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6ixPulse

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OwnPath commited on 26 days ago

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0cb5613

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1 Parent(s): d81c4fa

Kokoro-82M TTS + README/diagram update + dead-code/Ollama cleanup

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Files changed (13) hide show

.env.example +0 -3
README.md +12 -3
package-lock.json +998 -10
package.json +1 -0
server/agent-core.mjs +1 -1
server/index.mjs +4 -12
server/model-chat.mjs +1 -13
server/ollama-client.mjs +0 -146
server/research-tools.mjs +0 -28
src/App.tsx +90 -117
src/data/neighborhoods.ts +0 -50
src/lib/tts.ts +41 -0
src/styles.css +8 -0

.env.example CHANGED Viewed

@@ -59,7 +59,4 @@ AGENT_FANOUT=1
 # the decisions; this small assistant summarises each agent's evidence. The 0.5B is tiny, so it
 # often falls back to the main model — use openbmb/MiniCPM4-8B-GGUF for genuine summaries.
 AGENT_SUMMARIZER_PROVIDER=llamacpp
-OLLAMA_MODEL=
-OLLAMA_HOST=http://127.0.0.1:11434
-OLLAMA_TIMEOUT_MS=60000
 AGENT_PORT=8787

 # the decisions; this small assistant summarises each agent's evidence. The 0.5B is tiny, so it
 # often falls back to the main model — use openbmb/MiniCPM4-8B-GGUF for genuine summaries.
 AGENT_SUMMARIZER_PROVIDER=llamacpp
 AGENT_PORT=8787

README.md CHANGED Viewed

@@ -23,8 +23,10 @@ tags:
   - achievement:sharing
   - achievement:fieldnotes
 models:
-  - Qwen/Qwen3-Coder-30B-A3B-Instruct
   - nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16
 ---
 # 6ixPulse
@@ -56,7 +58,8 @@ The app then ranks Toronto neighborhoods, animates the map to the areas it is re
 - **Fans out a researcher per City Agent.** Affordability, Commute, Safety, Lifestyle and Growth agents each reason over only their own evidence; a **Recommendation agent runs last** with every agent's finding *and* all of their sources.
 - **Fails closed.** The UI withholds a rent, commute, safety, growth claim, or score unless the backend can tie it to source evidence — sources are named, never "S1".
 - **Shows a real Mapbox GL JS 3D map** with a research tour that zooms into each area while it is being researched, then flies out-and-in to the next.
-- **Two-tier brains, all < 32B.** NVIDIA Nemotron Nano Omni Reasoning is the main model (with `enable_thinking`); the small OpenBMB model on llama.cpp assists with summarisation. Falls back to HF when a provider is unavailable.
 ## Architecture
@@ -106,14 +109,16 @@ flowchart TB
   Score --- Transit
   Score --- CMHC
-  subgraph Models["Two-tier brains · all under 32B"]
     Main["Main: NVIDIA Nemotron Nano Omni Reasoning<br/>temp 0.6 · enable_thinking · reasoning_budget<br/>↳ fallback: HF Qwen3-Coder"]
     Assist["Assistant: OpenBMB MiniCPM via llama.cpp<br/>summarises each agent's evidence"]
   end
   Discover --- Main
   Fanout --- Assist
   Fanout --- Main
   Recommend --- Main
 ```
 ## Tech Stack
@@ -128,6 +133,7 @@ flowchart TB
 - Models (all < 32B):
   - Main agentic brain: `nvidia/nemotron-3-nano-omni-30b-a3b-reasoning` (build.nvidia.com)
   - Summarisation assistant: OpenBMB MiniCPM via llama.cpp (`openbmb/MiniCPM4-*-GGUF`)
   - Fallback: `Qwen/Qwen3-Coder-30B-A3B-Instruct` on the HF Router
 ## Build Small Hackathon Readiness
@@ -214,6 +220,8 @@ HF_MODEL=Qwen/Qwen3-Coder-30B-A3B-Instruct
 The reasoning model uses the Nemotron recipe (matching `ChatNVIDIA`): `temperature=0.6`, `top_p=0.95`, `chat_template_kwargs={"enable_thinking": true}`, and a `reasoning_budget`; its `<think>` reasoning is captured separately from the JSON answer. Thinking is enabled only for the decisions (discovery, recommendation, synthesis), and the per-agent fan-out runs in parallel on hosted APIs, so a full run stays within the response budget.
 ## Gradio Space
 This repo is prepared for a Docker-backed Gradio Space using `gradio.Server`, which is designed for custom frontends like React while still giving the project Gradio's API engine, queuing, MCP support, and Hugging Face Spaces hosting.
@@ -320,6 +328,7 @@ Dockerfile                     Docker Space image with Node + Python
 src/App.tsx                    app shell, agent panels, scores, listings
 src/components/MapCanvas.tsx   Mapbox/deck.gl map + research tour
 src/lib/agentApi.ts            frontend API client types
 server/index.mjs               agent API server; orchestrates the pipeline
 server/discover.mjs            model-driven neighbourhood discovery (+ coordinates)
 server/score-tools.mjs         8-dimension scoring from named no-key sources

   - achievement:sharing
   - achievement:fieldnotes
 models:
   - nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16
+  - Qwen/Qwen3-Coder-30B-A3B-Instruct
+  - openbmb/MiniCPM4-0.5B
+  - hexgrad/Kokoro-82M
 ---
 # 6ixPulse
 - **Fans out a researcher per City Agent.** Affordability, Commute, Safety, Lifestyle and Growth agents each reason over only their own evidence; a **Recommendation agent runs last** with every agent's finding *and* all of their sources.
 - **Fails closed.** The UI withholds a rent, commute, safety, growth claim, or score unless the backend can tie it to source evidence — sources are named, never "S1".
 - **Shows a real Mapbox GL JS 3D map** with a research tour that zooms into each area while it is being researched, then flies out-and-in to the next.
+- **Reads the findings aloud.** A Play button narrates a short spoken summary using **Kokoro-82M** neural TTS running entirely in the browser — no key, no backend.
+- **Models, all < 32B.** NVIDIA Nemotron Nano Omni Reasoning is the main brain (with `enable_thinking`); the small OpenBMB model on llama.cpp assists with summarisation; Kokoro-82M handles speech. Falls back to HF when a provider is unavailable.
 ## Architecture
   Score --- Transit
   Score --- CMHC
+  subgraph Models["Models · all under 32B"]
     Main["Main: NVIDIA Nemotron Nano Omni Reasoning<br/>temp 0.6 · enable_thinking · reasoning_budget<br/>↳ fallback: HF Qwen3-Coder"]
     Assist["Assistant: OpenBMB MiniCPM via llama.cpp<br/>summarises each agent's evidence"]
+    Speech["Voice: Kokoro-82M neural TTS<br/>in-browser, narrates the summary"]
   end
   Discover --- Main
   Fanout --- Assist
   Fanout --- Main
   Recommend --- Main
+  Panels -. Play .-> Speech
 ```
 ## Tech Stack
 - Models (all < 32B):
   - Main agentic brain: `nvidia/nemotron-3-nano-omni-30b-a3b-reasoning` (build.nvidia.com)
   - Summarisation assistant: OpenBMB MiniCPM via llama.cpp (`openbmb/MiniCPM4-*-GGUF`)
+  - Speech: `hexgrad/Kokoro-82M` neural TTS in the browser (transformers.js)
   - Fallback: `Qwen/Qwen3-Coder-30B-A3B-Instruct` on the HF Router
 ## Build Small Hackathon Readiness
 The reasoning model uses the Nemotron recipe (matching `ChatNVIDIA`): `temperature=0.6`, `top_p=0.95`, `chat_template_kwargs={"enable_thinking": true}`, and a `reasoning_budget`; its `<think>` reasoning is captured separately from the JSON answer. Thinking is enabled only for the decisions (discovery, recommendation, synthesis), and the per-agent fan-out runs in parallel on hosted APIs, so a full run stays within the response budget.
+For speech, the Play button uses **`hexgrad/Kokoro-82M`** neural TTS via `transformers.js`, running fully in the browser (ONNX/WASM). It is lazy-loaded — the ~80 MB model only downloads on the first Play, then is cached — and falls back to the browser's built-in speech while it loads or where it can't run. No key, no backend, 82M parameters.
 ## Gradio Space
 This repo is prepared for a Docker-backed Gradio Space using `gradio.Server`, which is designed for custom frontends like React while still giving the project Gradio's API engine, queuing, MCP support, and Hugging Face Spaces hosting.
 src/App.tsx                    app shell, agent panels, scores, listings
 src/components/MapCanvas.tsx   Mapbox/deck.gl map + research tour
 src/lib/agentApi.ts            frontend API client types
+src/lib/tts.ts                 Kokoro-82M in-browser narration
 server/index.mjs               agent API server; orchestrates the pipeline
 server/discover.mjs            model-driven neighbourhood discovery (+ coordinates)
 server/score-tools.mjs         8-dimension scoring from named no-key sources

