Create app.py

app.py

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+import math
+import time
+from threading import Thread
+
+import gradio as gr
+import matplotlib
+import numpy as np
+import torch
+
+try:
+    import whisper
+
+    HAS_WHISPER = True
+except ImportError:
+    HAS_WHISPER = False
+
+from peft import PeftModel
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    TextIteratorStreamer,
+)
+
+# Use non-interactive backend for matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+# ================================================================
+# CONFIGURATION
+# ================================================================
+BASE_MODEL = "ethicalabs/Echo-DSRN-114M-v0.1.2"
+ADAPTER_PATH = "ethicalabs/Echo-SmolTools-114M-Intent-PEFT"
+
+# ================================================================
+# METADATA: INTENTS & EXAMPLES
+# ================================================================
+INTENTS = [
+    "datetime_query",
+    "iot_hue_lightchange",
+    "transport_ticket",
+    "takeaway_query",
+    "qa_stock",
+    "general_greet",
+    "recommendation_events",
+    "music_dislikeness",
+    "iot_wemo_off",
+    "cooking_recipe",
+    "qa_currency",
+    "transport_traffic",
+    "general_quirky",
+    "weather_query",
+    "audio_volume_up",
+    "email_addcontact",
+    "takeaway_order",
+    "email_querycontact",
+    "iot_hue_lightup",
+    "recommendation_locations",
+    "play_audiobook",
+    "lists_createoradd",
+    "news_query",
+    "alarm_query",
+    "iot_wemo_on",
+    "general_joke",
+    "qa_definition",
+    "social_query",
+    "music_settings",
+    "audio_volume_other",
+    "calendar_remove",
+    "iot_hue_lightdim",
+    "calendar_query",
+    "email_sendemail",
+    "iot_cleaning",
+    "audio_volume_down",
+    "play_radio",
+    "cooking_query",
+    "datetime_convert",
+    "qa_maths",
+    "iot_hue_lightoff",
+    "iot_hue_lighton",
+    "transport_query",
+    "music_likeness",
+    "email_query",
+    "play_music",
+    "audio_volume_mute",
+    "social_post",
+    "alarm_set",
+    "qa_factoid",
+    "calendar_set",
+    "play_game",
+    "alarm_remove",
+    "lists_remove",
+    "transport_taxi",
+    "recommendation_movies",
+    "iot_coffee",
+    "music_query",
+    "play_podcasts",
+    "lists_query",
+]
+
+EXAMPLES = {
+    "it-IT": [
+        "spegni le luci per favore",
+        "abbassa le luci dell' ingresso",
+        "riproduci oro di mango",
+        "quali sono le previsioni meteo della settimana",
+        "riproduci malibu",
+    ],
+    "en-US": [
+        "turn the lights off please",
+        "dim the lights in the hall",
+        "clean the flat",
+        "cleaning is good dust is so bad do now your magic clean my carpet",
+        "list most rated delivery options for chinese food",
+    ],
+    "es-ES": [
+        "apaga las luces por favor",
+        "atenua las luces en el pasillo",
+        "oscurece la habitación",
+        "me gustaría escuchar barcelona de queen",
+        "ponme barcelona por queen",
+    ],
+    "pt-PT": [
+        "desligar as luzes",
+        "diminuir as luzes no salão",
+        "diz qual é o stato da minha memória disponível",
+        "mostra uma lista com entrega ao domicílio de comida chinesa com mais avaliações",
+        "eu gostava de ouvir punksinatra corridinho à portuguesa",
+    ],
+    "fr-FR": [
+        "éteigne les lumières s'il te plait",
+        "tamiser les lumières dans la salle",
+        "nettoyer la télévision",
+        "trouve mes plats à emporter thaïlandais autour de la concorde",
+        "j'aimerais écouter ne me quitte pas de jacques brel",
+    ],
+    "de-DE": [
+        "schalte bitte die lichter aus",
+        "dimme die lichter im eingangsbereich",
+        "wie lautet der status meines verfügbaren speichers",
+        "ich würde gerne queen's barcelona hören",
+        "spiel barcelona von queen",
+    ],
+}
+
+# ================================================================
+# MODEL LOADING
+# ================================================================
+print("🚀 Initializing Echo-DSRN Dashboard...")
+print(f"  Base: {BASE_MODEL}")
+print(f"  Adapter: {ADAPTER_PATH}")
+
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
+)
+model = PeftModel.from_pretrained(model, ADAPTER_PATH)
+model.eval()
+
+DEVICE = next(model.parameters()).device
+print(f"  ✅ Model loaded on {DEVICE}")
+
+if HAS_WHISPER:
+    print("🎙️ Loading Whisper 'tiny' engine...")
