| import os |
| import json |
| from PIL import Image, ImageDraw, ImageFont |
|
|
| DEMO_MODE = os.environ.get("LIFELOG_DEMO", "0") == "1" |
|
|
| |
| MODEL_TEXT = "openbmb/MiniCPM5-1B" |
| MODEL_VISION = "openbmb/MiniCPM-V-2_6" |
| MODEL_ASR = "openai/whisper-small" |
| MODEL_IMAGE = "black-forest-labs/FLUX.1-schnell" |
|
|
|
|
| def _gpu_decorator(duration=60): |
| try: |
| import spaces |
| return spaces.GPU(duration=duration) |
| except ImportError: |
| return lambda fn: fn |
|
|
|
|
| |
| |
| |
| _DEMO_FOLLOW_UPS = [ |
| ( |
| "That's a significant decision. What was the specific moment or event " |
| "that tipped the scales? Was there a single trigger, or has this been " |
| "building for a while?" |
| ), |
| ( |
| "I see. Let's stress-test this — what does the absolute worst-case " |
| "scenario look like if this doesn't work out? And on the flip side, " |
| "what's the best realistic outcome in six months?" |
| ), |
| ( |
| "Last question — who else is affected by this change? Are there " |
| "dependencies you need to manage — people counting on the old " |
| "arrangement, or opportunities blocked until this ships?" |
| ), |
| ] |
|
|
| _DEMO_CATEGORIZE = json.dumps({ |
| "category": "career", |
| "subcategory": "job_change", |
| "severity": 7, |
| "status_emoji": "🔧", |
| }) |
|
|
| _DEMO_PREDICT = json.dumps([ |
| { |
| "outcome": "Short-term financial pressure during the transition", |
| "probability": "high", |
| "valence": "negative", |
| "timeframe": "months", |
| }, |
| { |
| "outcome": "New growth opportunities and skill development", |
| "probability": "medium", |
| "valence": "positive", |
| "timeframe": "months", |
| }, |
| { |
| "outcome": "Stress and uncertainty while adjusting", |
| "probability": "high", |
| "valence": "negative", |
| "timeframe": "weeks", |
| }, |
| { |
| "outcome": "Improved long-term career satisfaction", |
| "probability": "medium", |
| "valence": "positive", |
| "timeframe": "years", |
| }, |
| ]) |
|
|
| _DEMO_CARD_PROMPT = ( |
| "A solitary figure standing at a crossroads in soft watercolor, one path " |
| "leading through a dense forest, the other opening to a sunlit meadow, " |
| "warm amber light breaking through clouds overhead" |
| ) |
|
|
| _DEMO_IMAGE_DESC = ( |
| "This appears to be a formal document with professional letterhead. " |
| "The key information suggests important correspondence regarding a " |
| "significant life decision or career change." |
| ) |
|
|
| _DEMO_PATTERN = """\ |
| ## 🔍 Debug Report: Life Pattern Analysis |
| |
| ### Recurring Patterns |
| - You tend to make major decisions after prolonged periods of dissatisfaction \ |
| rather than proactively. |
| - Career decisions show a pattern of choosing growth over stability. |
| - You process decisions emotionally first, then rationalize afterward. |
| |
| ### Category Distribution |
| Decisions are heavily weighted toward career (60%) with relationship decisions \ |
| as the second most common (20%). Work is your primary source of both \ |
| satisfaction and stress. |
| |
| ### Prediction Accuracy |
| Based on resolved decisions, predictions are ~65% accurate. You tend to \ |
| overestimate negative outcomes and underestimate how quickly you adapt. |
| |
| ### Risk Profile |
| **Moderate risk-taker.** You avoid purely speculative decisions but accept \ |
| significant uncertainty when the upside is clear. |
| |
| ### 🔧 Recommended Patch |
| Add a 72-hour cool-down for decisions with severity > 6. Your first instincts \ |
| are usually good, but stress-testing them before they ship to production would \ |
| catch edge cases.""" |
|
|
| |
| |
| |
| text_model = None |
| text_tokenizer = None |
| asr_pipe = None |
| vision_model = None |
| vision_tokenizer = None |
| image_pipe = None |
|
|
| if not DEMO_MODE: |
| import torch |
| from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
|
|
