Auto-update: Mon Jul 1 20:48:53 PDT 2024
This commit is contained in:
sanj
parent
79d139312a
commit
acb5e0ccaa
16 changed files with 140 additions and 110 deletions
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@ -1,58 +1 @@
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import requests
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import os
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import json
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filename = 'location_log.json'
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server = '!{!{ ENTER A PUBLIC URL TO YOUR SIJAPI INSTANCE }!}!'
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api_key = '!{!{ ENTER YOUR GLOBAL_API_KEY HERE }!}!'
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def upload_location_data(data):
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headers = {
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'Authorization': f'Bearer {api_key}',
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'Content-Type': 'application/json'
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}
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try:
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response = requests.post(f'{server}/locate', json=data, headers=headers)
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if response.status_code == 200:
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print('Location and weather updated successfully.')
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os.remove(filename)
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else:
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print(f'Failed to post data. Status code: {response.status_code}')
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print(response.text)
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except requests.RequestException as e:
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print(f'Error posting data: {e}')
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if not os.path.exists(filename):
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print('No data to upload.')
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else:
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try:
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with open(filename, 'r') as f:
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data = json.load(f)
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# Ensure all datetime fields are correctly named and add default context if missing
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for location in data:
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if 'date' in location:
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location['datetime'] = location.pop('date')
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# Ensure context dictionary exists with all required keys
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if 'context' not in location:
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location['context'] = {
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'action': 'manual',
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'device_type': 'Pythonista',
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'device_model': None,
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'device_name': None,
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'device_os': None
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}
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else:
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context = location['context']
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context.setdefault('action', 'manual')
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context.setdefault('device_type', 'Pythonista')
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context.setdefault('device_model', None)
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context.setdefault('device_name', None)
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context.setdefault('device_os', None)
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upload_location_data(data)
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except FileNotFoundError:
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print(f'File {filename} not found.')
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except json.JSONDecodeError:
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print(f'Error decoding JSON from {filename}.')
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except Exception as e:
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print(f'Unexpected error: {e}')
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how
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@ -1,3 +1,4 @@
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# __init__.py
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import os
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import json
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import yaml
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@ -1,4 +1,5 @@
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#!/Users/sij/miniforge3/envs/api/bin/python
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#__main__.py
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from fastapi import FastAPI, Request, HTTPException, Response
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from fastapi.responses import JSONResponse
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from fastapi.middleware.cors import CORSMiddleware
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@ -1,3 +1,4 @@
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# classes.py
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import asyncio
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import json
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import math
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58
sijapi/cli.py
Normal file
58
sijapi/cli.py
Normal file
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@ -0,0 +1,58 @@
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# cli.py
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import click
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import asyncio
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from datetime import datetime as dt_datetime, timedelta
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# Import your async functions and dependencies
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from your_main_app import build_daily_note_range_endpoint, loc
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def async_command(f):
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@click.command()
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@click.pass_context
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def wrapper(ctx, *args, **kwargs):
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async def run():
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return await f(*args, **kwargs)
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return asyncio.run(run())
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return wrapper
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@click.group()
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def cli():
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"""CLI for your application."""
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pass
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@cli.command()
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@click.argument('dt_start')
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@click.argument('dt_end')
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@async_command
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async def bulk_note_range(dt_start: str, dt_end: str):
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"""
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Build daily notes for a date range.
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DT_START and DT_END should be in YYYY-MM-DD format.
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"""
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try:
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start_date = dt_datetime.strptime(dt_start, "%Y-%m-%d")
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end_date = dt_datetime.strptime(dt_end, "%Y-%m-%d")
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except ValueError:
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click.echo("Error: Dates must be in YYYY-MM-DD format.")
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return
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if start_date > end_date:
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click.echo("Error: Start date must be before or equal to end date.")