+    try:
+        whisper_model = whisper.load_model("tiny", device=DEVICE)
+        print("  ✅ Whisper ready.")
+    except Exception as e:
+        print(f"  ❌ Whisper loading failed: {e}")
+        HAS_WHISPER = False
+else:
+    print("  ℹ️ Whisper not available (optional dependency).")
+    whisper_model = None
+
+
+# ================================================================
+# OBSERVABILITY HOOKS
+# ================================================================
+def get_dsrn_state_heatmap():
+    """Generates a heatmap of the DSRN recurrent state (c_t) magnitude."""
+    plt.close('all')
+    config = model.config
+    state_dim = config.hidden_size * config.num_heads
+
+    if hasattr(model, "_latest_c_states") and model._latest_c_states is not None:
+        c_vector = model._latest_c_states[-1][0].detach().cpu().float().numpy()
+        c_vector = np.abs(c_vector)
+    else:
+        c_vector = np.zeros(state_dim)
+
+    w = int(math.sqrt(state_dim))
+    h = state_dim // w
+    state_magnitudes = c_vector[: w * h].reshape((h, w))
+
+    fig, ax = plt.subplots(figsize=(6, 3.5), dpi=100)
+    fig.patch.set_facecolor("#0f0f23")
+    ax.set_facecolor("#0f0f23")
+
+    im = ax.imshow(state_magnitudes, cmap="magma", aspect="auto", interpolation="nearest")
+    ax.set_title(
+        "DSRN Slow State (c_t) Memory Density", color="#e0e0f0", fontsize=10, fontweight="bold"
+    )
+    ax.set_xticks([])
+    ax.set_yticks([])
+
+    cbar = plt.colorbar(im, ax=ax, fraction=0.02, pad=0.04)
+    cbar.ax.tick_params(colors="#707080", labelsize=7)
+    plt.tight_layout()
+    return fig
+
+
+def get_surprise_lambda_visual():
+    """Generates a visualization of the Surprise Lambda activation."""
+    if hasattr(model, "_latest_gate_stats") and getattr(model, "_latest_gate_stats") is not None:
+        surprise_val = model._latest_gate_stats[-1][0, -1].item()
+    else:
+        surprise_val = 0.0
+
+    surprise_val = np.clip(surprise_val, 0.0, 1.0)
+
+    plt.close('all')
+    fig, ax = plt.subplots(figsize=(6, 1.0), dpi=100)
+    fig.patch.set_facecolor("#0f0f23")
+    ax.set_facecolor("#0f0f23")
+
+    color = '#ef4444' if surprise_val > 0.6 else '#f59e0b' if surprise_val > 0.3 else '#10b981'
+    ax.barh([0], [surprise_val], color=color, height=0.6, alpha=0.9, zorder=2)
+    ax.barh([0], [1.0], color='white', height=0.6, alpha=0.1, zorder=0)
+
+    ax.set_xlim(0, 1)
+    ax.set_ylim(-0.5, 0.5)
+    ax.set_yticks([])
+    ax.set_xticks([0, 0.25, 0.5, 0.75, 1.0])
+    ax.tick_params(colors="#707080", labelsize=8)
+    for spine in ax.spines.values():
+        spine.set_visible(False)
+
+    ax.set_title(
+        f"DSRN Surprise Signal (λ_t): {surprise_val:.4f}",
+        color="#e0e0f0",
+        fontsize=9,
+        fontweight="bold",
+    )
+    plt.tight_layout()
+    return fig
+
+
+# ================================================================
+# INFERENCE LOGIC
+# ================================================================
+def transcribe_audio(audio_path):
+    if not HAS_WHISPER or audio_path is None:
+        return ""
+    try:
+        result = whisper_model.transcribe(audio_path)
+        return result["text"].strip()
+    except Exception as e:
+        return f"Error transcribing: {e}"
+
+
+def classify_intent(utterance, locale):
+    if not utterance.strip():
+        yield "", None, None, "0.0 TPS"
+        return
+
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a helpful multilingual intent classification assistant.",
+        },
+        {"role": "user", "content": f"Classify the intent of the following request: {utterance}"},
+    ]
+
+    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = tokenizer(prompt, return_tensors="pt").to(DEVICE)
+    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+    gen_kwargs = {
+        "input_ids": inputs.input_ids,
+        "max_new_tokens": 15,
+        "do_sample": False,
+        "pad_token_id": tokenizer.eos_token_id,
+        "streamer": streamer,
+        "output_dsrn_telemetry": True,
+    }
+
+    thread = Thread(target=lambda: model.generate(**gen_kwargs))
+    thread.start()
+
+    start_time = time.time()
+    tokens = 0
+    full_response = ""
+
+    for chunk in streamer:
+        full_response += chunk
+        tokens += 1
+        elapsed = time.time() - start_time
+        tps = tokens / elapsed if elapsed > 0 else 0
+
+        # Plotting is heavy, refresh every few tokens or at the end
+        if tokens % 2 == 0:
+            yield full_response.strip(), get_dsrn_state_heatmap(), get_surprise_lambda_visual(), f"⚡ {tps:.1f} TPS"
+
+    # Final yield to ensure plot is up to date
+    elapsed = time.time() - start_time
+    tps = tokens / elapsed if elapsed > 0 else 0
+    yield full_response.strip(), get_dsrn_state_heatmap(), get_surprise_lambda_visual(), f"⚡ {tps:.1f} TPS"
+
+
+# ================================================================
+# UI CONSTRUCTION
+# ================================================================
+with gr.Blocks(theme=gr.themes.Soft(), title="Echo-DSRN Multilingual Intent Classifier") as demo:
+    gr.Markdown(
+        f"""
+        # 🎙️ Echo Intent: Multilingual DSRN Dashboard
+        ### High-Fidelity 1-Shot Inference & Observability Cockpit
+
+        This dashboard provides real-time intent classification across 60 categories.
