Files
Odoo-Modules/fusion-woo-odoo/fusion_woocommerce/lib/ai_service.py
gsinghpal f759bf558f feat: add AI service (Claude + OpenAI) and image processor with EXIF geo-tagging
AIService wraps both Anthropic Claude and OpenAI APIs for product content
generation. ImageProcessor handles EXIF geo-tagging with company info and
GPS coordinates using piexif.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-01 14:39:44 -04:00

172 lines
6.7 KiB
Python

import json
import logging
_logger = logging.getLogger(__name__)
class AIService:
"""AI content generation service supporting Claude and OpenAI."""
def __init__(self, provider, api_key, model=None):
"""
Args:
provider: 'claude' or 'openai'
api_key: API key for the chosen provider
model: Model name (defaults to claude-sonnet-4-5-20250514 or gpt-4o)
"""
self.provider = provider
self.api_key = api_key
if model:
self.model = model
else:
self.model = 'claude-sonnet-4-5-20250514' if provider == 'claude' else 'gpt-4o'
self._client = None
def _get_client(self):
if self._client:
return self._client
if self.provider == 'claude':
try:
import anthropic
self._client = anthropic.Anthropic(api_key=self.api_key)
except ImportError:
raise RuntimeError("anthropic package not installed. Run: pip install anthropic")
elif self.provider == 'openai':
try:
import openai
self._client = openai.OpenAI(api_key=self.api_key)
except ImportError:
raise RuntimeError("openai package not installed. Run: pip install openai")
return self._client
def generate(self, system_prompt, user_message, max_tokens=2000):
"""Generate text using the configured AI provider."""
client = self._get_client()
try:
if self.provider == 'claude':
response = client.messages.create(
model=self.model,
max_tokens=max_tokens,
system=system_prompt,
messages=[{"role": "user", "content": user_message}],
)
return response.content[0].text
elif self.provider == 'openai':
response = client.chat.completions.create(
model=self.model,
max_tokens=max_tokens,
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_message},
],
)
return response.choices[0].message.content
except Exception as e:
_logger.error("AI generation failed (%s): %s", self.provider, str(e))
raise
def generate_product_content(self, product_info, prompts):
"""Generate all product content at once.
Args:
product_info: dict with keys like name, category, features, raw_description
prompts: dict with keys: title, short_desc, long_desc, meta_title, meta_desc, keywords
Returns:
dict with generated content for each field
"""
context = json.dumps(product_info, indent=2)
system = (
"You are an expert e-commerce copywriter and SEO specialist. "
"You create compelling, SEO-optimized product content for online stores. "
"Always respond with valid JSON containing the requested fields. "
"HTML descriptions should use proper semantic HTML tags."
)
user_msg = f"""Based on this product information:
{context}
Generate the following content as a JSON object with these exact keys:
1. "title": {prompts.get('title', 'SEO-optimized product title in Title Case')}
2. "short_description": {prompts.get('short_desc', 'Compelling 2-3 sentence HTML summary')}
3. "long_description": {prompts.get('long_desc', 'Detailed HTML product description with headings and lists')}
4. "meta_title": {prompts.get('meta_title', 'SEO meta title under 60 characters')}
5. "meta_description": {prompts.get('meta_desc', 'SEO meta description under 160 characters')}
6. "keywords": {prompts.get('keywords', 'Comma-separated SEO keywords')}
Respond ONLY with the JSON object, no markdown formatting."""
try:
raw = self.generate(system, user_msg, max_tokens=3000)
# Try to parse JSON from the response
# Strip any markdown code fences
cleaned = raw.strip()
if cleaned.startswith('```'):
cleaned = cleaned.split('\n', 1)[1]
if cleaned.endswith('```'):
cleaned = cleaned[:-3]
cleaned = cleaned.strip()
return json.loads(cleaned)
except json.JSONDecodeError:
_logger.warning("AI returned non-JSON response, returning raw text")
return {
'title': raw[:200] if raw else '',
'short_description': '',
'long_description': raw or '',
'meta_title': '',
'meta_description': '',
'keywords': '',
}
def generate_single_field(self, product_info, prompt, field_name):
"""Generate a single field using the given prompt."""
context = json.dumps(product_info, indent=2)
system = (
"You are an expert e-commerce copywriter and SEO specialist. "
"Respond with ONLY the requested content, no explanations or formatting."
)
user_msg = f"Product info:\n{context}\n\nTask: {prompt}"
result = self.generate(system, user_msg, max_tokens=1500)
return result.strip() if result else ''
def generate_image_metadata(self, product_name, product_category, prompt_alt, prompt_caption):
"""Generate SEO metadata for a product image.
Returns:
dict with: alt_text, caption, title, description
"""
system = (
"You are an SEO specialist for e-commerce product images. "
"Generate metadata that helps with image SEO and accessibility. "
"Respond ONLY with a JSON object."
)
user_msg = f"""Product: {product_name}
Category: {product_category}
Generate image metadata as JSON:
- "alt_text": {prompt_alt} (under 125 characters)
- "caption": {prompt_caption}
- "title": SEO-optimized image title
- "description": Descriptive image text for SEO
Respond ONLY with the JSON object."""
try:
raw = self.generate(system, user_msg, max_tokens=500)
cleaned = raw.strip()
if cleaned.startswith('```'):
cleaned = cleaned.split('\n', 1)[1]
if cleaned.endswith('```'):
cleaned = cleaned[:-3]
cleaned = cleaned.strip()
return json.loads(cleaned)
except (json.JSONDecodeError, Exception):
return {
'alt_text': product_name,
'caption': product_name,
'title': product_name,
'description': product_name,
}