["theme"] or "General",
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"Grade": e["grade"] or "-",
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"Preview": (e["essay_text"] or "")[:50] + "...",
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})
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return result
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