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return
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results = []
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current_date = start_date
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while current_date <= end_date:
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formatted_date = await loc.dt(current_date)
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result = await build_daily_note(formatted_date)
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results.append(result)
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current_date += timedelta(days=1)
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click.echo("Generated notes for the following dates:")
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for url in results:
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click.echo(url)
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if __name__ == '__main__':
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cli()
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@ -1,22 +1,34 @@
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from aura_sr import AuraSR
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from PIL import Image
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import torch
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import os
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aura_sr = AuraSR.from_pretrained("fal-ai/AuraSR")
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# Set environment variables for MPS
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os.environ['PYTORCH_MPS_HIGH_WATERMARK_RATIO'] = '0.0'
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os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1'
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# Initialize device as CPU for default
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device = torch.device('cpu')
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# Check if MPS is available
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if torch.backends.mps.is_available():
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if not torch.backends.mps.is_built():
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print("MPS not available because the current PyTorch install was not built with MPS enabled.")
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else:
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device = torch.device('mps:0')
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# Overwrite the default CUDA device with MPS
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torch.cuda.default_stream = device
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aura_sr = AuraSR.from_pretrained("fal-ai/AuraSR").to(device)
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def load_image_from_path(file_path):
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# Open image file
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return Image.open(file_path)
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def upscale_and_save(original_path):
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# load the image from the path
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original_image = load_image_from_path(original_path)
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# upscale the image using the pretrained model
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upscaled_image = aura_sr.upscale_4x(original_image)
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# save the upscaled image back to the original path
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# Overwrite the original image
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upscaled_image.save(original_path)
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# Now to use the function, provide the image path
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# Insert your image path
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upscale_and_save("/Users/sij/workshop/sijapi/sijapi/testbed/API__00482_ 2.png")
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@ -1,3 +1,4 @@
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# logs.py
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import os
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import sys
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from loguru import logger
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@ -1,6 +1,7 @@
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'''
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Uses whisper_cpp to create an OpenAI-compatible Whisper web service.
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'''
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# routers/asr.py
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import os
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import sys
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import uuid
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@ -66,7 +66,7 @@ IG_VISION_LLM = os.getenv("IG_VISION_LLM")
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IG_PROMPT_LLM = os.getenv("IG_PROMPT_LLM")
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IG_IMG_GEN = os.getenv("IG_IMG_GEN", "ComfyUI")
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IG_OUTPUT_PLATFORMS = os.getenv("IG_OUTPUT_PLATFORMS", "ig,ghost,obsidian").split(',')
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SD_WORKFLOWS_DIR = os.path.join(COMFYUI_DIR, 'workflows')
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IMG_WORKFLOWS_DIR = os.path.join(COMFYUI_DIR, 'workflows')
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COMFYUI_OUTPUT_DIR = COMFYUI_DIR / 'output'
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IG_PROFILES_DIR = os.path.join(BASE_DIR, 'profiles')
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IG_PROFILE_DIR = os.path.join(IG_PROFILES_DIR, PROFILE)
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@ -793,7 +793,7 @@ def load_json(json_payload, workflow):
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if json_payload:
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return json.loads(json_payload)
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elif workflow:
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workflow_path = os.path.join(SD_WORKFLOWS_DIR, f"{workflow}.json" if not workflow.endswith('.json') else workflow)
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workflow_path = os.path.join(IMG_WORKFLOWS_DIR, f"{workflow}.json" if not workflow.endswith('.json') else workflow)
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with open(workflow_path, 'r') as file:
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return json.load(file)
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else:
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@ -2,7 +2,7 @@
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Image generation module using StableDiffusion and similar models by way of ComfyUI.
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DEPENDS ON:
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LLM module
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COMFYUI_URL, COMFYUI_DIR, COMFYUI_OUTPUT_DIR, TS_SUBNET, TS_ADDRESS, DATA_DIR, SD_CONFIG_DIR, SD_IMAGE_DIR, SD_WORKFLOWS_DIR, LOCAL_HOSTS, API.URL, PHOTOPRISM_USER*, PHOTOPRISM_URL*, PHOTOPRISM_PASS*
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COMFYUI_URL, COMFYUI_DIR, COMFYUI_OUTPUT_DIR, TS_SUBNET, TS_ADDRESS, DATA_DIR, IMG_CONFIG_DIR, IMG_DIR, IMG_WORKFLOWS_DIR, LOCAL_HOSTS, API.URL, PHOTOPRISM_USER*, PHOTOPRISM_URL*, PHOTOPRISM_PASS*
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*unimplemented.