package-lock.json CHANGED Viewed

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+        "node": ">=8.0"
+      }
+    },
     "node_modules/robust-predicates": {
       "version": "2.0.4",
       "resolved": "https://registry.npmjs.org/robust-predicates/-/robust-predicates-2.0.4.tgz",
       "integrity": "sha512-eNv+WrVbKu1f3vbYJT/xtiF5syA5HPIMtf9IgY/nKg0sWqzAUEvqY/xm7OcZc/qafLx/iO9FgOmeSAp4v5ti/Q==",
       "license": "MIT"
     },
+    "node_modules/semver": {
+      "version": "7.8.4",
+      "resolved": "https://registry.npmjs.org/semver/-/semver-7.8.4.tgz",
+      "integrity": "sha512-rUCObTnP32Q08R2uuIrt7r9PlEonuTmtuXYcW6s5kjdlj3xbnwe+21yXptAUYcMAABLkYYTtnmzb3w3EDZfueA==",
+      "license": "ISC",
+      "bin": {
+        "semver": "bin/semver.js"
+      },
+      "engines": {
+        "node": ">=10"
+      }
+    },
+    "node_modules/semver-compare": {
+      "version": "1.0.0",
+      "resolved": "https://registry.npmjs.org/semver-compare/-/semver-compare-1.0.0.tgz",
+      "integrity": "sha512-YM3/ITh2MJ5MtzaM429anh+x2jiLVjqILF4m4oyQB18W7Ggea7BfqdH/wGMK7dDiMghv/6WG7znWMwUDzJiXow==",
+      "license": "MIT"
+    },
+    "node_modules/serialize-error": {
+      "version": "7.0.1",
+      "resolved": "https://registry.npmjs.org/serialize-error/-/serialize-error-7.0.1.tgz",
+      "integrity": "sha512-8I8TjW5KMOKsZQTvoxjuSIa7foAwPWGOts+6o7sgjz41/qMD9VQHEDxi6PBvK2l0MXUmqZyNpUK+T2tQaaElvw==",
+      "license": "MIT",
+      "dependencies": {
+        "type-fest": "^0.13.1"
+      },
+      "engines": {
+        "node": ">=10"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/sindresorhus"
+      }
+    },
+    "node_modules/sharp": {
+      "version": "0.34.5",
+      "resolved": "https://registry.npmjs.org/sharp/-/sharp-0.34.5.tgz",
+      "integrity": "sha512-Ou9I5Ft9WNcCbXrU9cMgPBcCK8LiwLqcbywW3t4oDV37n1pzpuNLsYiAV8eODnjbtQlSDwZ2cUEeQz4E54Hltg==",
+      "hasInstallScript": true,
+      "license": "Apache-2.0",
+      "dependencies": {
+        "@img/colour": "^1.0.0",
+        "detect-libc": "^2.1.2",
+        "semver": "^7.7.3"
+      },
+      "engines": {
+        "node": "^18.17.0 || ^20.3.0 || >=21.0.0"
+      },
+      "funding": {
+        "url": "https://opencollective.com/libvips"
+      },
+      "optionalDependencies": {
+        "@img/sharp-darwin-arm64": "0.34.5",
+        "@img/sharp-darwin-x64": "0.34.5",
+        "@img/sharp-libvips-darwin-arm64": "1.2.4",
+        "@img/sharp-libvips-darwin-x64": "1.2.4",
+        "@img/sharp-libvips-linux-arm": "1.2.4",
+        "@img/sharp-libvips-linux-arm64": "1.2.4",
+        "@img/sharp-libvips-linux-ppc64": "1.2.4",
+        "@img/sharp-libvips-linux-riscv64": "1.2.4",
+        "@img/sharp-libvips-linux-s390x": "1.2.4",
+        "@img/sharp-libvips-linux-x64": "1.2.4",
+        "@img/sharp-libvips-linuxmusl-arm64": "1.2.4",
+        "@img/sharp-libvips-linuxmusl-x64": "1.2.4",
+        "@img/sharp-linux-arm": "0.34.5",
+        "@img/sharp-linux-arm64": "0.34.5",
+        "@img/sharp-linux-ppc64": "0.34.5",
+        "@img/sharp-linux-riscv64": "0.34.5",
+        "@img/sharp-linux-s390x": "0.34.5",
+        "@img/sharp-linux-x64": "0.34.5",
+        "@img/sharp-linuxmusl-arm64": "0.34.5",
+        "@img/sharp-linuxmusl-x64": "0.34.5",
+        "@img/sharp-wasm32": "0.34.5",
+        "@img/sharp-win32-arm64": "0.34.5",
+        "@img/sharp-win32-ia32": "0.34.5",
+        "@img/sharp-win32-x64": "0.34.5"
+      }
+    },
     "node_modules/source-map-js": {
       "version": "1.2.1",
       "resolved": "https://registry.npmjs.org/source-map-js/-/source-map-js-1.2.1.tgz",
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       "license": "MIT"
     },
+    "node_modules/sprintf-js": {
+      "version": "1.1.3",
+      "resolved": "https://registry.npmjs.org/sprintf-js/-/sprintf-js-1.1.3.tgz",
+      "integrity": "sha512-Oo+0REFV59/rz3gfJNKQiBlwfHaSESl1pcGyABQsnnIfWOFt6JNj5gCog2U6MLZ//IGYD+nA8nI+mTShREReaA==",
+      "license": "BSD-3-Clause"
+    },
     "node_modules/supercluster": {
       "version": "8.0.1",
       "resolved": "https://registry.npmjs.org/supercluster/-/supercluster-8.0.1.tgz",
         "node": ">=12.17"
       }
     },
+    "node_modules/tar": {
+      "version": "7.5.16",
+      "resolved": "https://registry.npmjs.org/tar/-/tar-7.5.16.tgz",
+      "integrity": "sha512-56adEpPMouktRlBLXiaYFFzZ/3+JXa8P9n7WbR+ibIjtviN55mEaOkiysCnPnWm+7kkui1Dn8J9l+g6zV8731w==",
+      "license": "BlueOak-1.0.0",
+      "dependencies": {
+        "@isaacs/fs-minipass": "^4.0.0",
+        "chownr": "^3.0.0",
+        "minipass": "^7.1.2",
+        "minizlib": "^3.1.0",
+        "yallist": "^5.0.0"
+      },
+      "engines": {
+        "node": ">=18"
+      }
+    },
     "node_modules/tinyglobby": {
       "version": "0.2.17",
       "resolved": "https://registry.npmjs.org/tinyglobby/-/tinyglobby-0.2.17.tgz",
       "integrity": "sha512-oJFu94HQb+KVduSUQL7wnpmqnfmLsOA/nAh6b6EH0wCEoK0/mPeXU6c3wKDV83MkOuHPRHtSXKKU99IBazS/2w==",
       "license": "0BSD"
     },
+    "node_modules/type-fest": {
+      "version": "0.13.1",
+      "resolved": "https://registry.npmjs.org/type-fest/-/type-fest-0.13.1.tgz",
+      "integrity": "sha512-34R7HTnG0XIJcBSn5XhDd7nNFPRcXYRZrBB2O2jdKqYODldSzBAqzsWoZYYvduky73toYS/ESqxPvkDf/F0XMg==",
+      "license": "(MIT OR CC0-1.0)",
+      "engines": {
+        "node": ">=10"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/sindresorhus"
+      }
+    },
     "node_modules/typescript": {
       "version": "5.9.3",
       "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.9.3.tgz",
       "engines": {
         "node": ">=12.17"
       }
+    },
+    "node_modules/yallist": {
+      "version": "5.0.0",
+      "resolved": "https://registry.npmjs.org/yallist/-/yallist-5.0.0.tgz",
+      "integrity": "sha512-YgvUTfwqyc7UXVMrB+SImsVYSmTS8X/tSrtdNZMImM+n7+QTriRXyXim0mBrTXNeqzVF0KWGgHPeiyViFFrNDw==",
+      "license": "BlueOak-1.0.0",
+      "engines": {
+        "node": ">=18"
+      }
     }
   }
 }

