[split-required] Split remaining Python monoliths (Phase 1 continued)
klausur-service (7 monoliths): - grid_editor_helpers.py (1,737 → 5 files: columns, filters, headers, zones) - cv_cell_grid.py (1,675 → 7 files: build, legacy, streaming, merge, vocab) - worksheet_editor_api.py (1,305 → 4 files: models, AI, reconstruct, routes) - legal_corpus_ingestion.py (1,280 → 3 files: registry, chunking, ingestion) - cv_review.py (1,248 → 4 files: pipeline, spell, LLM, barrel) - cv_preprocessing.py (1,166 → 3 files: deskew, dewarp, barrel) - rbac.py, admin_api.py, routes/eh.py remain (next batch) backend-lehrer (1 monolith): - classroom_engine/repository.py (1,705 → 7 files by domain) All re-export barrels preserve backward compatibility. Zero import errors verified. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Benjamin Admin
485
klausur-service/backend/worksheet_editor_ai.py
Normal file
485
klausur-service/backend/worksheet_editor_ai.py
Normal file
@@ -0,0 +1,485 @@
|
||||
"""
|
||||
Worksheet Editor AI — AI image generation and AI worksheet modification.
|
||||
"""
|
||||
|
||||
import io
|
||||
import json
|
||||
import base64
|
||||
import logging
|
||||
import re
|
||||
import time
|
||||
import random
|
||||
from typing import List, Dict
|
||||
|
||||
import httpx
|
||||
|
||||
from worksheet_editor_models import (
|
||||
AIImageRequest,
|
||||
AIImageResponse,
|
||||
AIImageStyle,
|
||||
AIModifyRequest,
|
||||
AIModifyResponse,
|
||||
OLLAMA_URL,
|
||||
STYLE_PROMPTS,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# =============================================
|
||||
# AI IMAGE GENERATION
|
||||
# =============================================
|
||||
|
||||
async def generate_ai_image_logic(request: AIImageRequest) -> AIImageResponse:
|
||||
"""
|
||||
Generate an AI image using Ollama with a text-to-image model.
|
||||
|
||||
Falls back to a placeholder if Ollama is not available.
|
||||
"""
|
||||
from fastapi import HTTPException
|
||||
|
||||
try:
|
||||
# Build enhanced prompt with style
|
||||
style_modifier = STYLE_PROMPTS.get(request.style, "")
|
||||
enhanced_prompt = f"{request.prompt}, {style_modifier}"
|
||||
|
||||
logger.info(f"Generating AI image: {enhanced_prompt[:100]}...")
|
||||
|
||||
# Check if Ollama is available
|
||||
async with httpx.AsyncClient(timeout=10.0) as check_client:
|
||||
try:
|
||||
health_response = await check_client.get(f"{OLLAMA_URL}/api/tags")
|
||||
if health_response.status_code != 200:
|
||||
raise HTTPException(status_code=503, detail="Ollama service not available")
|
||||
except httpx.ConnectError:
|
||||
logger.warning("Ollama not reachable, returning placeholder")
|
||||
return _generate_placeholder_image(request, enhanced_prompt)
|
||||
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=300.0) as client:
|
||||
tags_response = await client.get(f"{OLLAMA_URL}/api/tags")
|
||||
available_models = [m.get("name", "") for m in tags_response.json().get("models", [])]
|
||||
|
||||
sd_model = None
|
||||
for model in available_models:
|
||||
if "stable" in model.lower() or "sd" in model.lower() or "diffusion" in model.lower():
|
||||
sd_model = model
|
||||
break
|
||||
|
||||
if not sd_model:
|
||||
logger.warning("No Stable Diffusion model found in Ollama")
|
||||
return _generate_placeholder_image(request, enhanced_prompt)
|
||||
|
||||
logger.info(f"SD model found: {sd_model}, but image generation API not implemented")
|
||||
return _generate_placeholder_image(request, enhanced_prompt)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Image generation failed: {e}")
|
||||
return _generate_placeholder_image(request, enhanced_prompt)
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"AI image generation error: {e}")
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
def _generate_placeholder_image(request: AIImageRequest, prompt: str) -> AIImageResponse:
|
||||
"""
|
||||
Generate a placeholder image when AI generation is not available.
|
||||
Creates a simple SVG-based placeholder with the prompt text.