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'''
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@ -30,7 +30,7 @@ import shutil
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# from photoprism.Photo import Photo
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# from webdav3.client import Client
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from sijapi.routers.llm import query_ollama
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from sijapi import API, L, COMFYUI_URL, COMFYUI_OUTPUT_DIR, SD_CONFIG_PATH, SD_IMAGE_DIR, SD_WORKFLOWS_DIR
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from sijapi import API, L, COMFYUI_URL, COMFYUI_OUTPUT_DIR, IMG_CONFIG_PATH, IMG_DIR, IMG_WORKFLOWS_DIR
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img = APIRouter()
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@ -86,7 +86,7 @@ async def workflow(prompt: str, scene: str = None, size: str = None, earlyout: s
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width, height = map(int, size.split('x'))
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L.DEBUG(f"Parsed width: {width}; parsed height: {height}")
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workflow_path = Path(SD_WORKFLOWS_DIR) / scene_workflow['workflow']
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workflow_path = Path(IMG_WORKFLOWS_DIR) / scene_workflow['workflow']
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workflow_data = json.loads(workflow_path.read_text())
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post = {
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@ -104,7 +104,7 @@ async def workflow(prompt: str, scene: str = None, size: str = None, earlyout: s
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print(f"Prompt ID: {prompt_id}")
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max_size = max(width, height) if downscale_to_fit else None
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destination_path = Path(destination_path).with_suffix(".jpg") if destination_path else SD_IMAGE_DIR / f"{prompt_id}.jpg"
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destination_path = Path(destination_path).with_suffix(".jpg") if destination_path else IMG_DIR / f"{prompt_id}.jpg"
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if earlyout:
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asyncio.create_task(generate_and_save_image(prompt_id, saved_file_key, max_size, destination_path))
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@ -132,7 +132,7 @@ async def generate_and_save_image(prompt_id, saved_file_key, max_size, destinati
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def get_web_path(file_path: Path) -> str:
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uri = file_path.relative_to(SD_IMAGE_DIR)
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uri = file_path.relative_to(IMG_DIR)
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web_path = f"{API.URL}/img/{uri}"
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return web_path
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@ -174,8 +174,8 @@ async def get_image(status_data, key):
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async def save_as_jpg(image_data, prompt_id, max_size = None, quality = 100, destination_path: Path = None):
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destination_path_png = (SD_IMAGE_DIR / prompt_id).with_suffix(".png")
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destination_path_jpg = destination_path.with_suffix(".jpg") if destination_path else (SD_IMAGE_DIR / prompt_id).with_suffix(".jpg")
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destination_path_png = (IMG_DIR / prompt_id).with_suffix(".png")
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destination_path_jpg = destination_path.with_suffix(".jpg") if destination_path else (IMG_DIR / prompt_id).with_suffix(".jpg")
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try:
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destination_path_png.parent.mkdir(parents=True, exist_ok=True)
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@ -224,16 +224,16 @@ def set_presets(workflow_data, preset_values):
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def get_return_path(destination_path):
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sd_dir = Path(SD_IMAGE_DIR)
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sd_dir = Path(IMG_DIR)
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if destination_path.parent.samefile(sd_dir):
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return destination_path.name
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else:
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return str(destination_path)
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def get_scene(scene):
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with open(SD_CONFIG_PATH, 'r') as SD_CONFIG_file:
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SD_CONFIG = yaml.safe_load(SD_CONFIG_file)
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for scene_data in SD_CONFIG['scenes']:
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with open(IMG_CONFIG_PATH, 'r') as IMG_CONFIG_file:
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IMG_CONFIG = yaml.safe_load(IMG_CONFIG_file)
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for scene_data in IMG_CONFIG['scenes']:
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if scene_data['scene'] == scene:
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L.DEBUG(f"Found scene for \"{scene}\".")