package.json CHANGED Viewed

@@ -17,6 +17,7 @@
     "@deck.gl/core": "^9.0.0",
     "@deck.gl/layers": "^9.0.0",
     "@deck.gl/mapbox": "^9.0.0",
     "lucide-react": "^0.468.0",
     "mapbox-gl": "^3.11.1",
     "react": "^19.0.0",

     "@deck.gl/core": "^9.0.0",
     "@deck.gl/layers": "^9.0.0",
     "@deck.gl/mapbox": "^9.0.0",
+    "kokoro-js": "^1.2.1",
     "lucide-react": "^0.468.0",
     "mapbox-gl": "^3.11.1",
     "react": "^19.0.0",

server/agent-core.mjs CHANGED Viewed

@@ -416,7 +416,7 @@ function buildEvidenceRecommendation(run, selected, webResearch, evidenceNotes)
 }
 function sanitizeTrace(trace = []) {
-  const visibleTools = new Set(["discover_neighborhoods", "plan_research", "parse_renter_intent", "housing_web_research", "score_neighborhoods", "hf_reasoning", "nvidia_reasoning", "ollama_reasoning", "llamacpp_reasoning"]);
   return trace
     .filter((step) => visibleTools.has(step.tool) || step.tool.startsWith("agent_"))
     .map((step) => {

 }
 function sanitizeTrace(trace = []) {
+  const visibleTools = new Set(["discover_neighborhoods", "plan_research", "parse_renter_intent", "housing_web_research", "score_neighborhoods", "hf_reasoning", "nvidia_reasoning", "llamacpp_reasoning"]);
   return trace
     .filter((step) => visibleTools.has(step.tool) || step.tool.startsWith("agent_"))
     .map((step) => {