| print("[LifeLog] Loading text model…") |
| text_tokenizer = AutoTokenizer.from_pretrained( |
| MODEL_TEXT, trust_remote_code=True |
| ) |
| text_model = AutoModelForCausalLM.from_pretrained( |
| MODEL_TEXT, |
| torch_dtype=torch.bfloat16, |
| trust_remote_code=True, |
| device_map="auto", |
| ) |
|
|
| print("[LifeLog] Loading ASR model…") |
| asr_pipe = pipeline( |
| "automatic-speech-recognition", |
| model=MODEL_ASR, |
| torch_dtype=torch.float16, |
| device_map="auto", |
| ) |
|
|
| print("[LifeLog] Loading vision model…") |
| vision_tokenizer = AutoTokenizer.from_pretrained( |
| MODEL_VISION, trust_remote_code=True |
| ) |
| |
| |
| from transformers import PreTrainedModel |
| if not hasattr(PreTrainedModel, "all_tied_weights_keys"): |
| PreTrainedModel.all_tied_weights_keys = {} |
| vision_model = AutoModelForCausalLM.from_pretrained( |
| MODEL_VISION, |
| torch_dtype=torch.bfloat16, |
| trust_remote_code=True, |
| ) |
|
|
| print("[LifeLog] Loading image generation model…") |
| from diffusers import FluxPipeline |
|
|
| image_pipe = FluxPipeline.from_pretrained( |
| MODEL_IMAGE, torch_dtype=torch.bfloat16 |
| ) |
| image_pipe.enable_model_cpu_offload() |
|
|
| print("[LifeLog] All models loaded.") |
|
|
|
|
| |
| |
| |
|
|
| @_gpu_decorator(duration=60) |
| def generate_text(messages: list[dict], max_tokens: int = 512) -> str: |
| if DEMO_MODE: |
| last = messages[-1].get("content", "") if messages else "" |
| lower = last.lower() |
| if "consequence" in lower or ("predict" in lower and "json" in lower): |
| return _DEMO_PREDICT |
| if "category" in lower and "json" in lower: |
| return _DEMO_CATEGORIZE |
| if "image prompt" in lower or "moment card" in lower: |
| return _DEMO_CARD_PROMPT |
| if "pattern" in lower or "debug report" in lower: |
| return _DEMO_PATTERN |
| if "#1" in last: |
| return _DEMO_FOLLOW_UPS[0] |
| if "#2" in last: |
| return _DEMO_FOLLOW_UPS[1] |
| if "#3" in last: |
| return _DEMO_FOLLOW_UPS[2] |
| return _DEMO_FOLLOW_UPS[0] |
|
|
| text = text_tokenizer.apply_chat_template( |
| messages, tokenize=False, add_generation_prompt=True |
| ) |
| inputs = text_tokenizer([text], return_tensors="pt").to(text_model.device) |
| output_ids = text_model.generate( |
| **inputs, max_new_tokens=max_tokens, temperature=0.7, do_sample=True |
| ) |
| output_ids = output_ids[:, inputs.input_ids.shape[-1]:] |
| return text_tokenizer.decode(output_ids[0], skip_special_tokens=True) |
|
|
|
|
| @_gpu_decorator(duration=30) |
| def transcribe_audio(audio_path: str) -> str: |
| if DEMO_MODE: |
| return "I decided to leave my current job and pursue freelancing full-time." |
| result = asr_pipe(audio_path) |
| return result["text"] |
|
|
|
|
| @_gpu_decorator(duration=60) |
| def describe_image(image_path: str, question: str) -> str: |
| if DEMO_MODE: |
| return _DEMO_IMAGE_DESC |
|
|
| image = Image.open(image_path).convert("RGB") |
| msgs = [{"role": "user", "content": question}] |
| response = vision_model.chat( |
| image=image, msgs=msgs, tokenizer=vision_tokenizer |
| ) |
| return response |
|
|
|
|
| @_gpu_decorator(duration=120) |
| def generate_moment_card(prompt: str) -> Image.Image: |
| if DEMO_MODE: |
| img = Image.new("RGB", (512, 512), color=(22, 27, 34)) |
| draw = ImageDraw.Draw(img) |
| try: |
| font = ImageFont.truetype("arial.ttf", 18) |
| except OSError: |
| font = ImageFont.load_default() |
| draw.multiline_text( |
| (256, 230), |
| "Moment Card\n(Demo Mode)", |
| fill=(34, 197, 94), |
| font=font, |
| anchor="mm", |
| align="center", |
| ) |
| draw.rectangle([20, 20, 492, 492], outline=(48, 54, 61), width=2) |
| return img |
|
|
| import torch |
|
|
| image = image_pipe( |
| prompt=prompt, |
| height=512, |
| width=512, |
| guidance_scale=0.0, |
| num_inference_steps=4, |
| max_sequence_length=256, |
| generator=torch.Generator(device="cpu").manual_seed(0), |
| ).images[0] |
| return image |
|
|