+
+        **🚀 Model Lineage**:
+        - **Base**: `{BASE_MODEL}`
+        - **Adapter**: `{ADAPTER_PATH}`
+
+        **⚠️ Limitations**: While highly optimized for edge-routing, accuracy varies by locale. The 114M model may occasionally confuse overlapping semantic clusters (e.g., *calendar* vs. *alarm*) in low-context utterances.
+        """
+    )
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            with gr.Group():
+                locale = gr.Dropdown(
+                    choices=list(EXAMPLES.keys()),
+                    value="en-US",
+                    label="Target Locale",
+                    info="Select target language for intent examples.",
+                )
+                examples_dropdown = gr.Dropdown(
+                    choices=EXAMPLES["en-US"],
+                    value=EXAMPLES["en-US"][0],
+                    label="Example Utterances",
+                    info="Pre-compiled samples from the Amazon MASSIVE validation set.",
+                )
+
+            audio_input = gr.Audio(
+                sources=["microphone"],
+                type="filepath",
+                label="Voice Command (Experimental)",
+                visible=HAS_WHISPER,
+            )
+            transcribe_btn = gr.Button(
+                "🎤 Transcribe Audio", variant="secondary", visible=HAS_WHISPER
+            )
+
+            input_text = gr.Textbox(
+                value=EXAMPLES["en-US"][0],
+                placeholder="Enter a request in any supported language...",
+                label="Utterance",
+                lines=2,
+            )
+
+            with gr.Row():
+                classify_btn = gr.Button("🚀 Classify Intent", variant="primary")
+                reset_btn = gr.Button("🔄 Reset")
+
+            with gr.Group():
+                gr.Markdown("### 🏷️ Predicted Intent")
+                output_label = gr.Label(label="", show_label=False)
+                tps_stats = gr.Markdown("**Telemetry:** 0.0 TPS")
+
+        with gr.Column(scale=3):
+            gr.Markdown("### 🧠 DSRN Core Observability")
+            surprise_plot = gr.Plot(label="Surprise Bar")
+            heatmap_plot = gr.Plot(label="State Heatmap")
+
+    with gr.Accordion("📚 Reference: Intent Registry (60 Classes)", open=False):
+        gr.Markdown(", ".join([f"`{i}`" for i in INTENTS]))
+
+    # --- EVENT HANDLERS ---
+    def update_examples(loc):
+        return gr.update(choices=EXAMPLES[loc], value=EXAMPLES[loc][0])
+
+    def handle_reset():
+        return None, "", "en-US", EXAMPLES["en-US"][0], None, None, None, "**Telemetry:** 0.0 TPS"
+
+    locale.change(update_examples, locale, examples_dropdown)
+    examples_dropdown.change(lambda x: x, examples_dropdown, input_text)
+
+    transcribe_btn.click(transcribe_audio, inputs=[audio_input], outputs=[input_text])
+
+    classify_btn.click(
+        classify_intent,
+        inputs=[input_text, locale],
+        outputs=[output_label, heatmap_plot, surprise_plot, tps_stats],
+    )
+
+    reset_btn.click(
+        handle_reset,
+        outputs=[
+            audio_input,
+            input_text,
+            locale,
+            examples_dropdown,
+            output_label,
+            heatmap_plot,
+            surprise_plot,
+            tps_stats,
+        ],
+    )
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)