server/index.mjs CHANGED Viewed

@@ -4,7 +4,6 @@ import { resolve } from "node:path";
 import { applyEvidencePolicy, buildLocalAgentRun, mergeModelRecommendation } from "./agent-core.mjs";
 import { configuredModel, runHfAgent } from "./hf-client.mjs";
 import { configuredNvidiaModel, runNvidiaAgent } from "./nvidia-client.mjs";
-import { configuredOllamaModel, runOllamaAgent } from "./ollama-client.mjs";
 import { configuredLlamaCppModel, llamaCppEnabled, runLlamaCppAgent } from "./llamacpp-client.mjs";
 import { runAgentFanOut } from "./agent-fanout.mjs";
 import { discoverNeighborhoods, buildDiscoveredRows } from "./discover.mjs";
@@ -45,7 +44,6 @@ const server = createServer(async (request, response) => {
         ),
         nvidiaConfigured: Boolean(process.env.NVIDIA_API_KEY || process.env.NGC_API_KEY),
         llamacppConfigured: llamaCppEnabled(),
-        ollamaConfigured: Boolean(configuredOllamaModel()),
         searchProvider: configuredSearchProvider(),
         search: searchProviderStatus(),
         researchEnabled: process.env.RESEARCH_ENABLED !== "0",
@@ -302,9 +300,6 @@ async function runConfiguredModel(localRun) {
   if (provider === "llamacpp") {
     return { provider, ...(await runLlamaCppAgent(localRun)) };
   }
-  if (provider === "ollama") {
-    return { provider, ...(await runOllamaAgent(localRun)) };
-  }
   if (provider === "nvidia") {
     return { provider, ...(await runNvidiaAgent(localRun)) };
   }
@@ -313,20 +308,18 @@ async function runConfiguredModel(localRun) {
   }
   // auto: Nemotron (NVIDIA) first when keyed, then a local llama.cpp/OpenBMB GGUF,
-  // then Ollama, then the HF model — so the agent always lands on a working brain.
   const nvidia = await runNvidiaAgent(localRun);
   if (nvidia.status === "done") return { provider: "nvidia", ...nvidia };
   const llamacpp = await runLlamaCppAgent(localRun);
   if (llamacpp.status === "done") return { provider: "llamacpp", ...llamacpp };
-  const ollama = await runOllamaAgent(localRun);
-  if (ollama.status === "done") return { provider: "ollama", ...ollama };
   const hf = await runHfAgent(localRun);
   if (hf.status === "done") return { provider: "hf", ...hf };
   return {
     provider: "auto",
     status: "error",
-    reason: `NVIDIA: ${nvidia.reason}; llama.cpp: ${llamacpp.reason}; Ollama: ${ollama.reason}; Hugging Face: ${hf.reason}`,
     model: configuredAgentModel(),
     result: null,
   };
@@ -334,7 +327,7 @@ async function runConfiguredModel(localRun) {
 function configuredProvider() {
   const raw = (process.env.AGENT_MODEL_PROVIDER || process.env.AGENT_PROVIDER || "hf").toLowerCase();
-  if (["nvidia", "llamacpp", "ollama", "hf", "auto"].includes(raw)) return raw;
   return "hf";
 }
@@ -342,9 +335,8 @@ function configuredAgentModel() {
   const provider = configuredProvider();
   if (provider === "nvidia") return configuredNvidiaModel();
   if (provider === "llamacpp") return configuredLlamaCppModel();
-  if (provider === "ollama") return configuredOllamaModel();
   if (provider === "auto") {
-    return configuredNvidiaModel() || configuredLlamaCppModel() || configuredOllamaModel() || configuredModel();
   }
   return configuredModel();
 }

 import { applyEvidencePolicy, buildLocalAgentRun, mergeModelRecommendation } from "./agent-core.mjs";
 import { configuredModel, runHfAgent } from "./hf-client.mjs";
 import { configuredNvidiaModel, runNvidiaAgent } from "./nvidia-client.mjs";
 import { configuredLlamaCppModel, llamaCppEnabled, runLlamaCppAgent } from "./llamacpp-client.mjs";
 import { runAgentFanOut } from "./agent-fanout.mjs";
 import { discoverNeighborhoods, buildDiscoveredRows } from "./discover.mjs";
         ),
         nvidiaConfigured: Boolean(process.env.NVIDIA_API_KEY || process.env.NGC_API_KEY),
         llamacppConfigured: llamaCppEnabled(),
         searchProvider: configuredSearchProvider(),
         search: searchProviderStatus(),
         researchEnabled: process.env.RESEARCH_ENABLED !== "0",
   if (provider === "llamacpp") {
     return { provider, ...(await runLlamaCppAgent(localRun)) };
   }
   if (provider === "nvidia") {
     return { provider, ...(await runNvidiaAgent(localRun)) };
   }
   }
   // auto: Nemotron (NVIDIA) first when keyed, then a local llama.cpp/OpenBMB GGUF,
+  // then the HF model — so the agent always lands on a working brain.
   const nvidia = await runNvidiaAgent(localRun);
   if (nvidia.status === "done") return { provider: "nvidia", ...nvidia };
   const llamacpp = await runLlamaCppAgent(localRun);
   if (llamacpp.status === "done") return { provider: "llamacpp", ...llamacpp };
   const hf = await runHfAgent(localRun);
   if (hf.status === "done") return { provider: "hf", ...hf };
   return {
     provider: "auto",
     status: "error",
+    reason: `NVIDIA: ${nvidia.reason}; llama.cpp: ${llamacpp.reason}; Hugging Face: ${hf.reason}`,
     model: configuredAgentModel(),
     result: null,
   };
 function configuredProvider() {
   const raw = (process.env.AGENT_MODEL_PROVIDER || process.env.AGENT_PROVIDER || "hf").toLowerCase();
+  if (["nvidia", "llamacpp", "hf", "auto"].includes(raw)) return raw;
   return "hf";
 }
   const provider = configuredProvider();
   if (provider === "nvidia") return configuredNvidiaModel();
   if (provider === "llamacpp") return configuredLlamaCppModel();
   if (provider === "auto") {
+    return configuredNvidiaModel() || configuredLlamaCppModel() || configuredModel();
   }
   return configuredModel();
 }

server/model-chat.mjs CHANGED Viewed

@@ -1,5 +1,5 @@
 // Shared "talk to the configured agentic model" helper. Resolves whichever brain is
-// active (Nemotron / llama.cpp+OpenBMB / Ollama / HF) and sends an OpenAI-compatible chat
 // request, so the per-agent fan-out reasons with the SAME agentic model as the synthesiser.
 import { stripReasoning } from "./model-prompt.mjs";
@@ -18,7 +18,6 @@ export function resolveActiveProvider(env = process.env) {
   const has = {
     nvidia: Boolean(env.NVIDIA_API_KEY || env.NGC_API_KEY),
     llamacpp: env.LLAMACPP_ENABLED === "1",
-    ollama: Boolean(env.OLLAMA_MODEL || env.AGENT_OLLAMA_MODEL),
     hf: Boolean(env.HF_TOKEN || env.HUGGINGFACEHUB_API_TOKEN || env.HUGGING_FACE_HUB_TOKEN),
   };
   if (raw !== "auto" && raw !== "") return has[raw] ? raw : firstAvailable(has);
@@ -28,7 +27,6 @@ export function resolveActiveProvider(env = process.env) {
 function firstAvailable(has) {
   if (has.nvidia) return "nvidia";
   if (has.llamacpp) return "llamacpp";
-  if (has.ollama) return "ollama";
   if (has.hf) return "hf";
   return null;
 }
@@ -37,7 +35,6 @@ function availability(env) {
   return {
     nvidia: Boolean(env.NVIDIA_API_KEY || env.NGC_API_KEY),
     llamacpp: env.LLAMACPP_ENABLED === "1",
-    ollama: Boolean(env.OLLAMA_MODEL || env.AGENT_OLLAMA_MODEL),
     hf: Boolean(env.HF_TOKEN || env.HUGGINGFACEHUB_API_TOKEN || env.HUGGING_FACE_HUB_TOKEN),
   };
 }
@@ -51,7 +48,6 @@ export function resolveMainProvider(env = process.env) {
   if (raw !== "auto" && raw !== "" && has[raw]) return raw;
   if (has.nvidia) return "nvidia";
   if (has.hf) return "hf";
-  if (has.ollama) return "ollama";
   if (has.llamacpp) return "llamacpp";
   return null;
 }
@@ -85,14 +81,6 @@ function endpointFor(provider, env) {
       grammar: true,
     };
   }
-  if (provider === "ollama") {
-    const host = env.OLLAMA_HOST || "http://127.0.0.1:11434";
-    return {
-      url: env.OLLAMA_CHAT_COMPLETIONS_URL || `${host.replace(/\/$/, "")}/v1/chat/completions`,
-      model: env.OLLAMA_MODEL || env.AGENT_OLLAMA_MODEL,
-      auth: "ollama",
-    };
-  }
   return {
     url: env.HF_CHAT_COMPLETIONS_URL || "https://router.huggingface.co/v1/chat/completions",
     model: env.HF_MODEL || "Qwen/Qwen3-Coder-30B-A3B-Instruct",