|
||||
"""
|
||||
from PIL import Image, ImageDraw, ImageFont
|
||||
|
||||
width, height = request.width, request.height
|
||||
|
||||
style_colors = {
|
||||
AIImageStyle.REALISTIC: ("#2563eb", "#dbeafe"),
|
||||
AIImageStyle.CARTOON: ("#f97316", "#ffedd5"),
|
||||
AIImageStyle.SKETCH: ("#6b7280", "#f3f4f6"),
|
||||
AIImageStyle.CLIPART: ("#8b5cf6", "#ede9fe"),
|
||||
AIImageStyle.EDUCATIONAL: ("#059669", "#d1fae5"),
|
||||
}
|
||||
|
||||
fg_color, bg_color = style_colors.get(request.style, ("#6366f1", "#e0e7ff"))
|
||||
|
||||
img = Image.new('RGB', (width, height), bg_color)
|
||||
draw = ImageDraw.Draw(img)
|
||||
|
||||
draw.rectangle([5, 5, width-6, height-6], outline=fg_color, width=3)
|
||||
|
||||
cx, cy = width // 2, height // 2 - 30
|
||||
draw.ellipse([cx-40, cy-40, cx+40, cy+40], outline=fg_color, width=3)
|
||||
draw.line([cx-20, cy-10, cx+20, cy-10], fill=fg_color, width=3)
|
||||
draw.line([cx, cy-10, cx, cy+20], fill=fg_color, width=3)
|
||||
|
||||
try:
|
||||
font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 14)
|
||||
except Exception:
|
||||
font = ImageFont.load_default()
|
||||
|
||||
max_chars = 40
|
||||
lines = []
|
||||
words = prompt[:200].split()
|
||||
current_line = ""
|
||||
for word in words:
|
||||
if len(current_line) + len(word) + 1 <= max_chars:
|
||||
current_line += (" " + word if current_line else word)
|
||||
else:
|
||||
if current_line:
|
||||
lines.append(current_line)
|
||||
current_line = word
|
||||
if current_line:
|
||||
lines.append(current_line)
|
||||
|
||||
text_y = cy + 60
|
||||
for line in lines[:4]:
|
||||
bbox = draw.textbbox((0, 0), line, font=font)
|
||||
text_width = bbox[2] - bbox[0]
|
||||
draw.text((cx - text_width // 2, text_y), line, fill=fg_color, font=font)
|
||||
text_y += 20
|
||||
|
||||
badge_text = "KI-Bild (Platzhalter)"
|
||||
try:
|
||||
badge_font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 10)
|
||||
except Exception:
|
||||
badge_font = font
|
||||
draw.rectangle([10, height-30, 150, height-10], fill=fg_color)
|
||||
draw.text((15, height-27), badge_text, fill="white", font=badge_font)
|
||||
|
||||
buffer = io.BytesIO()
|
||||
img.save(buffer, format='PNG')
|
||||
buffer.seek(0)
|
||||
|
||||
image_base64 = f"data:image/png;base64,{base64.b64encode(buffer.getvalue()).decode('utf-8')}"
|
||||
|
||||
return AIImageResponse(
|
||||
image_base64=image_base64,
|
||||
prompt_used=prompt,
|
||||
error="AI image generation not available. Using placeholder."
|
||||
)
|
||||
|
||||
|
||||
# =============================================
|
||||
# AI WORKSHEET MODIFICATION
|
||||
# =============================================
|
||||
|
||||
async def modify_worksheet_with_ai_logic(request: AIModifyRequest) -> AIModifyResponse:
|
||||
"""
|
||||
Modify a worksheet using AI based on natural language prompt.
|
||||
"""
|
||||
try:
|
||||
logger.info(f"AI modify request: {request.prompt[:100]}...")
|
||||
|
||||
try:
|
||||
canvas_data = json.loads(request.canvas_json)
|
||||
except json.JSONDecodeError:
|
||||
return AIModifyResponse(
|
||||
message="Fehler beim Parsen des Canvas",
|
||||
error="Invalid canvas JSON"
|
||||
)
|
||||
|
||||
system_prompt = """Du bist ein Assistent fuer die Bearbeitung von Arbeitsblaettern.
|
||||
Du erhaeltst den aktuellen Zustand eines Canvas im JSON-Format und eine Anweisung des Nutzers.
|
||||
Deine Aufgabe ist es, die gewuenschten Aenderungen am Canvas vorzunehmen.
