From 3d27110f3c311844df2fc002747c1fbebe822963 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 21 Oct 2025 00:27:22 +0000 Subject: [PATCH] Add non-blocking browser-based serving for dashboards Implements a .serve() method for both OfflineFeatureCentricDashboard and AbstractOnlineFeatureCentricDashboard that opens a web interface in the browser while keeping the notebook non-blocking. Architecture: - Created BaseServableDashboard abstract class with FastAPI server logic - Both dashboard classes inherit from it and implement: - get_initial_html(): Returns form UI - handle_request(): Processes form submissions - Reuses existing HTML generation code Features: - Non-blocking: Returns control to notebook immediately - Clean web UI with forms for all dashboard options - Works alongside existing .display() for Jupyter widgets - All subclasses get .serve() for free Dependencies added: - fastapi - uvicorn[standard] - python-multipart Usage: dashboard.serve(port=8000, open_browser=True, block=False) Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude --- pyproject.toml | 5 +- test_serve.py | 85 + test_serve_simple.py | 109 + tiny_dashboard/__init__.py | 2 + tiny_dashboard/base_servable_dashboard.py | 115 + tiny_dashboard/feature_centric_dashboards.py | 414 +- uv.lock | 4032 ++++++++++++++++++ 7 files changed, 4759 insertions(+), 3 deletions(-) create mode 100644 test_serve.py create mode 100644 test_serve_simple.py create mode 100644 tiny_dashboard/base_servable_dashboard.py create mode 100644 uv.lock diff --git a/pyproject.toml b/pyproject.toml index 0bd5194..2f587e6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,7 +20,10 @@ classifiers = [ ] dependencies = [ "nnterp", - "ipywidgets", + "ipywidgets", + "fastapi", + "uvicorn[standard]", + "python-multipart", ] diff --git a/test_serve.py b/test_serve.py new file mode 100644 index 0000000..17ddba3 --- /dev/null +++ b/test_serve.py @@ -0,0 +1,85 @@ +""" +Test script for the .serve() functionality. +This creates a minimal dashboard to verify the serving functionality works. +""" + +import torch as th +from transformers import AutoTokenizer +from tiny_dashboard import OfflineFeatureCentricDashboard, OnlineFeatureCentricDashboard + +def test_offline_serve(): + """Test the offline dashboard serve functionality""" + print("Testing Offline Dashboard serve()...") + + # Create minimal test data + tokenizer = AutoTokenizer.from_pretrained("gpt2") + + # Create some fake activation examples + max_activation_examples = { + 0: [ + (5.2, ["Hello", " world", "!"], [1.0, 5.2, 0.5]), + (4.8, ["Test", " example"], [4.8, 2.1]), + ], + 1: [ + (3.5, ["Another", " test"], [3.5, 1.2]), + ] + } + + dashboard = OfflineFeatureCentricDashboard( + max_activation_examples=max_activation_examples, + tokenizer=tokenizer, + window_size=50, + max_examples=10 + ) + + # Test serving (non-blocking) + print("Starting server on http://localhost:8000") + print("Press Ctrl+C to stop") + server = dashboard.serve(port=8000, open_browser=False, block=True) + + +def test_online_serve(): + """Test the online dashboard serve functionality""" + print("Testing Online Dashboard serve()...") + + tokenizer = AutoTokenizer.from_pretrained("gpt2") + + # Create a simple activation function for testing + def get_feature_activation(text: str, feature_indices: tuple[int, ...]) -> th.Tensor: + """Dummy activation function that returns random values""" + tokens = tokenizer.tokenize(text, add_special_tokens=True) + num_tokens = len(tokens) + num_features = len(feature_indices) + + # Return random activations + return th.rand(num_tokens, num_features) * 5.0 + + dashboard = OnlineFeatureCentricDashboard( + get_feature_activation=get_feature_activation, + tokenizer=tokenizer, + window_size=50 + ) + + # Test serving (non-blocking) + print("Starting server on http://localhost:8001") + print("Press Ctrl+C to stop") + server = dashboard.serve(port=8001, open_browser=False, block=True) + + +if __name__ == "__main__": + import sys + + if len(sys.argv) < 2: + print("Usage: python test_serve.py [offline|online]") + sys.exit(1) + + mode = sys.argv[1].lower() + + if mode == "offline": + test_offline_serve() + elif mode == "online": + test_online_serve() + else: + print(f"Unknown mode: {mode}") + print("Use 'offline' or 'online'") + sys.exit(1) diff --git a/test_serve_simple.py b/test_serve_simple.py new file mode 100644 index 0000000..376e119 --- /dev/null +++ b/test_serve_simple.py @@ -0,0 +1,109 @@ +""" +Simple test script for the .serve() functionality without requiring model downloads. +""" + +import torch as th +from tiny_dashboard import OfflineFeatureCentricDashboard, OnlineFeatureCentricDashboard + + +class MockTokenizer: + """Mock tokenizer for testing""" + bos_token = "" + + def tokenize(self, text, add_special_tokens=False): + # Simple whitespace tokenization + tokens = text.split() + if add_special_tokens: + tokens = [self.bos_token] + tokens + return tokens + + def convert_ids_to_tokens(self, ids): + return [f"token_{i}" for i in ids] + + def convert_tokens_to_ids(self, tokens): + # Simple hash-based ID generation + return [hash(token) % 50000 for token in tokens] + + +def test_offline_serve(): + """Test the offline dashboard serve functionality""" + print("Testing Offline Dashboard serve()...") + + tokenizer = MockTokenizer() + + # Create some fake activation examples + max_activation_examples = { + 0: [ + (5.2, ["Hello", " world", "!"], [1.0, 5.2, 0.5]), + (4.8, ["Test", " example"], [4.8, 2.1]), + ], + 1: [ + (3.5, ["Another", " test"], [3.5, 1.2]), + ], + 42: [ + (6.1, ["Feature", " forty", " two"], [2.0, 6.1, 3.0]), + ] + } + + dashboard = OfflineFeatureCentricDashboard( + max_activation_examples=max_activation_examples, + tokenizer=tokenizer, + window_size=50, + max_examples=10 + ) + + # Test serving (blocking) + print("Starting server on http://localhost:8002") + print("Press Ctrl+C to stop") + server = dashboard.serve(port=8002, open_browser=False, block=True) + + +def test_online_serve(): + """Test the online dashboard serve functionality""" + print("Testing Online Dashboard serve()...") + + tokenizer = MockTokenizer() + + # Create a simple activation function for testing + def get_feature_activation(text: str, feature_indices: tuple[int, ...]) -> th.Tensor: + """Dummy activation function that returns random values""" + tokens = tokenizer.tokenize(text, add_special_tokens=True) + num_tokens = len(tokens) + num_features = len(feature_indices) + + # Return random activations scaled by feature index + activations = th.rand(num_tokens, num_features) * 5.0 + # Make different features have different patterns + for i, feat_idx in enumerate(feature_indices): + activations[:, i] *= (1 + feat_idx * 0.1) + return activations + + dashboard = OnlineFeatureCentricDashboard( + get_feature_activation=get_feature_activation, + tokenizer=tokenizer, + window_size=50 + ) + + # Test serving (blocking) + print("Starting server on http://localhost:8001") + print("Press Ctrl+C to stop") + server = dashboard.serve(port=8001, open_browser=False, block=True) + + +if __name__ == "__main__": + import sys + + if len(sys.argv) < 2: + print("Usage: python test_serve_simple.py [offline|online]") + sys.exit(1) + + mode = sys.argv[1].lower() + + if mode == "offline": + test_offline_serve() + elif mode == "online": + test_online_serve() + else: + print(f"Unknown mode: {mode}") + print("Use 'offline' or 'online'") + sys.exit(1) diff --git a/tiny_dashboard/__init__.py b/tiny_dashboard/__init__.py index c3140be..d6d72bb 100644 --- a/tiny_dashboard/__init__.py +++ b/tiny_dashboard/__init__.py @@ -3,11 +3,13 @@ OnlineFeatureCentricDashboard, AbstractOnlineFeatureCentricDashboard, ) +from .base_servable_dashboard import BaseServableDashboard from .visualization_utils import activation_visualization __all__ = [ "OfflineFeatureCentricDashboard", "OnlineFeatureCentricDashboard", "AbstractOnlineFeatureCentricDashboard", + "BaseServableDashboard", "activation_visualization", ] diff --git a/tiny_dashboard/base_servable_dashboard.py b/tiny_dashboard/base_servable_dashboard.py new file mode 100644 index 0000000..de8b21d --- /dev/null +++ b/tiny_dashboard/base_servable_dashboard.py @@ -0,0 +1,115 @@ +import threading +import webbrowser +from abc import ABC, abstractmethod +from typing import Any +import uvicorn +from fastapi import FastAPI, Request, Form +from fastapi.responses import HTMLResponse + + +class BaseServableDashboard(ABC): + """ + Abstract base class that provides web serving capability to dashboards. + Subclasses must implement get_initial_html() and handle_request() to define + their specific UI and behavior. + """ + + @abstractmethod + def get_initial_html(self) -> str: + """ + Generate the initial HTML page with input form. + + Returns: + HTML string containing the form for user input + """ + pass + + @abstractmethod + def handle_request(self, form_data: dict[str, Any]) -> str: + """ + Process form submission and generate result HTML. + + Args: + form_data: Dictionary containing form field values + + Returns: + HTML string containing the analysis results + """ + pass + + def serve(self, port: int = 8000, open_browser: bool = True, block: bool = False): + """ + Start a web server to serve the dashboard in a browser. + + Args: + port: Port number to run the server on (default: 8000) + open_browser: Whether to automatically open browser (default: True) + block: Whether to block execution (default: False for non-blocking) + + Returns: + Server instance if non-blocking, None if blocking + """ + app = FastAPI() + + @app.get("/", response_class=HTMLResponse) + async def root(): + return self.get_initial_html() + + @app.post("/analyze", response_class=HTMLResponse) + async def analyze(request: Request): + form_data = await request.form() + form_dict = dict(form_data) + try: + return self.handle_request(form_dict) + except Exception as e: + # Return error HTML + import traceback + error_html = f""" + + + + Error + + + +