 // Shared "talk to the configured agentic model" helper. Resolves whichever brain is
+// active (Nemotron / llama.cpp+OpenBMB / HF) and sends an OpenAI-compatible chat
 // request, so the per-agent fan-out reasons with the SAME agentic model as the synthesiser.
 import { stripReasoning } from "./model-prompt.mjs";
   const has = {
     nvidia: Boolean(env.NVIDIA_API_KEY || env.NGC_API_KEY),
     llamacpp: env.LLAMACPP_ENABLED === "1",
     hf: Boolean(env.HF_TOKEN || env.HUGGINGFACEHUB_API_TOKEN || env.HUGGING_FACE_HUB_TOKEN),
   };
   if (raw !== "auto" && raw !== "") return has[raw] ? raw : firstAvailable(has);
 function firstAvailable(has) {
   if (has.nvidia) return "nvidia";
   if (has.llamacpp) return "llamacpp";
   if (has.hf) return "hf";
   return null;
 }
   return {
     nvidia: Boolean(env.NVIDIA_API_KEY || env.NGC_API_KEY),
     llamacpp: env.LLAMACPP_ENABLED === "1",
     hf: Boolean(env.HF_TOKEN || env.HUGGINGFACEHUB_API_TOKEN || env.HUGGING_FACE_HUB_TOKEN),
   };
 }
   if (raw !== "auto" && raw !== "" && has[raw]) return raw;
   if (has.nvidia) return "nvidia";
   if (has.hf) return "hf";
   if (has.llamacpp) return "llamacpp";
   return null;
 }
       grammar: true,
     };
   }
   return {
     url: env.HF_CHAT_COMPLETIONS_URL || "https://router.huggingface.co/v1/chat/completions",
     model: env.HF_MODEL || "Qwen/Qwen3-Coder-30B-A3B-Instruct",

server/ollama-client.mjs DELETED Viewed

@@ -1,146 +0,0 @@
-import { buildModelContext } from "./agent-core.mjs";
-import { SYSTEM_PROMPT, parseModelJson } from "./model-prompt.mjs";
-const DEFAULT_OLLAMA_URL = "http://127.0.0.1:11434/v1/chat/completions";
-export async function runOllamaAgent(localRun, env = process.env) {
-  if (env.AGENT_OFFLINE === "1") {
-    return {
-      status: "skipped",
-      reason: "AGENT_OFFLINE is enabled",
-      model: configuredOllamaModel(env),
-      result: null,
-    };
-  }
-  const model = configuredOllamaModel(env);
-  if (!model) {
-    return {
-      status: "skipped",
-      reason: "OLLAMA_MODEL is not configured",
-      model: null,
-      result: null,
-    };
-  }
-  const context = buildModelContext(localRun);
-  const basePayload = {
-    model,
-    messages: [
-      { role: "system", content: SYSTEM_PROMPT },
-      { role: "user", content: JSON.stringify(context) },
-    ],
-    temperature: Number(env.OLLAMA_TEMPERATURE ?? 0.2),
-    max_tokens: Number(env.OLLAMA_MAX_TOKENS ?? 900),
-    stream: false,
-  };
-  const richPayload = {
-    ...basePayload,
-    response_format: { type: "json_object" },
-    reasoning_effort: env.OLLAMA_REASONING_EFFORT || "medium",
-  };
-  try {
-    const body = await postChat(richPayload, env);
-    return {
-      status: "done",
-      reason: null,
-      model,
-      result: parseModelJson(body, "Ollama"),
-    };
-  } catch (error) {
-    if (isRetryableProviderError(error)) {
-      try {
-        const body = await postChat(basePayload, env);
-        return {
-          status: "done",
-          reason: "Retried without JSON/reasoning parameters",
-          model,
-          result: parseModelJson(body, "Ollama"),
-        };
-      } catch (retryError) {
-        return errorResult(retryError, model);
-      }
-    }
-    return errorResult(error, model);
-  }
-}
-export function configuredOllamaModel(env = process.env) {
-  return env.OLLAMA_MODEL || env.AGENT_OLLAMA_MODEL || "";
-}
-export function ollamaChatCompletionsUrl(env = process.env) {
-  if (env.OLLAMA_CHAT_COMPLETIONS_URL) return env.OLLAMA_CHAT_COMPLETIONS_URL;
-  const host = env.OLLAMA_HOST || "http://127.0.0.1:11434";
-  return `${host.replace(/\/$/, "")}/v1/chat/completions`;
-}
-async function postChat(payload, env) {
-  const timeoutMs = Number(env.OLLAMA_TIMEOUT_MS ?? 60000);
-  const controller = new AbortController();
-  const timeout = setTimeout(() => controller.abort(), timeoutMs);
-  try {
-    const response = await fetch(ollamaChatCompletionsUrl(env), {
-      method: "POST",
-      headers: {
-        Authorization: "Bearer ollama",
-        "Content-Type": "application/json",
-      },
-      body: JSON.stringify(payload),
-      signal: controller.signal,
-    });
-    const text = await response.text();
-    if (!response.ok) {
-      throw new OllamaRequestError(response.status, conciseError(text));
-    }
-    try {
-      return JSON.parse(text);
-    } catch {
-      throw new Error("Ollama returned a non-JSON chat response");
-    }
-  } catch (error) {
-    if (error?.name === "AbortError") {
-      throw new Error(`Ollama request timed out after ${timeoutMs}ms`);
-    }
-    throw error;
-  } finally {
-    clearTimeout(timeout);
-  }
-}
-function isRetryableProviderError(error) {
-  return error instanceof OllamaRequestError && (error.status === 400 || error.status === 422);
-}
-function errorResult(error, model) {
-  return {
-    status: "error",
-    reason: error instanceof Error ? error.message : "Unknown Ollama error",
-    model,
-    result: null,
-  };
-}
-function conciseError(text) {
-  if (!text) return "empty error body";
-  try {
-    const parsed = JSON.parse(text);
-    return parsed.error?.message || parsed.error || parsed.message || JSON.stringify(parsed).slice(0, 500);
-  } catch {
-    return text.slice(0, 500);
-  }
-}
-class OllamaRequestError extends Error {
-  constructor(status, message) {
-    super(`Ollama request failed with HTTP ${status}: ${message}`);
-    this.name = "OllamaRequestError";
-    this.status = status;
-  }
-}

