pyr0ball
9c4d8b7883
- Add TimeEffortProfile + StepAnalysis Pydantic schemas; serialised into RecipeSuggestion so the frontend receives active/passive/total minutes, effort label, and detected equipment per suggestion. - parse_time_effort() now drives max_total_min filter (falls back to step-count estimate when directions contain no explicit time mentions). - _PRODUCT_TOKEN_STOPWORDS: strips marketing/packaging words from multi-word product labels before adding individual ingredient tokens to pantry_set. "Organic Extra Firm Tofu" → adds "tofu"; improves packaged-food pantry match. - L1 candidate pool raised to 60 (was 20); min_match_ratio lowered to 0.35 (was 0.60) to keep enough results for plant-based / packaged-food pantries. - household.py: tighten import to pull HEIMDALL_URL/ADMIN_TOKEN from services.heimdall_orch (matches refactor in cloud_session.py).
107 lines
4.1 KiB
Python
107 lines
4.1 KiB
Python
#!/usr/bin/env python3
|
|
"""
|
|
Prompt validation harness for recipe scanner (kiwi#9).
|
|
|
|
Runs the draft extraction prompt against fixture images using the Anthropic API
|
|
directly (bypasses llm.yaml — for prompt dev only, not production path).
|
|
|
|
Usage:
|
|
python extract_test.py <image1.jpg> [image2.jpg]
|
|
"""
|
|
import base64
|
|
import io
|
|
import json
|
|
import os
|
|
import sys
|
|
from pathlib import Path
|
|
|
|
from PIL import Image, ImageOps
|
|
import anthropic
|
|
|
|
PROMPT = """
|
|
You are extracting a recipe from a photograph of a recipe card, cookbook page, or handwritten note.
|
|
|
|
If two images are provided, treat them as a single recipe across two pages (e.g. ingredients on page 1, directions on page 2).
|
|
|
|
Return a single JSON object with these fields:
|
|
- title: recipe name (string)
|
|
- subtitle: any secondary title or serving suggestion e.g. "with Broccoli & Ranch Dressing" (string or null)
|
|
- servings: serving size if shown, as a string e.g. "2", "4-6" (string or null)
|
|
- cook_time: total cook time if shown, e.g. "15 min", "1 hour" (string or null)
|
|
- source_note: any attribution text like "From Betty Crocker" or "Purple Carrot" (string or null)
|
|
- ingredients: array of ingredient objects, each with:
|
|
- name: normalized generic ingredient name, lowercase, no quantities, no brand names
|
|
(e.g. "Follow Your Heart® Vegan Ranch" → "ranch dressing")
|
|
- qty: quantity as a string, preserving fractions e.g. "1/2", "¼" (string or null)
|
|
- unit: unit of measure, null for countable items (e.g. "3 eggs" → unit: null)
|
|
- raw: the original ingredient line verbatim, exactly as it appears
|
|
- steps: ordered array of instruction strings, one distinct step per element
|
|
- notes: any tips, substitutions, storage instructions, or variations (string or null)
|
|
- confidence: "high" if text is clear and complete, "medium" if some parts are uncertain,
|
|
"low" if mostly handwritten or significantly degraded
|
|
- warnings: array of strings describing anything the user should double-check
|
|
(e.g. "Directions appear to continue on another page not shown")
|
|
|
|
Return only valid JSON. No markdown fences. No explanation outside the JSON.
|
|
If the image does not appear to be a recipe at all, return: {"error": "not_a_recipe"}
|
|
""".strip()
|
|
|
|
|
|
def load_image_b64(path: Path) -> str:
|
|
"""Load image, apply EXIF rotation, return base64-encoded JPEG."""
|
|
with open(path, "rb") as f:
|
|
img = Image.open(io.BytesIO(f.read()))
|
|
img = ImageOps.exif_transpose(img) # fix phone rotation
|
|
img = img.convert("RGB")
|
|
buf = io.BytesIO()
|
|
img.save(buf, format="JPEG", quality=90)
|
|
return base64.b64encode(buf.getvalue()).decode()
|
|
|
|
|
|
def extract(image_paths: list[Path]) -> dict:
|
|
client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])
|
|
|
|
content = []
|
|
for i, path in enumerate(image_paths):
|
|
if i > 0:
|
|
content.append({"type": "text", "text": f"(Page {i + 1} of the same recipe:)"})
|
|
content.append({
|
|
"type": "image",
|
|
"source": {
|
|
"type": "base64",
|
|
"media_type": "image/jpeg",
|
|
"data": load_image_b64(path),
|
|
},
|
|
})
|
|
content.append({"type": "text", "text": PROMPT})
|
|
|
|
msg = client.messages.create(
|
|
model="claude-opus-4-6", # best vision for prompt dev; production uses VisionRouter
|
|
max_tokens=2048,
|
|
messages=[{"role": "user", "content": content}],
|
|
)
|
|
raw = msg.content[0].text.strip()
|
|
# Strip markdown fences if the model adds them anyway
|
|
if raw.startswith("```"):
|
|
raw = raw.split("```")[1]
|
|
if raw.startswith("json"):
|
|
raw = raw[4:]
|
|
return json.loads(raw)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
paths = [Path(p) for p in sys.argv[1:]]
|
|
if not paths:
|
|
print("Usage: python extract_test.py <image1.jpg> [image2.jpg]")
|
|
sys.exit(1)
|
|
|
|
for p in paths:
|
|
if not p.exists():
|
|
print(f"File not found: {p}")
|
|
sys.exit(1)
|
|
|
|
print(f"Extracting from: {[p.name for p in paths]}")
|
|
print("Applying EXIF rotation + sending to claude-opus-4-6...\n")
|
|
|
|
result = extract(paths)
|
|
print(json.dumps(result, indent=2, ensure_ascii=False))
|