utils/color_model.py

import numpy as np
import tensorflow as tf
from huggingface_hub import hf_hub_download

# Download .h5 model from HF
model_path = hf_hub_download(
    repo_id="danielritchie/vibe-color-model",
    filename="vibe_model.h5"
)

# Load Keras model
model = tf.keras.models.load_model(model_path, compile=False)


# -------------------------------------------------
# Model Inference
# -------------------------------------------------

def infer_color(vad):
    input_data = np.array([[
        vad["V"],
        vad["A"],
        vad["D"],
        vad["Cx"],
        vad["Co"]
    ]], dtype=np.float32)

    output = model.predict(input_data, verbose=0)[0]
    r, g, b, e, i = output

    return {
        "R": float(r),
        "G": float(g),
        "B": float(b),
        "E": float(e),  # Energy
        "I": float(i)   # Intensity
    }


# -------------------------------------------------
# Cinematic Drama Rendering
# -------------------------------------------------

def apply_cinematic_blend(model_output, drama):
    """
    drama:
        0.0  → near white
        1.0  → cinematic target
        1.5  → expressive peak
    """

    color = np.array([
        model_output["R"],
        model_output["G"],
        model_output["B"]
    ])

    energy = model_output["E"]
    intensity = model_output["I"]

    white = np.array([1.0, 1.0, 1.0])

    # Nonlinear cinematic curve
    curve = drama ** 2.2

    # Blend strength influenced by model intensity
    strength = np.clip(curve * intensity, 0.0, 1.0)

    # Blend toward white baseline
    blended = white * (1 - strength) + color * strength

    # Subtle energy-based brightness shaping
    brightness_boost = 0.9 + (0.25 * energy)
    blended = blended * brightness_boost

    blended = np.clip(blended, 0.0, 1.0)

    return {
        "R": int(blended[0] * 255),
        "G": int(blended[1] * 255),
        "B": int(blended[2] * 255),
        "E": energy,
        "I": intensity
    }


# -------------------------------------------------
# Render HTML Block
# -------------------------------------------------

def render_color(model_output):

    # Convert 0–1 floats to 0–255 integers
    r = int(max(0, min(255, model_output["R"] * 255)))
    g = int(max(0, min(255, model_output["G"] * 255)))
    b = int(max(0, min(255, model_output["B"] * 255)))

    return f"""
    <div style="
        width:100%;
        height:240px;
        border-radius:18px;
        background: rgb({r},{g},{b});
        box-shadow: 0px 6px 32px rgba(0,0,0,0.25);
        transition: all 0.35s cubic-bezier(.4,0,.2,1);
    "></div>
    """