server/research-tools.mjs CHANGED Viewed

@@ -114,34 +114,6 @@ const TORONTO_DATASETS = {
   },
 };
-const SOURCE_CATEGORIES = [
-  {
-    category: "listings",
-    sourceType: "market_listing",
-    domains: ["realtor.ca", "zillow.com", "rentals.ca", "condos.ca", "zumper.com", "padmapper.com"],
-  },
-  {
-    category: "community",
-    sourceType: "resident_discussion",
-    domains: ["reddit.com/r/askTO", "reddit.com/r/TorontoRenting", "reddit.com/r/toronto"],
-  },
-  {
-    category: "reviews",
-    sourceType: "local_reviews",
-    domains: ["google.com/maps", "yelp.ca", "tripadvisor.ca"],
-  },
-  {
-    category: "official",
-    sourceType: "official_context",
-    domains: ["toronto.ca", "ttc.ca", "torontopolice.on.ca", "metrolinx.com"],
-  },
-  {
-    category: "market",
-    sourceType: "market_context",
-    domains: ["zolo.ca", "housesigma.com", "strata.ca", "rentals.ca/blog", "wowa.ca"],
-  },
-];
 const officialDataCache = new Map();
 export async function runHousingResearch(localRun, env = process.env) {

   },
 };
 const officialDataCache = new Map();
 export async function runHousingResearch(localRun, env = process.env) {

src/App.tsx CHANGED Viewed

@@ -26,13 +26,13 @@ import {
 import MapCanvas from "./components/MapCanvas";
 import { DIMENSIONS, type DimensionKey, type LayerKey } from "./data/neighborhoods";
 import { runAgentBackend, type AgentBackendRun } from "./lib/agentApi";
 import {
   agentDimension,
   cityAgents,
   colorForScore,
   consensus,
   defaultPrompt,
-  formatRentRange,
   getAgentFinding,
   getAgentThinking,
   initialAgentStates,
@@ -684,31 +684,57 @@ function DetailPanel({
   recommendation: AgentBackendRun["recommendation"] | null;
 }) {
   const fit = consensus(selected.overall);
-  const [playing, setPlaying] = useState(false);
   const stopSpeech = useCallback(() => {
     if (typeof window !== "undefined" && window.speechSynthesis) window.speechSynthesis.cancel();
-    setPlaying(false);
   }, []);
   // Stop narration when the neighbourhood changes or the panel unmounts.
   useEffect(() => stopSpeech, [selected.id, stopSpeech]);
-  const togglePlay = () => {
-    if (typeof window === "undefined" || !window.speechSynthesis) return;
-    if (playing) {
       stopSpeech();
       return;
     }
-    const utterance = new SpeechSynthesisUtterance(buildSpokenSummary(selected, recommendation, webResearch));
-    utterance.rate = 1.02;
-    utterance.pitch = 1;
-    utterance.onend = () => setPlaying(false);
-    utterance.onerror = () => setPlaying(false);
-    window.speechSynthesis.cancel();
-    window.speechSynthesis.speak(utterance);
-    setPlaying(true);
   };
   const safetyFact = findNeighborhoodFact(webResearch, selected.name, "safety");
   const rentFact = findNeighborhoodFact(webResearch, selected.name, "rent");
   const commuteFact = findNeighborhoodFact(webResearch, selected.name, "commute");
@@ -831,8 +857,14 @@ function DetailPanel({
             onClick={togglePlay}
             aria-label={playing ? "Stop summary audio" : "Play summary audio"}
           >
-            {playing ? <Square size={14} fill="currentColor" /> : <Volume2 size={15} />}
-            {playing ? "Stop" : "Play"}
           </button>
         </div>
       </section>
@@ -1079,13 +1111,6 @@ function CompareModal({
   );
 }
-function ScoreBadge({ score }: { score: number }) {
-  return (
-    <span className="score-badge" style={{ background: colorForScore(score || 1) }}>
-      {score}
-    </span>
-  );
-}
 function TrustBadge({ sourced }: { sourced: boolean }) {
   return <span className={`trust-badge ${sourced ? "sourced" : "pending"}`}>{sourced ? "Sourced" : "Needs source"}</span>;
@@ -1162,7 +1187,48 @@ function buildSpokenSummary(
   }
   const text = parts.join(" ").replace(/\s+/g, " ").trim();
   const words = text.split(" ");
-  return words.length > 60 ? `${words.slice(0, 60).join(" ").replace(/[,;:]+$/, "")}.` : text;
 }
 function TowerLogo() {
@@ -1186,80 +1252,6 @@ function TowerLogo() {
   );
 }
-function Sparkline({ selected }: { selected: RankedNeighborhood }) {
-  const { path, area } = useMemo(() => {
-    const count = 26;
-    const width = 240;
-    const height = 56;
-    const pad = 5;
-    const values = Array.from({ length: count }, (_, index) => {
-      return (
-        10 +
-        (selected.trend / 12) * index * 1.15 +
-        Math.sin(index * 1.3 + selected.name.length) * 3 +
-        Math.cos(index * 0.7 + selected.seed) * 2
-      );
-    });
-    const min = Math.min(...values);
-    const max = Math.max(...values);
-    const x = (index: number) => (index / (count - 1)) * (width - pad * 2) + pad;
-    const y = (value: number) =>
-      height - pad - ((value - min) / ((max - min) || 1)) * (height - pad * 2);
-    const line = values
-      .map((value, index) => `${index ? "L" : "M"}${x(index).toFixed(1)} ${y(value).toFixed(1)}`)
-      .join(" ");
-    return {
-      path: line,
-      area: `${line} L ${x(count - 1).toFixed(1)} ${height} L ${x(0).toFixed(1)} ${height} Z`,
-    };
-  }, [selected]);
-  return (
-    <svg viewBox="0 0 240 56" preserveAspectRatio="none" aria-hidden="true">
-      <path d={area} fill="rgba(95, 127, 170, 0.16)" />
-      <path d={path} fill="none" stroke="#46648d" strokeWidth="2" strokeLinecap="round" />
-    </svg>
-  );
-}
-function buildWhy(neighborhood: RankedNeighborhood) {
-  const rows: Array<{ tone: "good" | "warn"; text: string }> = [];
-  const candidates: Array<[DimensionKey, string]> = [
-    ["commute", `${neighborhood.comLo}-${neighborhood.comHi} min to Union via ${neighborhood.comMode}`],
-    ["affordability", "Lower rent pressure than the downtown core"],
-    ["amenities", "Strong grocery, cafe and service access"],
-    ["lifestyle", neighborhood.lifeHi],
-    ["transit", "Strong transit access"],
-    ["safety", "Relatively strong safety signals"],
-    ["growth", neighborhood.growthNote],
-  ];
-  candidates.forEach(([dimension, text]) => {
-    if (rows.length < 4 && neighborhood.dims[dimension] >= 75) rows.push({ tone: "good", text });
-  });
-  if (rows.length < 3) {
-    candidates.forEach(([, text]) => {
-      if (rows.length < 4 && !rows.some((row) => row.text === text)) rows.push({ tone: "good", text });
-    });
-  }
-  const weakest = candidates.reduce((low, current) =>
-    neighborhood.dims[current[0]] < neighborhood.dims[low[0]] ? current : low,
-  );
-  const warnings: Record<DimensionKey, string> = {
-    affordability: "Pricey at the very top end",
-    safety: "Mixed safety signals, verify locally",
-    commute: "Longer commute on some routes",
-    transit: "Transit can need a transfer",
-    amenities: "Fewer late-night options",
-    lifestyle: "Quieter after dark",
-    growth: "Slower projected upside",
-  };
-  rows.push({ tone: "warn", text: warnings[weakest[0]] });
-  return rows;
-}
 function priorityChips(parsed: ParsedPrompt) {
   const meta: Record<DimensionKey, [string, string]> = {
     affordability: ["Affordability", "#5f7faa"],
@@ -1336,25 +1328,6 @@ function alpha(hex: string, value: number) {
   return `rgba(${red},${green},${blue},${value})`;
 }
-function hasNeighborhoodSource(
-  webResearch: AgentBackendRun["webResearch"] | null,
-  neighborhood: string,
-  sourceTypes: string[],
-) {
-  if (!webResearch?.enabled) return false;
-  const target = normalizeEvidenceName(neighborhood);
-  return webResearch.sources.some((source) => {
-    const sourceNeighborhood = normalizeEvidenceName(source.neighborhood);
-    const sourceTypeMatch = sourceTypes.includes(source.sourceType);
-    const neighborhoodMatch =
-      !sourceNeighborhood ||
-      sourceNeighborhood.includes(target) ||
-      target.includes(sourceNeighborhood) ||
-      source.neighborhood.includes(", ");
-    return sourceTypeMatch && neighborhoodMatch;
-  });
-}
 function findNeighborhoodFact(
   webResearch: AgentBackendRun["webResearch"] | null,
   neighborhood: string,