|
||||
|
||||
Der Canvas verwendet Fabric.js. Hier sind die wichtigsten Objekttypen:
|
||||
- i-text: Interaktiver Text mit fontFamily, fontSize, fill, left, top
|
||||
- rect: Rechteck mit left, top, width, height, fill, stroke, strokeWidth
|
||||
- circle: Kreis mit left, top, radius, fill, stroke, strokeWidth
|
||||
- line: Linie mit x1, y1, x2, y2, stroke, strokeWidth
|
||||
|
||||
Das Canvas ist 794x1123 Pixel (A4 bei 96 DPI).
|
||||
|
||||
Antworte NUR mit einem JSON-Objekt in diesem Format:
|
||||
{
|
||||
"action": "modify" oder "add" oder "delete" oder "info",
|
||||
"objects": [...], // Neue/modifizierte Objekte (bei modify/add)
|
||||
"message": "Kurze Beschreibung der Aenderung"
|
||||
}
|
||||
|
||||
Wenn du Objekte hinzufuegst, generiere eindeutige IDs im Format "obj_<timestamp>_<random>".
|
||||
"""
|
||||
|
||||
user_prompt = f"""Aktueller Canvas-Zustand:
|
||||
```json
|
||||
{json.dumps(canvas_data, indent=2)[:5000]}
|
||||
```
|
||||
|
||||
Nutzer-Anweisung: {request.prompt}
|
||||
|
||||
Fuehre die Aenderung durch und antworte mit dem JSON-Objekt."""
|
||||
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=120.0) as client:
|
||||
response = await client.post(
|
||||
f"{OLLAMA_URL}/api/generate",
|
||||
json={
|
||||
"model": request.model,
|
||||
"prompt": user_prompt,
|
||||
"system": system_prompt,
|
||||
"stream": False,
|
||||
"options": {
|
||||
"temperature": 0.3,
|
||||
"num_predict": 4096
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
if response.status_code != 200:
|
||||
logger.warning(f"Ollama error: {response.status_code}, trying local fallback")
|
||||
return _handle_simple_modification(request.prompt, canvas_data)
|
||||
|
||||
ai_response = response.json().get("response", "")
|
||||
|
||||
except httpx.ConnectError:
|
||||
logger.warning("Ollama not reachable")
|
||||
return _handle_simple_modification(request.prompt, canvas_data)
|
||||
except httpx.TimeoutException:
|
||||
logger.warning("Ollama timeout, trying local fallback")
|
||||
return _handle_simple_modification(request.prompt, canvas_data)
|
||||
|
||||
try:
|
||||
json_start = ai_response.find('{')
|
||||
json_end = ai_response.rfind('}') + 1
|
||||
|
||||
if json_start == -1 or json_end <= json_start:
|
||||
logger.warning(f"No JSON found in AI response: {ai_response[:200]}")
|
||||
return AIModifyResponse(
|
||||
message="KI konnte die Anfrage nicht verarbeiten",
|
||||
error="No JSON in response"
|
||||
)
|
||||
|
||||
ai_json = json.loads(ai_response[json_start:json_end])
|
||||
action = ai_json.get("action", "info")
|
||||
message = ai_json.get("message", "Aenderungen angewendet")
|
||||
new_objects = ai_json.get("objects", [])
|
||||
|
||||
if action == "info":
|
||||
return AIModifyResponse(message=message)
|
||||
|
||||
if action == "add" and new_objects:
|
||||
existing_objects = canvas_data.get("objects", [])
|
||||
existing_objects.extend(new_objects)
|
||||
canvas_data["objects"] = existing_objects
|
||||
return AIModifyResponse(
|
||||
modified_canvas_json=json.dumps(canvas_data),
|
||||
message=message
|
||||
)
|
||||
|
||||
if action == "modify" and new_objects:
|
||||
existing_objects = canvas_data.get("objects", [])
|
||||
new_ids = {obj.get("id") for obj in new_objects if obj.get("id")}
|
||||
kept_objects = [obj for obj in existing_objects if obj.get("id") not in new_ids]
|
||||
kept_objects.extend(new_objects)
|
||||
canvas_data["objects"] = kept_objects
|
||||
return AIModifyResponse(
|
||||
modified_canvas_json=json.dumps(canvas_data),
|
||||
message=message
|
||||
)
|
||||
|
||||
if action == "delete":
|
||||
delete_ids = ai_json.get("delete_ids", [])
|
||||
if delete_ids:
|
||||
existing_objects = canvas_data.get("objects", [])
|
||||
canvas_data["objects"] = [obj for obj in existing_objects if obj.get("id") not in delete_ids]
|
||||
return AIModifyResponse(
|
||||
modified_canvas_json=json.dumps(canvas_data),
|
||||
message=message
|
||||
)
|
||||
|
||||
return AIModifyResponse(message=message)
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
logger.error(f"Failed to parse AI JSON: {e}")
|
||||
return AIModifyResponse(
|
||||
message="Fehler beim Verarbeiten der KI-Antwort",
|
||||
error=str(e)
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"AI modify error: {e}")
|
||||
return AIModifyResponse(
|
||||
message="Ein unerwarteter Fehler ist aufgetreten",
|
||||
error=str(e)
|
||||
)
|
||||
|
||||
|
||||
def _handle_simple_modification(prompt: str, canvas_data: dict) -> AIModifyResponse:
|
||||
"""
|
||||
Handle simple modifications locally when Ollama is not available.
