Update app.py

app.py

@@ -1,13 +1,15 @@
 # app.py — Affection 👁️ (Hugging Face Space)
 
 import os
-import gradio as gr
-import matplotlib.pyplot as plt
 
+# Disable Spaces reload BEFORE importing gradio
 os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
 os.environ["HF_HUB_DISABLE_TELEMETRY"] = "1"
 os.environ["SPACES_DISABLE_RELOAD"] = "1"
 
+import gradio as gr
+import matplotlib.pyplot as plt
+
 from utils.presets import EMOTION_PRESETS
 from utils.drama import apply_drama
 from utils.color_model import infer_color, render_color
@@ -36,17 +38,22 @@ def apply_passion(raw: dict, passion: float) -> dict:
 # ------------------------------------------------------------
 # Valence–Arousal Visualization (2D Projection)
 # ------------------------------------------------------------
-def generate_scatter(raw, amplified, cinematic, label, passion, drama):
+def generate_scatter(raw, amplified, cinematic, target, label, passion, drama):
 
     fig, ax = plt.subplots(figsize=(6, 6))
+    base_color = "#2C3E50"
 
-    base_color = "#2C3E50"  # neutral deep tone
-
-    # Plot cinematic anchors faintly
+    # ----------------------------------
+    # Background Cinematic Anchors
+    # ----------------------------------
     for _, preset in EMOTION_PRESETS.items():
         t = preset["target"]
         ax.scatter(t["V"], t["A"], alpha=0.1, s=90, color="#BBBBBB")
 
+    # ----------------------------------
+    # Active Emotional Trajectory
+    # ----------------------------------
+
     # Natural
     ax.scatter(
         raw["V"], raw["A"],
@@ -77,13 +84,75 @@ def generate_scatter(raw, amplified, cinematic, label, passion, drama):
         label="After Drama (Cinematic Alignment)"
     )
 
-    # Arrow 1 — Raw → Amplified
+    # Cinematic Anchor (Target)
+    ax.scatter(
+        target["V"],
+        target["A"],
+        s=160,
+        marker="X",
+        color="#E74C3C",
+        edgecolor="black",
+        linewidth=1.2,
+        label="Cinematic Anchor"
+    )
+
+    ax.text(
+        target["V"],
+        target["A"],
+        f"  {label} Target",
+        verticalalignment="center",
+        fontsize=9,
+        weight="bold",
+        color="#E74C3C"
+    )
+
+    # ----------------------------------
+    # Dynamic Zoom (20% padded, centered)
+    # ----------------------------------
+    xs = [
+        raw["V"],
+        amplified["V"],
+        cinematic["V"],
+        target["V"]
+    ]
+
+    ys = [
+        raw["A"],
+        amplified["A"],
+        cinematic["A"],
+        target["A"]
+    ]
+
+    min_x, max_x = min(xs), max(xs)
+    min_y, max_y = min(ys), max(ys)
+
+    span_x = max_x - min_x
+    span_y = max_y - min_y
+
+    span = max(span_x, span_y)
+    span = max(span, 0.05)  # prevent collapse
+
+    padding = span * 0.20
+    center_x = (min_x + max_x) / 2
+    center_y = (min_y + max_y) / 2
+    half_range = (span / 2) + padding
+
+    ax.set_xlim(center_x - half_range, center_x + half_range)
+    ax.set_ylim(center_y - half_range, center_y + half_range)
+    ax.set_aspect("equal", adjustable="box")
+
+    # ----------------------------------
+    # Proportional Arrow Heads
+    # ----------------------------------
+    arrow_head = span * 0.04
+
+    # Raw → Amplified
     ax.arrow(
         raw["V"],
         raw["A"],
         amplified["V"] - raw["V"],
         amplified["A"] - raw["A"],
-        head_width=0.02,
+        head_width=arrow_head,
         length_includes_head=True,
         color=base_color,
         linestyle="--",
@@ -91,13 +160,13 @@ def generate_scatter(raw, amplified, cinematic, label, passion, drama):
         alpha=0.6
     )
 
-    # Arrow 2 — Amplified → Cinematic
+    # Amplified → Cinematic
     ax.arrow(
         amplified["V"],
         amplified["A"],
         cinematic["V"] - amplified["V"],
         cinematic["A"] - amplified["A"],
-        head_width=0.02,
+        head_width=arrow_head,
         length_includes_head=True,
         color=base_color,
         linestyle="-",
@@ -105,36 +174,6 @@ def generate_scatter(raw, amplified, cinematic, label, passion, drama):
         alpha=0.9
     )
 