 import MapCanvas from "./components/MapCanvas";
 import { DIMENSIONS, type DimensionKey, type LayerKey } from "./data/neighborhoods";
 import { runAgentBackend, type AgentBackendRun } from "./lib/agentApi";
+import { synthesizeKokoro } from "./lib/tts";
 import {
   agentDimension,
   cityAgents,
   colorForScore,
   consensus,
   defaultPrompt,
   getAgentFinding,
   getAgentThinking,
   initialAgentStates,
   recommendation: AgentBackendRun["recommendation"] | null;
 }) {
   const fit = consensus(selected.overall);
+  const [audioState, setAudioState] = useState<"idle" | "loading" | "playing">("idle");
+  const audioRef = useRef<HTMLAudioElement | null>(null);
+  const reqRef = useRef(0);
   const stopSpeech = useCallback(() => {
+    reqRef.current += 1; // invalidate any in-flight synthesis
+    if (audioRef.current) {
+      audioRef.current.pause();
+      if (audioRef.current.src) URL.revokeObjectURL(audioRef.current.src);
+      audioRef.current = null;
+    }
     if (typeof window !== "undefined" && window.speechSynthesis) window.speechSynthesis.cancel();
+    setAudioState("idle");
   }, []);
   // Stop narration when the neighbourhood changes or the panel unmounts.
   useEffect(() => stopSpeech, [selected.id, stopSpeech]);
+  const togglePlay = async () => {
+    if (audioState !== "idle") {
       stopSpeech();
       return;
     }
+    const text = buildSpokenSummary(selected, recommendation, webResearch);
+    const req = (reqRef.current += 1);
+    setAudioState("loading");
+    try {
+      const url = await synthesizeKokoro(text);
+      if (reqRef.current !== req) {
+        URL.revokeObjectURL(url);
+        return;
+      }
+      const element = new Audio(url);
+      audioRef.current = element;
+      element.onended = () => {
+        URL.revokeObjectURL(url);
+        if (reqRef.current === req) setAudioState("idle");
+      };
+      await element.play();
+      if (reqRef.current === req) setAudioState("playing");
+    } catch {
+      // Kokoro unavailable (still downloading / unsupported) — fall back to the browser voice.
+      if (reqRef.current !== req) return;
+      speakWithBrowser(text, () => {
+        if (reqRef.current === req) setAudioState("idle");
+      });
+      setAudioState("playing");
+    }
   };
+  const playing = audioState !== "idle";
   const safetyFact = findNeighborhoodFact(webResearch, selected.name, "safety");
   const rentFact = findNeighborhoodFact(webResearch, selected.name, "rent");
   const commuteFact = findNeighborhoodFact(webResearch, selected.name, "commute");
             onClick={togglePlay}
             aria-label={playing ? "Stop summary audio" : "Play summary audio"}
           >
+            {audioState === "loading" ? (
+              <LoaderCircle size={14} className="spin" />
+            ) : audioState === "playing" ? (
+              <Square size={14} fill="currentColor" />
+            ) : (
+              <Volume2 size={15} />
+            )}
+            {audioState === "loading" ? "Voice…" : audioState === "playing" ? "Stop" : "Play"}
           </button>
         </div>
       </section>
   );
 }
 function TrustBadge({ sourced }: { sourced: boolean }) {
   return <span className={`trust-badge ${sourced ? "sourced" : "pending"}`}>{sourced ? "Sourced" : "Needs source"}</span>;
   }
   const text = parts.join(" ").replace(/\s+/g, " ").trim();
   const words = text.split(" ");
+  return words.length > 55 ? `${words.slice(0, 55).join(" ").replace(/[,;:]+$/, "")}.` : text;
+}
+// Choose the most natural-sounding English voice the browser offers. Cloud/"Natural" voices
+// (non-localService) sound far smoother than the default robotic system voice.
+function pickVoice(voices: SpeechSynthesisVoice[]) {
+  if (!voices.length) return null;
+  const english = voices.filter((voice) => /^en[-_]?/i.test(voice.lang));
+  const pool = english.length ? english : voices;
+  const score = (voice: SpeechSynthesisVoice) => {
+    let value = 0;
+    if (/natural|neural|premium|enhanced/i.test(voice.name)) value += 100;
+    if (/google/i.test(voice.name)) value += 60;
+    if (/(samantha|ava|allison|serena|zoe|jenny|aria|libby|sonia|nora|emma)/i.test(voice.name)) value += 50;
+    if (!voice.localService) value += 30;
+    if (/en[-_]US/i.test(voice.lang)) value += 12;
+    if (/en[-_](CA|GB|AU)/i.test(voice.lang)) value += 8;
+    if (/zira|david|mark|microsoft/i.test(voice.name)) value -= 15;
+    return value;
+  };
+  return [...pool].sort((a, b) => score(b) - score(a))[0] ?? null;
+}
+// Fallback narration using the browser's built-in speech, for the brief window while Kokoro
+// downloads or on browsers where it can't run.
+function speakWithBrowser(text: string, onEnd: () => void) {
+  if (typeof window === "undefined" || !window.speechSynthesis) {
+    onEnd();
+    return;
+  }
+  const utterance = new SpeechSynthesisUtterance(text);
+  const voice = pickVoice(window.speechSynthesis.getVoices());
+  if (voice) {
+    utterance.voice = voice;
+    utterance.lang = voice.lang;
+  }
+  utterance.rate = 0.96;
+  utterance.pitch = 1.02;
+  utterance.onend = onEnd;
+  utterance.onerror = onEnd;
+  window.speechSynthesis.cancel();
+  window.speechSynthesis.speak(utterance);
 }
 function TowerLogo() {
   );
 }
 function priorityChips(parsed: ParsedPrompt) {
   const meta: Record<DimensionKey, [string, string]> = {
     affordability: ["Affordability", "#5f7faa"],
   return `rgba(${red},${green},${blue},${value})`;
 }
 function findNeighborhoodFact(
   webResearch: AgentBackendRun["webResearch"] | null,
   neighborhood: string,