|
||||
Supports basic commands like adding headings, lines, etc.
|
||||
"""
|
||||
prompt_lower = prompt.lower()
|
||||
objects = canvas_data.get("objects", [])
|
||||
|
||||
def generate_id():
|
||||
return f"obj_{int(time.time()*1000)}_{random.randint(1000, 9999)}"
|
||||
|
||||
# Add heading
|
||||
if "ueberschrift" in prompt_lower or "titel" in prompt_lower or "heading" in prompt_lower:
|
||||
text_match = re.search(r'"([^"]+)"', prompt)
|
||||
text = text_match.group(1) if text_match else "Ueberschrift"
|
||||
|
||||
new_text = {
|
||||
"type": "i-text", "id": generate_id(), "text": text,
|
||||
"left": 397, "top": 50, "originX": "center",
|
||||
"fontFamily": "Arial", "fontSize": 28, "fontWeight": "bold", "fill": "#000000"
|
||||
}
|
||||
objects.append(new_text)
|
||||
canvas_data["objects"] = objects
|
||||
return AIModifyResponse(
|
||||
modified_canvas_json=json.dumps(canvas_data),
|
||||
message=f"Ueberschrift '{text}' hinzugefuegt"
|
||||
)
|
||||
|
||||
# Add lines for writing
|
||||
if "linie" in prompt_lower or "line" in prompt_lower or "schreib" in prompt_lower:
|
||||
num_match = re.search(r'(\d+)', prompt)
|
||||
num_lines = int(num_match.group(1)) if num_match else 5
|
||||
num_lines = min(num_lines, 20)
|
||||
|
||||
start_y = 150
|
||||
line_spacing = 40
|
||||
|
||||
for i in range(num_lines):
|
||||
new_line = {
|
||||
"type": "line", "id": generate_id(),
|
||||
"x1": 60, "y1": start_y + i * line_spacing,
|
||||
"x2": 734, "y2": start_y + i * line_spacing,
|
||||
"stroke": "#cccccc", "strokeWidth": 1
|
||||
}
|
||||
objects.append(new_line)
|
||||
|
||||
canvas_data["objects"] = objects
|
||||
return AIModifyResponse(
|
||||
modified_canvas_json=json.dumps(canvas_data),
|
||||
message=f"{num_lines} Schreiblinien hinzugefuegt"
|
||||
)
|
||||
|
||||
# Make text bigger
|
||||
if "groesser" in prompt_lower or "bigger" in prompt_lower or "larger" in prompt_lower:
|
||||
modified = 0
|
||||
for obj in objects:
|
||||
if obj.get("type") in ["i-text", "text", "textbox"]:
|
||||
current_size = obj.get("fontSize", 16)
|
||||
obj["fontSize"] = int(current_size * 1.25)
|
||||
modified += 1
|
||||
|
||||
canvas_data["objects"] = objects
|
||||
if modified > 0:
|
||||
return AIModifyResponse(
|
||||
modified_canvas_json=json.dumps(canvas_data),
|
||||
message=f"{modified} Texte vergroessert"
|
||||
)
|
||||
|
||||
# Center elements
|
||||
if "zentrier" in prompt_lower or "center" in prompt_lower or "mitte" in prompt_lower:
|
||||
center_x = 397
|
||||
for obj in objects:
|
||||
if not obj.get("isGrid"):
|
||||
obj["left"] = center_x
|
||||
obj["originX"] = "center"
|
||||
|
||||
canvas_data["objects"] = objects
|
||||
return AIModifyResponse(
|
||||
modified_canvas_json=json.dumps(canvas_data),
|
||||
message="Elemente zentriert"
|
||||
)
|
||||
|
||||
# Add numbering
|
||||
if "nummer" in prompt_lower or "nummerier" in prompt_lower or "1-10" in prompt_lower:
|
||||
range_match = re.search(r'(\d+)\s*[-bis]+\s*(\d+)', prompt)
|
||||
if range_match:
|
||||
start, end = int(range_match.group(1)), int(range_match.group(2))
|
||||
else:
|
||||