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return scene_data
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@ -246,9 +246,9 @@ def get_matching_scene(prompt):
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prompt_lower = prompt.lower()
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max_count = 0
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scene_data = None
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with open(SD_CONFIG_PATH, 'r') as SD_CONFIG_file:
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SD_CONFIG = yaml.safe_load(SD_CONFIG_file)
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for sc in SD_CONFIG['scenes']:
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with open(IMG_CONFIG_PATH, 'r') as IMG_CONFIG_file:
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IMG_CONFIG = yaml.safe_load(IMG_CONFIG_file)
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for sc in IMG_CONFIG['scenes']:
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count = sum(1 for trigger in sc['triggers'] if trigger in prompt_lower)
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if count > max_count:
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max_count = count
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@ -259,7 +259,7 @@ def get_matching_scene(prompt):
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return scene_data
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else:
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L.DEBUG(f"No matching scenes found, falling back to default scene.")
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return SD_CONFIG['scenes'][0]
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return IMG_CONFIG['scenes'][0]
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@ -400,7 +400,7 @@ async def get_generation_options():
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async def load_workflow(workflow_path: str, workflow:str):
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workflow_path = workflow_path if workflow_path else os.path.join(SD_WORKFLOWS_DIR, f"{workflow}.json" if not workflow.endswith('.json') else workflow)
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workflow_path = workflow_path if workflow_path else os.path.join(IMG_WORKFLOWS_DIR, f"{workflow}.json" if not workflow.endswith('.json') else workflow)
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with open(workflow_path, 'r') as file:
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return json.load(file)
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'''
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Interfaces with Ollama and creates an OpenAI-compatible relay API.
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'''
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# routers/llm.py
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from fastapi import APIRouter, HTTPException, Request, Response, BackgroundTasks, File, Form, UploadFile
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from fastapi.responses import StreamingResponse, JSONResponse, FileResponse
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from datetime import datetime as dt_datetime
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# routers/news.py
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import os
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import uuid
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import asyncio
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@ -90,7 +91,7 @@ async def download_and_save_article(article, site_name, earliest_date, bg_tasks:
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try:
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banner_url = article.top_image
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if banner_url:
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banner_image = download_file(banner_url, Path(OBSIDIAN_VAULT_DIR / OBSIDIAN_RESOURCES_DIR))
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banner_image = download_file(banner_url, Path(OBSIDIAN_VAULT_DIR / OBSIDIAN_RESOURCES_DIR / f"{dt_datetime.now().strftime('%Y%m%d%H%M%S')}.jpg"))
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if banner_image:
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banner_markdown = f"![[{OBSIDIAN_RESOURCES_DIR}/{banner_image}]]"
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except Exception as e:
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@ -120,7 +121,7 @@ tags:
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bg_tasks=bg_tasks,
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text=tts_text,
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voice=voice,
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model="eleven_turbo_v2",
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model="xtts2",
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podcast=True,
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title=audio_filename,
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output_dir=Path(OBSIDIAN_VAULT_DIR) / OBSIDIAN_RESOURCES_DIR
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@ -176,7 +177,7 @@ async def process_news_site(site, bg_tasks: BackgroundTasks):
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earliest_date,
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bg_tasks,
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tts_mode=site.tts if hasattr(site, 'tts') else "off",
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voice=site.tts if hasattr(site, 'tts') else DEFAULT_11L_VOICE
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voice=site.voice if hasattr(site, 'voice') else DEFAULT_11L_VOICE
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))
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tasks.append(task)
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@ -237,12 +238,11 @@ async def archive_post(
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async def clip_get(
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bg_tasks: BackgroundTasks,
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url: str,
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title: Optional[str] = Query(None),
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encoding: str = Query('utf-8'),
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tts: str = Query('summary'),
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voice: str = Query(DEFAULT_VOICE)
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):
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markdown_filename = await process_article(bg_tasks, url, title, encoding, tts=tts, voice=voice)
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parsed_content = await parse_article(url)
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markdown_filename = await process_article(bg_tasks, parsed_content, tts, voice)
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return {"message": "Clip saved successfully", "markdown_filename": markdown_filename}
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@news.post("/note/add")
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@ -1,6 +1,7 @@
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'''
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Manages an Obsidian vault, in particular daily notes, using information and functionality drawn from the other routers, primarily calendar, email, ig, llm, rag, img, serve, time, tts, and weather.
