Update app.py

app.py

@@ -1,15 +1,13 @@
 # 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
@@ -38,22 +36,17 @@ def apply_passion(raw: dict, passion: float) -> dict:
 # ------------------------------------------------------------
 # Valence–Arousal Visualization (2D Projection)
 # ------------------------------------------------------------
-def generate_scatter(raw, amplified, cinematic, target, label, passion, drama):
+def generate_scatter(raw, amplified, cinematic, label, passion, drama):
 
     fig, ax = plt.subplots(figsize=(6, 6))
-    base_color = "#2C3E50"
 
-    # ----------------------------------
-    # Background Cinematic Anchors
-    # ----------------------------------
+    base_color = "#2C3E50"  # neutral deep tone
+
+    # Plot cinematic anchors faintly
     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"],
@@ -84,75 +77,13 @@ def generate_scatter(raw, amplified, cinematic, target, label, passion, drama):
         label="After Drama (Cinematic Alignment)"
     )
 
-    # 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
+    # Arrow 1 — Raw → Amplified
     ax.arrow(
         raw["V"],
         raw["A"],
         amplified["V"] - raw["V"],
         amplified["A"] - raw["A"],
-        head_width=arrow_head,
+        head_width=0.02,
         length_includes_head=True,
         color=base_color,
         linestyle="--",
@@ -160,13 +91,13 @@ def generate_scatter(raw, amplified, cinematic, target, label, passion, drama):
         alpha=0.6
     )
 
-    # Amplified → Cinematic
+    # Arrow 2 — Amplified → Cinematic
     ax.arrow(
         amplified["V"],
         amplified["A"],
         cinematic["V"] - amplified["V"],
         cinematic["A"] - amplified["A"],
-        head_width=arrow_head,
+        head_width=0.02,
         length_includes_head=True,
         color=base_color,
         linestyle="-",
@@ -174,6 +105,36 @@ def generate_scatter(raw, amplified, cinematic, target, 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)}")
@@ -202,15 +163,7 @@ def run_pipeline(preset_name, passion, drama):
     color_params = infer_color(cinematic)
     color_block = render_color(color_params)
 
-    fig = generate_scatter(
-        natural,
-        amplified,
-        cinematic,
-        target,
-        preset_name,
-        passion,
-        drama
-    )
+    fig = generate_scatter(natural, amplified, cinematic, preset_name, passion, drama)
 
     return (
         text,
@@ -253,17 +206,23 @@ with gr.Blocks(title="Affection 👁️ — Edge Emotional Intelligence") as dem
 
     gr.Markdown(
         """
-This demo isolates a single loop transformation for clarity.
+This section provides a simplified visualization of a more complex on-device architecture.
 
-Hardware deployment performs:
-• Transcript ingestion
-• VAD extraction
-• Structural metrics
-• Radial amplification (Passion)
-• Cinematic alignment (Drama)
-• Continuous emotional state streaming
+In hardware deployment, the NVIDIA Jetson Orin Nano performs all of the following:  
 
-Edge loop capable of ~200Hz execution.
+• 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.
         """
     )
 
@@ -294,8 +253,8 @@ Edge loop capable of ~200Hz execution.
     gr.Markdown(
         """
 **Note:**  
-This 2D VA projection is for visualization only.  
-The actual transformation operates in 5D VAD+CC space.
+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.
         """
     )
 
@@ -308,10 +267,15 @@ The actual transformation operates in 5D VAD+CC space.
 
     gr.Markdown(
         """
-VAD+CC → Embedded Model → Color Rendering
+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.
 
-Model:
+Model used here (same as deployment):  
 https://huggingface.co/danielritchie/vibe-color-model
+
+VAD+CC (Affect Engine) → Embedded Model → Color Rendering (Expression)
         """
     )
 
@@ -334,9 +298,4 @@ https://huggingface.co/danielritchie/vibe-color-model
 
     demo.load(fn=run_pipeline, inputs=[preset_selector, passion, drama], outputs=outputs)
 
-
-demo.launch(
-    server_name="0.0.0.0",
-    server_port=7860,
-    ssr_mode=False
-)
+demo.launch(server_name="0.0.0.0", server_port=7860)