Error Processing Request

+
+ Error: {str(e)} +
+

Traceback:

+ 
{traceback.format_exc()}
+ Go Back + + + """ + return error_html + + # Configure and create server + config = uvicorn.Config( + app, + host="127.0.0.1", + port=port, + log_level="warning", + access_log=False + ) + server = uvicorn.Server(config) + + url = f"http://127.0.0.1:{port}" + print(f"Dashboard serving at {url}") + + if open_browser: + # Open browser after a short delay to ensure server is ready + threading.Timer(0.5, lambda: webbrowser.open(url)).start() + + if block: + server.run() + return None + else: + # Run server in background thread + thread = threading.Thread(target=server.run, daemon=True) + thread.start() + return server diff --git a/tiny_dashboard/feature_centric_dashboards.py b/tiny_dashboard/feature_centric_dashboards.py index f08e525..8219a13 100644 --- a/tiny_dashboard/feature_centric_dashboards.py +++ b/tiny_dashboard/feature_centric_dashboards.py @@ -25,9 +25,10 @@ create_token_html, create_highlighted_tokens_html, ) +from .base_servable_dashboard import BaseServableDashboard -class OfflineFeatureCentricDashboard: +class OfflineFeatureCentricDashboard(BaseServableDashboard): """ This Dashboard is composed of a feature selector and a feature viewer. The feature selector allows you to select a feature and view the max activating examples for that feature. @@ -226,8 +227,161 @@ def export_to_html(self, output_path: str, feature_to_export: int): with open(output_path, "w", encoding="utf-8") as f: f.write(html_content) + def get_initial_html(self) -> str: + """Generate the initial HTML page with feature selection form""" + available_features_str = ", ".join(map(str, self.available_features[:20])) + if len(self.available_features) > 20: + available_features_str += "..." + + return f""" + + + + Offline Feature Dashboard + + + +
+

Offline Feature Dashboard

+
+ Available features: {available_features_str} +
Total features: {len(self.available_features)} +
+
+
+ + +
+
+ +
+ +
+
+ + + """ -class AbstractOnlineFeatureCentricDashboard(ABC): + def handle_request(self, form_data: dict) -> str: + """Process form submission and generate feature examples HTML""" + try: + feature_idx = int(form_data.get("feature_idx", 0)) + use_absolute_max = form_data.get("use_absolute_max") == "true" + + if feature_idx not in self.max_activation_examples: + return f""" + + + + Error + + + +
+ Feature {feature_idx} not found. Available features: {self.available_features} +
+ Go Back + + + """ + + # Generate the HTML using existing method + html_content = self.generate_html(feature_idx, use_absolute_max) + + # Add a back button + html_with_nav = html_content.replace( + "", + '
← Back to Feature Selection
' + ) + + return html_with_nav + + except ValueError as e: + return f""" + + + + Error + + + +
Invalid feature index: {str(e)}
+ Go Back + + + """ + + +class AbstractOnlineFeatureCentricDashboard(BaseServableDashboard): """ Abstract base class for real-time feature analysis dashboards. Users can input text, select a feature, and see the activation patterns @@ -597,6 +751,262 @@ def display(self): ) display(dashboard) + def get_initial_html(self) -> str: + """Generate the initial HTML page with analysis form""" + model_info = "Model response generation available" if self.model is not None else "No model loaded" + + return f""" + + + + Online Feature Dashboard + + + +
+