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'''
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# routers/note.py
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from fastapi import APIRouter, BackgroundTasks, File, UploadFile, Form, HTTPException, Response, Query, Path as FastAPIPath
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from fastapi.responses import JSONResponse, PlainTextResponse
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import os, re
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@ -30,7 +30,7 @@ from selenium.webdriver.support import expected_conditions as EC
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from sijapi import (
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L, LOGS_DIR, TS_ID, CASETABLE_PATH, COURTLISTENER_DOCKETS_URL, COURTLISTENER_API_KEY,
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COURTLISTENER_BASE_URL, COURTLISTENER_DOCKETS_DIR, COURTLISTENER_SEARCH_DIR, ALERTS_DIR,
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MAC_UN, MAC_PW, MAC_ID, TS_TAILNET, DATA_DIR, SD_IMAGE_DIR, PUBLIC_KEY, OBSIDIAN_VAULT_DIR
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MAC_UN, MAC_PW, MAC_ID, TS_TAILNET, DATA_DIR, IMG_DIR, PUBLIC_KEY, OBSIDIAN_VAULT_DIR
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)
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from sijapi.utilities import bool_convert, sanitize_filename, assemble_journal_path
|
||||
from sijapi.routers import loc, note
|
||||
|
|
@ -44,7 +44,7 @@ async def get_pgp():
|
|||
|
||||
@serve.get("/img/{image_name}")
|
||||
def serve_image(image_name: str):
|
||||
image_path = os.path.join(SD_IMAGE_DIR, image_name)
|
||||
image_path = os.path.join(IMG_DIR, image_name)
|
||||
if os.path.exists(image_path):
|
||||
return FileResponse(image_path)
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@ from typing import Optional, Union, List
|
|||
from pydub import AudioSegment
|
||||
from TTS.api import TTS
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
from datetime import datetime as dt_datetime
|
||||
from time import time
|
||||
import torch
|
||||
import traceback
|
||||
|
|
@ -66,21 +66,26 @@ async def list_11l_voices():
|
|||
|
||||
|
||||
|
||||
|
||||
def select_voice(voice_name: str) -> str:
|
||||
async def select_voice(voice_name: str) -> str:
|
||||
try:
|
||||
voice_file = VOICE_DIR / f"{voice_name}.wav"
|
||||
L.DEBUG(f"select_voice received query to use voice: {voice_name}. Looking for {voice_file} inside {VOICE_DIR}.")
|
||||
# Case Insensitive comparison
|
||||
voice_name_lower = voice_name.lower()
|
||||
L.DEBUG(f"Looking for {voice_name_lower}")
|
||||
for item in VOICE_DIR.iterdir():
|
||||
L.DEBUG(f"Checking {item.name.lower()}")
|
||||
if item.name.lower() == f"{voice_name_lower}.wav":
|
||||
L.DEBUG(f"select_voice received query to use voice: {voice_name}. Found {item} inside {VOICE_DIR}.")
|
||||
return str(item)
|
||||
|
||||
if voice_file.is_file():
|
||||
return str(voice_file)
|
||||
else:
|
||||
L.ERR(f"Voice file not found")
|
||||
raise HTTPException(status_code=404, detail="Voice file not found")
|
||||
|
||||
except Exception as e:
|
||||
L.ERR(f"Voice file not found: {str(e)}")
|
||||
return None
|
||||
|
||||
|
||||
|
||||
@tts.post("/tts")
|
||||
@tts.post("/tts/speak")
|
||||
@tts.post("/v1/audio/speech")
|
||||
|
|
@ -132,13 +137,14 @@ async def generate_speech(
|
|||
|
||||
try:
|
||||
model = model if model else await get_model(voice, voice_file)
|
||||
|
||||
title = title if title else "TTS audio"
|
||||
output_path = output_dir / f"{dt_datetime.now().strftime('%Y%m%d%H%M%S')} {title}.wav"
|
||||
if model == "eleven_turbo_v2":
|
||||
L.INFO("Using ElevenLabs.")
