Pure NumPy Forward Pass

requirements.txt

@@ -1,6 +1,5 @@
 gradio
 numpy
 matplotlib
-tensorflow
 huggingface_hub
 

utils/color_model.py

@@ -1,65 +1,60 @@
+import json
 import numpy as np
-import tensorflow as tf
 from huggingface_hub import hf_hub_download
 
-
-# Download model file from HF
-model_path = hf_hub_download(
+# Download weights
+weights_path = hf_hub_download(
     repo_id="danielritchie/vibe-color-model",
-    filename="vibe_model.tflite"   # confirm exact filename
+    filename="vibe_weights.json"
 )
 
-# Load TFLite interpreter
-interpreter = tf.lite.Interpreter(model_path=model_path)
-interpreter.allocate_tensors()
+with open(weights_path, "r") as f:
+    weights = json.load(f)
+
+# Extract layers
+W0 = np.array(weights["layer_0"]["weights"], dtype=np.float32)
+b0 = np.array(weights["layer_0"]["biases"], dtype=np.float32)
+
+W1 = np.array(weights["layer_1"]["weights"], dtype=np.float32)
+b1 = np.array(weights["layer_1"]["biases"], dtype=np.float32)
+
+W2 = np.array(weights["layer_2"]["weights"], dtype=np.float32)
+b2 = np.array(weights["layer_2"]["biases"], dtype=np.float32)
+
+
+def relu(x):
+    return np.maximum(0, x)
 
-input_details = interpreter.get_input_details()
-output_details = interpreter.get_output_details()
+
+def sigmoid(x):
+    return 1 / (1 + np.exp(-x))
 
 
 def infer_color(vad):
-    input_data = np.array([[
+    x = np.array([
         vad["V"],
         vad["A"],
         vad["D"],
         vad["Cx"],
         vad["Co"]
-    ]], dtype=np.float32)
+    ], dtype=np.float32)
 
-    interpreter.set_tensor(input_details[0]["index"], input_data)
-    interpreter.invoke()
-    output_data = interpreter.get_tensor(output_details[0]["index"])
+    # Layer 0
+    x = relu(np.dot(x, W0) + b0)
 
-    r, g, b, e, i = output_data[0]
+    # Layer 1
+    x = relu(np.dot(x, W1) + b1)
 
-    return {
-        "R": float(r),
-        "G": float(g),
-        "B": float(b),
-        "E": float(e),
-        "I": float(i)
-    }
+    # Layer 2
+    x = sigmoid(np.dot(x, W2) + b2)
 
+    r, g, b, e, i = x.tolist()
 
-def scale_rgb(rgb):
     return {
-        "R": int(max(0, min(255, rgb["R"] * 255))),
-        "G": int(max(0, min(255, rgb["G"] * 255))),
-        "B": int(max(0, min(255, rgb["B"] * 255))),
-        "E": rgb["E"],
-        "I": rgb["I"]
+        "R": r,
+        "G": g,
+        "B": b,
+        "E": e,
+        "I": i
     }
 
-
-def render_color(rgb):
-    return f"""
-    <div style="
-        width:100%;
-        height:240px;
-        border-radius:18px;
-        background: rgb({rgb['R']},{rgb['G']},{rgb['B']});
-        box-shadow: 0px 6px 32px rgba(0,0,0,0.25);
-        transition: all 0.3s ease-in-out;
-    "></div>
-    """
-