Online Feature Dashboard

+
{model_info}
+
+
+ + +
+ +
+
+ + +
+ +
+ + +
+
+ +
+
+ + +
+ +
+ + +
+
+ +
+ + +
+ + +
+
+ + + """ + + def handle_request(self, form_data: dict) -> str: + """Process form submission and generate analysis HTML""" + try: + # Extract form data + text = form_data.get("text_input", "").strip() + if not text: + raise ValueError("No text provided") + + # Parse feature inputs + feature_input_str = form_data.get("feature_input", "[]").strip() + feature_indices = parse_list_str(feature_input_str) if feature_input_str else [] + + highlight_feature_str = form_data.get("highlight_feature", "").strip() + highlight_features = parse_list_str(highlight_feature_str) + if len(highlight_features) not in (1, 2): + raise ValueError("Please enter one or two features to highlight") + + tooltip_features_str = form_data.get("tooltip_features", "").strip() + tooltip_features = parse_list_str(tooltip_features_str) if tooltip_features_str else [] + + # Ensure all required features are computed + feature_indices = list( + dict.fromkeys(highlight_features + tooltip_features + feature_indices) + ) + + # Handle options + chat_formatting = form_data.get("chat_formatting") == "true" + generate_response = form_data.get("generate_response") == "true" + min_max_act_value = form_data.get("min_max_act", "").strip().lower() + + # Process text + if chat_formatting: + text = apply_chat(text, self.tokenizer, add_bos=False) + + if generate_response: + if self.model is None: + raise ValueError("Cannot generate response: no model loaded") + if text.startswith(self.tokenizer.bos_token): + text = text[len(self.tokenizer.bos_token):] + text = self.generate_model_response(text) + + # Tokenize and get activations + tokens = self.tokenizer.tokenize(text, add_special_tokens=True) + activations = self.get_feature_activation(text, tuple(feature_indices)) + + if len(tokens) != activations.shape[0]: + raise ValueError( + f"Token/activation mismatch: {len(tokens)} tokens vs {activations.shape[0]} activations" + ) + + # Handle min_max_act + min_max_act = None + if min_max_act_value and min_max_act_value != "auto": + try: + min_max_act = float(min_max_act_value) + except ValueError: + raise ValueError("Min-max act must be empty, 'auto' or a float value") + elif min_max_act_value == "auto": + if self.max_acts is not None and highlight_features[0] in self.max_acts: + min_max_act = self.max_acts[highlight_features[0]] + else: + raise ValueError("Cannot use 'auto': no max_acts available") + + # Create HTML visualization (adapted from _create_html_highlight) + highlight_positions = [feature_indices.index(f) for f in highlight_features] + tooltip_positions = [feature_indices.index(f) for f in tooltip_features] + activation_names = [f"Feature {feature_indices[i]}" for i in range(len(feature_indices))] + + html_content, max_acts_str = create_highlighted_tokens_html( + tokens=tokens, + activations=activations, + tokenizer=self.tokenizer, + highlight_features=highlight_positions, + tooltip_features=tooltip_positions, + color1=(255, 0, 0), + color2=self.second_highlight_color, + activation_names=activation_names, + return_max_acts_str=True, + min_max_act=min_max_act, + ) + + example_html = create_example_html(max_acts_str, html_content, static=True) + result_html = create_base_html(title="Feature Analysis", content=example_html) + + # Add back button + result_with_nav = result_html.replace( + "", + '
← Back to Input Form
' + ) + + return result_with_nav + + except Exception as e: + import traceback + return f""" + + + + Error + + + +

Error Processing Analysis

+
Error: {str(e)}
+

Traceback:

+ 
{traceback.format_exc()}
+ Go Back + + + """ + class OnlineFeatureCentricDashboard(AbstractOnlineFeatureCentricDashboard): """Implementation of AbstractOnlineFeatureCentricDashboard using functions diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..8bf8813 --- /dev/null +++ b/uv.lock @@ -0,0 +1,4032 @@ +version = 1 +revision = 3 +requires-python = ">=3.8" +resolution-markers = [ + "python_full_version >= '3.12'", + "python_full_version == '3.11.*'", + "python_full_version == '3.10.*'", + "python_full_version == '3.9.*'", + "python_full_version >= '3.8.1' and python_full_version < '3.9'", + "python_full_version < '3.8.1'", +] + +[[package]] +name = "accelerate" +version = "1.0.1" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.8.1' and python_full_version < '3.9'", + "python_full_version < '3.8.1'", +] +dependencies = [ + { name = "huggingface-hub", marker = "python_full_version < '3.9'" }, + { name = "numpy", version = "1.24.4", source = { 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