src/data/neighborhoods.ts CHANGED Viewed

@@ -47,11 +47,6 @@ export interface Neighborhood {
   tradeoff: string;
 }
-export interface NeighborhoodFeatureProperties {
-  id: string;
-  name: string;
-}
 export const DIMENSIONS: DimensionKey[] = [
   "affordability",
   "safety",
@@ -442,48 +437,3 @@ export const neighborhoods: Neighborhood[] = [
     tradeoff: "Well over the rent cap; you pay for the convenience.",
   },
 ];
-export const unionStation: LngLat = [-79.3806, 43.6452];
-function mockBoundary(
-  [lng, lat]: LngLat,
-  radiusLng: number,
-  radiusLat: number,
-  seed: number,
-): LngLat[] {
-  const points: LngLat[] = [];
-  for (let i = 0; i < 18; i += 1) {
-    const angle = (Math.PI * 2 * i) / 18;
-    const wobble = 1 + Math.sin(seed * 1.7 + i * 0.9) * 0.14;
-    points.push([
-      Number((lng + Math.cos(angle) * radiusLng * wobble).toFixed(6)),
-      Number((lat + Math.sin(angle) * radiusLat * wobble).toFixed(6)),
-    ]);
-  }
-  points.push(points[0]);
-  return points;
-}
-export function buildNeighborhoodGeoJson(rows = neighborhoods) {
-  return {
-    type: "FeatureCollection",
-    features: rows.map((neighborhood) => ({
-      type: "Feature",
-      properties: {
-        id: neighborhood.id,
-        name: neighborhood.name,
-      } satisfies NeighborhoodFeatureProperties,
-      geometry: {
-        type: "Polygon",
-        coordinates: [
-          mockBoundary(
-            neighborhood.center,
-            neighborhood.radiusLng,
-            neighborhood.radiusLat,
-            neighborhood.seed,
-          ),
-        ],
-      },
-    })),
-  };
-}

   tradeoff: string;
 }
 export const DIMENSIONS: DimensionKey[] = [
   "affordability",
   "safety",
     tradeoff: "Well over the rent cap; you pay for the convenience.",
   },
 ];

src/lib/tts.ts ADDED Viewed

	@@ -0,0 +1,41 @@

+// Kokoro-82M neural text-to-speech, run entirely in the browser via transformers.js (ONNX).
+// 82M params — well under the hackathon's 32B limit — with no API key and no backend. The
+// model (~80MB, quantised) downloads from the Hugging Face CDN on first use, then is cached.
+const MODEL_ID = "onnx-community/Kokoro-82M-v1.0-ONNX";
+const DEFAULT_VOICE = "af_heart"; // warm, natural American female
+// kokoro-js has no bundled types; keep the surface we use minimal.
+type KokoroModel = {
+  generate: (text: string, options: { voice: string }) => Promise<{ toBlob: () => Blob }>;
+};
+let modelPromise: Promise<KokoroModel> | null = null;
+async function loadModel(): Promise<KokoroModel> {
+  if (!modelPromise) {
+    modelPromise = (async () => {
+      const { KokoroTTS } = await import("kokoro-js");
+      return (await KokoroTTS.from_pretrained(MODEL_ID, {
+        dtype: "q8",
+        device: "wasm",
+      })) as unknown as KokoroModel;
+    })().catch((error) => {
+      modelPromise = null; // allow a retry on next click
+      throw error;
+    });
+  }
+  return modelPromise;
+}
+// Warm the model in the background so the first Play click is fast.
+export function preloadKokoro(): void {
+  void loadModel().catch(() => {});
+}
+// Synthesize text and return an object-URL for an audio blob. Caller plays + revokes it.
+export async function synthesizeKokoro(text: string, voice: string = DEFAULT_VOICE): Promise<string> {
+  const model = await loadModel();
+  const audio = await model.generate(text, { voice });
+  return URL.createObjectURL(audio.toBlob());
+}

src/styles.css CHANGED Viewed

@@ -260,6 +260,14 @@ textarea:focus-visible {
   50% { box-shadow: 0 0 0 5px rgba(48, 75, 120, 0); }
 }
 .map-scan-fx {
   position: absolute;
   inset: 0;

   50% { box-shadow: 0 0 0 5px rgba(48, 75, 120, 0); }
 }
+.secondary-action .spin {
+  animation: tts-spin 0.9s linear infinite;
+}
+@keyframes tts-spin {
+  to { transform: rotate(360deg); }
+}
 .map-scan-fx {
   position: absolute;
   inset: 0;