|
||||
audio_file_path = await elevenlabs_tts(model, text, voice, title, output_dir)
|
||||
else: # if model == "xtts":
|
||||
L.INFO("Using XTTS2")
|
||||
audio_file_path = await local_tts(text, speed, voice, voice_file, podcast, bg_tasks, title, output_dir)
|
||||
audio_file_path = await local_tts(text, speed, voice, voice_file, podcast, bg_tasks, title, output_path)
|
||||
#else:
|
||||
# raise HTTPException(status_code=400, detail="Invalid model specified")
|
||||
|
||||
|
|
@ -158,7 +164,7 @@ async def generate_speech(
|
|||
|
||||
|
||||
async def get_model(voice: str = None, voice_file: UploadFile = None):
|
||||
if voice_file or (voice and select_voice(voice)):
|
||||
if voice_file or (voice and await select_voice(voice)):
|
||||
return "xtts"
|
||||
|
||||
elif voice and await determine_voice_id(voice):
|
||||
|
|
@ -220,7 +226,7 @@ async def elevenlabs_tts(model: str, input_text: str, voice: str, title: str = N
|
|||
async with httpx.AsyncClient(timeout=httpx.Timeout(300.0)) as client: # 5 minutes timeout
|
||||
response = await client.post(url, json=payload, headers=headers)
|
||||
output_dir = output_dir if output_dir else TTS_OUTPUT_DIR
|
||||
title = title if title else datetime.now().strftime("%Y%m%d%H%M%S")
|
||||
title = title if title else dt_datetime.now().strftime("%Y%m%d%H%M%S")
|
||||
filename = f"{sanitize_filename(title)}.mp3"
|
||||
file_path = Path(output_dir) / filename
|
||||
if response.status_code == 200:
|
||||
|
|
@ -245,7 +251,9 @@ async def get_text_content(text: Optional[str], file: Optional[UploadFile]) -> s
|
|||
|
||||
async def get_voice_file_path(voice: str = None, voice_file: UploadFile = None) -> str:
|
||||
if voice:
|
||||
return select_voice(voice)
|
||||
L.DEBUG(f"Looking for voice: {voice}")
|
||||
selected_voice = await select_voice(voice)
|
||||
return selected_voice
|
||||
elif voice_file and isinstance(voice_file, UploadFile):
|
||||
VOICE_DIR.mkdir(exist_ok=True)
|
||||
|
||||
|
|
@ -272,8 +280,9 @@ async def get_voice_file_path(voice: str = None, voice_file: UploadFile = None)
|
|||
return str(new_file)
|
||||
|
||||
else:
|
||||
L.DEBUG(f"{datetime.now().strftime('%Y%m%d%H%M%S')}: No voice specified or file provided, using default voice: {DEFAULT_VOICE}")
|
||||
return select_voice(DEFAULT_VOICE)
|
||||
L.DEBUG(f"{dt_datetime.now().strftime('%Y%m%d%H%M%S')}: No voice specified or file provided, using default voice: {DEFAULT_VOICE}")
|
||||
selected_voice = await select_voice(DEFAULT_VOICE)
|
||||
return selected_voice
|
||||
|
||||
|
||||
|
||||
|
|
@ -290,7 +299,7 @@ async def local_tts(
|
|||
if output_path:
|
||||
file_path = Path(output_path)
|
||||
else:
|
||||
datetime_str = datetime.now().strftime("%Y%m%d%H%M%S")
|
||||
datetime_str = dt_datetime.now().strftime("%Y%m%d%H%M%S")
|
||||
title = sanitize_filename(title) if title else "Audio"
|
||||
filename = f"{datetime_str}_{title}.wav"
|
||||
file_path = TTS_OUTPUT_DIR / filename
|
||||
|
|
|
|||
|
|
@ -1,3 +1,4 @@
|
|||
# utilities.py
|
||||
import re
|
||||
import os
|
||||
from fastapi import Form
|
||||
|
|
|
|||
Loading…
Reference in a new issue