pyr0ball
c18bfec8f5
- pyproject.toml: add [tool.ruff] config suppressing E402/W293 globally, F841/E741/E702 in tests, E741 in scripts - inventory.py: split semicolon import (E702) - recipes.py: fix logger -> log (F821 undefined name) - shopping.py: rename l -> lnk in list comprehension (E741) - format_conversion.py: noqa F841 on CUDA flag (used as future hook) - backfill_keywords.py: rename done -> _done (F841) - ingest_purplecarrot.py: drop == True comparison (E712) - Auto-fix: I001 import sorting, F401 unused imports across all files
107 lines
4.1 KiB
Python
107 lines
4.1 KiB
Python
#!/usr/bin/env python3
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"""
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Prompt validation harness for recipe scanner (kiwi#9).
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Runs the draft extraction prompt against fixture images using the Anthropic API
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directly (bypasses llm.yaml — for prompt dev only, not production path).
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Usage:
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python extract_test.py <image1.jpg> [image2.jpg]
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"""
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import base64
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import io
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import json
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import os
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import sys
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from pathlib import Path
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import anthropic
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from PIL import Image, ImageOps
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PROMPT = """
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You are extracting a recipe from a photograph of a recipe card, cookbook page, or handwritten note.
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If two images are provided, treat them as a single recipe across two pages (e.g. ingredients on page 1, directions on page 2).
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Return a single JSON object with these fields:
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- title: recipe name (string)
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- subtitle: any secondary title or serving suggestion e.g. "with Broccoli & Ranch Dressing" (string or null)
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- servings: serving size if shown, as a string e.g. "2", "4-6" (string or null)
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- cook_time: total cook time if shown, e.g. "15 min", "1 hour" (string or null)
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- source_note: any attribution text like "From Betty Crocker" or "Purple Carrot" (string or null)
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- ingredients: array of ingredient objects, each with:
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- name: normalized generic ingredient name, lowercase, no quantities, no brand names
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(e.g. "Follow Your Heart® Vegan Ranch" → "ranch dressing")
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- qty: quantity as a string, preserving fractions e.g. "1/2", "¼" (string or null)
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- unit: unit of measure, null for countable items (e.g. "3 eggs" → unit: null)
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- raw: the original ingredient line verbatim, exactly as it appears
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- steps: ordered array of instruction strings, one distinct step per element
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- notes: any tips, substitutions, storage instructions, or variations (string or null)
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- confidence: "high" if text is clear and complete, "medium" if some parts are uncertain,
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"low" if mostly handwritten or significantly degraded
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- warnings: array of strings describing anything the user should double-check
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(e.g. "Directions appear to continue on another page not shown")
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Return only valid JSON. No markdown fences. No explanation outside the JSON.
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If the image does not appear to be a recipe at all, return: {"error": "not_a_recipe"}
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""".strip()
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def load_image_b64(path: Path) -> str:
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"""Load image, apply EXIF rotation, return base64-encoded JPEG."""
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with open(path, "rb") as f:
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img = Image.open(io.BytesIO(f.read()))
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img = ImageOps.exif_transpose(img) # fix phone rotation
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img = img.convert("RGB")
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buf = io.BytesIO()
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img.save(buf, format="JPEG", quality=90)
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return base64.b64encode(buf.getvalue()).decode()
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def extract(image_paths: list[Path]) -> dict:
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client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])
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content = []
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for i, path in enumerate(image_paths):
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if i > 0:
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content.append({"type": "text", "text": f"(Page {i + 1} of the same recipe:)"})
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content.append({
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"type": "image",
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"source": {
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"type": "base64",
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"media_type": "image/jpeg",
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"data": load_image_b64(path),
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},
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})
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content.append({"type": "text", "text": PROMPT})
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msg = client.messages.create(
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model="claude-opus-4-6", # best vision for prompt dev; production uses VisionRouter
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max_tokens=2048,
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messages=[{"role": "user", "content": content}],
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)
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raw = msg.content[0].text.strip()
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# Strip markdown fences if the model adds them anyway
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if raw.startswith("```"):
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raw = raw.split("```")[1]
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if raw.startswith("json"):
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raw = raw[4:]
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return json.loads(raw)
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if __name__ == "__main__":
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paths = [Path(p) for p in sys.argv[1:]]
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if not paths:
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print("Usage: python extract_test.py <image1.jpg> [image2.jpg]")
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sys.exit(1)
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for p in paths:
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if not p.exists():
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print(f"File not found: {p}")
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sys.exit(1)
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print(f"Extracting from: {[p.name for p in paths]}")
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print("Applying EXIF rotation + sending to claude-opus-4-6...\n")
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result = extract(paths)
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print(json.dumps(result, indent=2, ensure_ascii=False))
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