start, end = 1, 10
|
||||
|
||||
y = 100
|
||||
for i in range(start, min(end + 1, start + 20)):
|
||||
new_text = {
|
||||
"type": "i-text", "id": generate_id(), "text": f"{i}.",
|
||||
"left": 40, "top": y, "fontFamily": "Arial", "fontSize": 14, "fill": "#000000"
|
||||
}
|
||||
objects.append(new_text)
|
||||
y += 35
|
||||
|
||||
canvas_data["objects"] = objects
|
||||
return AIModifyResponse(
|
||||
modified_canvas_json=json.dumps(canvas_data),
|
||||
message=f"Nummerierung {start}-{end} hinzugefuegt"
|
||||
)
|
||||
|
||||
# Add rectangle/box
|
||||
if "rechteck" in prompt_lower or "box" in prompt_lower or "kasten" in prompt_lower:
|
||||
new_rect = {
|
||||
"type": "rect", "id": generate_id(),
|
||||
"left": 100, "top": 200, "width": 200, "height": 100,
|
||||
"fill": "transparent", "stroke": "#000000", "strokeWidth": 2
|
||||
}
|
||||
objects.append(new_rect)
|
||||
canvas_data["objects"] = objects
|
||||
return AIModifyResponse(
|
||||
modified_canvas_json=json.dumps(canvas_data),
|
||||
message="Rechteck hinzugefuegt"
|
||||
)
|
||||
|
||||
# Add grid/raster
|
||||
if "raster" in prompt_lower or "grid" in prompt_lower or "tabelle" in prompt_lower:
|
||||
dim_match = re.search(r'(\d+)\s*[x/\u00d7\*mal by]\s*(\d+)', prompt_lower)
|
||||
if dim_match:
|
||||
cols = int(dim_match.group(1))
|
||||
rows = int(dim_match.group(2))
|
||||
else:
|
||||
nums = re.findall(r'(\d+)', prompt)
|
||||
if len(nums) >= 2:
|
||||
cols, rows = int(nums[0]), int(nums[1])
|
||||
else:
|
||||
cols, rows = 3, 4
|
||||
|
||||
cols = min(max(1, cols), 10)
|
||||
rows = min(max(1, rows), 15)
|
||||
|
||||
canvas_width = 794
|
||||
canvas_height = 1123
|
||||
margin = 60
|
||||
available_width = canvas_width - 2 * margin
|
||||
available_height = canvas_height - 2 * margin - 80
|
||||
|
||||
cell_width = available_width / cols
|
||||
cell_height = min(available_height / rows, 80)
|
||||
|
||||
start_x = margin
|
||||
start_y = 120
|
||||
|
||||
grid_objects = []
|
||||
for r in range(rows + 1):
|
||||
y = start_y + r * cell_height
|
||||
grid_objects.append({
|
||||
"type": "line", "id": generate_id(),
|
||||
"x1": start_x, "y1": y,
|
||||
"x2": start_x + cols * cell_width, "y2": y,
|
||||
"stroke": "#666666", "strokeWidth": 1, "isGrid": True
|
||||
})
|
||||
|
||||
for c in range(cols + 1):
|
||||
x = start_x + c * cell_width
|
||||
grid_objects.append({
|
||||
"type": "line", "id": generate_id(),
|
||||
"x1": x, "y1": start_y,
|
||||
"x2": x, "y2": start_y + rows * cell_height,
|
||||
"stroke": "#666666", "strokeWidth": 1, "isGrid": True
|
||||
})
|
||||
|
||||
objects.extend(grid_objects)
|
||||
canvas_data["objects"] = objects
|
||||
return AIModifyResponse(
|
||||
modified_canvas_json=json.dumps(canvas_data),
|
||||
message=f"{cols}x{rows} Raster hinzugefuegt ({cols} Spalten, {rows} Zeilen)"
|
||||
)
|
||||
|
||||
# Default: Ollama needed
|
||||
return AIModifyResponse(
|
||||
message="Diese Aenderung erfordert den KI-Service. Bitte stellen Sie sicher, dass Ollama laeuft.",
|
||||
error="Complex modification requires Ollama"
|
||||
)
|
||||
Reference in New Issue
Block a user