-    # ----------------------------------
-    # Dynamic Zoom (Centered + 20% Padding)
-    # ----------------------------------
-    xs = [raw["V"], amplified["V"], cinematic["V"]]
-    ys = [raw["A"], amplified["A"], cinematic["A"]]
-
-    min_x, max_x = min(xs), max(xs)
-    min_y, max_y = min(ys), max(ys)
-
-    span_x = max_x - min_x
-    span_y = max_y - min_y
-
-    # Use the larger span to keep square framing
-    span = max(span_x, span_y)
-
-    # Avoid zero-span collapse
-    span = max(span, 0.05)
-
-    padding = span * 0.20  # 20% larger
-
-    center_x = (min_x + max_x) / 2
-    center_y = (min_y + max_y) / 2
-
-    half_range = (span / 2) + padding
-
-    ax.set_xlim(center_x - half_range, center_x + half_range)
-    ax.set_ylim(center_y - half_range, center_y + half_range)
-
-    ax.set_aspect('equal', adjustable='box')
-    
     ax.set_xlabel("Valence")
     ax.set_ylabel("Arousal")
     ax.set_title(f"{label}\nPassion={round(passion,2)} | Drama={round(drama,2)}")
@@ -163,7 +202,15 @@ def run_pipeline(preset_name, passion, drama):
     color_params = infer_color(cinematic)
     color_block = render_color(color_params)
 
-    fig = generate_scatter(natural, amplified, cinematic, preset_name, passion, drama)
+    fig = generate_scatter(
+        natural,
+        amplified,
+        cinematic,
+        target,
+        preset_name,
+        passion,
+        drama
+    )
 
     return (
         text,
@@ -206,23 +253,17 @@ with gr.Blocks(title="Affection 👁️ — Edge Emotional Intelligence") as dem
 
     gr.Markdown(
         """
-This section provides a simplified visualization of a more complex on-device architecture.
-
-In hardware deployment, the NVIDIA Jetson Orin Nano performs all of the following:  
-
-• Robot hardware daemon service  
-• Interactive conversational application  
-• Real-time transcript ingestion   
-• VAD extraction (NRC-VAD lexicon)   
-• Structural language metrics (Complexity + Coherence)   
-• Radial emotional amplification (Passion)  
-• Cinematic nearest-exemplar alignment (Drama)  
-• Dual-timescale blending (fast burst + slow baseline via Nemotron/Ollama)  
-• Continuous emotional state streaming for display on an expression module  
-
 This demo isolates a single loop transformation for clarity.
 
-Our NVIDIA edge device is capable of running this loop 200x per second.
+Hardware deployment performs:
+• Transcript ingestion
+• VAD extraction
+• Structural metrics
+• Radial amplification (Passion)
+• Cinematic alignment (Drama)
+• Continuous emotional state streaming
+
+Edge loop capable of ~200Hz execution.
         """
     )
 
@@ -253,8 +294,8 @@ Our NVIDIA edge device is capable of running this loop 200x per second.
     gr.Markdown(
         """
 **Note:**  
-This plot shows a 2D Valence–Arousal projection for visualization only, but results are from the actual model.  
-Actual transformation and color inference are more complex and operate on the full 5D VAD+CC vector.
+This 2D VA projection is for visualization only.  
+The actual transformation operates in 5D VAD+CC space.
         """
     )
 
@@ -267,15 +308,10 @@ Actual transformation and color inference are more complex and operate on the fu
 
     gr.Markdown(
         """
-The finalized VAD+CC vector is transmitted to an expressive display module. In this example, we are converting to colors to be used for eyes.
-
-The module does not compute emotion.  
-It receives the 5D emotional state and runs a trained neural model to convert it into expressive color.
+VAD+CC → Embedded Model → Color Rendering
 
-Model used here (same as deployment):  
+Model:
 https://huggingface.co/danielritchie/vibe-color-model
-
-VAD+CC (Affect Engine) → Embedded Model → Color Rendering (Expression)
         """
     )
 
@@ -298,6 +334,7 @@ VAD+CC (Affect Engine) → Embedded Model → Color Rendering (Expression)
 
     demo.load(fn=run_pipeline, inputs=[preset_selector, passion, drama], outputs=outputs)
 
+
 demo.launch(
     server_name="0.0.0.0",
     server_port=7860,