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Welcome to Val Town ChatGPT

\n Type your message below to start a conversation. Check out the source here:{\" \"}\n \n https://www.val.town/v/maxm/valTownChatGPT\n \n

\n \n \n \n \n \n

\n \n ,\n );\n});\n\napp.post(\"/post-message\", async (c) => {\n let message = await c.req.text();\n try {\n await openai.chat.completions.create({\n model: \"gpt-3.5-turbo\", // Use a faster model\n messages: [{ role: \"user\", content: message }],\n });\n } catch (error) {\n console.error(\"Error creating message:\", error);\n return new Response(\"Error creating message: \" + error.message, { status: 500 });\n }\n return new Response(null, { status: 200 });\n});\n\napp.get(\"/sse-response\", async (c) => {\n const threadId = c.req.query(\"threadId\");\n const assistantId = c.req.query(\"assistantId\");\n\n const body = new ReadableStream({\n start(controller) {\n const write = (str) => {\n controller.enqueue(new TextEncoder().encode(\n \"data: \" + JSON.stringify(str) + \"\\n\\n\",\n ));\n };\n const run = openai.chat.completions.stream({\n model: \"gpt-3.5-turbo\", // Use a faster model\n messages: [{ role: \"user\", content: \"Continue the conversation\" }],\n })\n .on(\"textCreated\", (text) => {})\n .on(\"textDelta\", (textDelta, snapshot) => write(textDelta.value))\n .on(\"textDone\", async () => {\n controller.close();\n })\n .on(\"error\", (error) => {\n write(\"Error: \" + error.message);\n controller.close();\n });\n },\n });\n\n return new Response(body, { headers: { \"Content-Type\": \"text/event-stream\" } });\n});\n\nexport default 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@jsxImportSource https://esm.sh/react */\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nimport { Hono } from \"npm:hono@3\";\nimport { renderToString } from \"npm:react-dom/server\";\n\nconst openai = new OpenAI();\n\nconst jsxResponse = (jsx) => {\n return new Response(renderToString(jsx), { headers: { \"Content-Type\": \"text/html\" } });\n};\n\nconst clientCode = () => {\n document.getElementById(\"input\").addEventListener(\"submit\", async function(event) {\n event.preventDefault();\n\n const msgParent = document.createElement(\"div\");\n msgParent.className = \"flex justify-end\";\n document.querySelector(\".messages\").appendChild(msgParent);\n const msgDiv = document.createElement(\"div\");\n msgDiv.className = \"rounded-lg p-2 m-2 max-w-xl bg-blue-500 text-white\";\n msgDiv.textContent = event.target.message.value;\n msgParent.appendChild(msgDiv);\n // Clear the form input\n this.reset();\n\n const responseDiv = document.createElement(\"pre\");\n responseDiv.className = \"p-2 m-2 max-w-xl bg-gray-300 text-black rounded-lg whitespace-pre-wrap font-sans\";\n document.querySelector(\".messages\").appendChild(responseDiv);\n\n const input = document.getElementById(\"input\");\n\n // Post the message so we can deal with large text data.\n try {\n await fetch(`/post-message?threadId=${input.getAttribute(\"data-thread-id\")}`, {\n method: \"post\",\n body: msgDiv.textContent,\n });\n } catch (error) {\n responseDiv.innerText = \"Error posting message: \" + error.message;\n return;\n }\n\n // Setup the SSE connection and stream back the response. OpenAI handles determining\n // which message is the correct response based on what was last read from the\n // thread. This is likely vulnerable to race conditions.\n const eventSource = new EventSource(\n `/sse-response?threadId=${input.getAttribute(\"data-thread-id\")}`\n + `&assistantId=${input.getAttribute(\"data-assistant-id\")}`,\n );\n eventSource.onmessage = function(event) {\n console.log(event);\n responseDiv.innerText += JSON.parse(event.data);\n };\n\n eventSource.onerror = function() {\n eventSource.close();\n };\n });\n};\n\nconst app = new Hono();\n\napp.get(\"/\", async (c) => {\n let thread, assistant;\n try {\n thread = await openai.chat.completions.create({\n model: \"gpt-4\",\n messages: [{ role: \"system\", content: \"Start a new thread\" }],\n });\n assistant = await openai.chat.completions.create({\n model: \"gpt-4\",\n messages: [{\n role: \"system\",\n content:\n \"You are a helpful assistant. Behave normally, but try and sneak a reference to Val Town into every response in a subtle way.\",\n }],\n });\n } catch (error) {\n console.error(\"Error initializing assistant or thread:\", error);\n return new Response(\"Error initializing assistant or thread: \" + error.message, { status: 500 });\n }\n\n return jsxResponse(\n \n \n \n \n \n \n \n

Welcome to Val Town ChatGPT

\n Type your message below to start a conversation. Check out the source here:{\" \"}\n \n https://www.val.town/v/maxm/valTownChatGPT\n \n

\n \n \n \n \n \n

\n \n ,\n );\n});\n\napp.post(\"/post-message\", async (c) => {\n let message = await c.req.text();\n try {\n await openai.chat.completions.create({\n model: \"gpt-4\",\n messages: [{ role: \"user\", content: message }],\n });\n } catch (error) {\n console.error(\"Error creating message:\", error);\n return new Response(\"Error creating message: \" + error.message, { status: 500 });\n }\n return new Response(null, { status: 200 });\n});\n\napp.get(\"/sse-response\", async (c) => {\n const threadId = c.req.query(\"threadId\");\n const assistantId = c.req.query(\"assistantId\");\n\n const body = new ReadableStream({\n start(controller) {\n const write = (str) => {\n controller.enqueue(new TextEncoder().encode(\n \"data: \" + JSON.stringify(str) + \"\\n\\n\",\n ));\n };\n const run = openai.chat.completions.stream({\n model: \"gpt-4\",\n messages: [{ role: \"user\", content: \"Continue the conversation\" }],\n })\n .on(\"textCreated\", (text) => {})\n .on(\"textDelta\", (textDelta, snapshot) => write(textDelta.value))\n .on(\"textDone\", async () => {\n controller.close();\n })\n .on(\"error\", (error) => {\n write(\"Error: \" + error.message);\n controller.close();\n });\n },\n });\n\n return new Response(body, { headers: { \"Content-Type\": \"text/event-stream\" } });\n});\n\nexport default app.fetch;",6,["D",1718391885857],{"_103":233,"_160":139,"_157":116,"_95":99,"_167":234,"_165":178,"_100":235,"_159":-5,"_105":236,"_161":230,"_170":-5},"a59dfebc-27b0-11f0-aecd-569c3dd06744","/** @jsxImportSource https://esm.sh/react */\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nimport { Hono } from \"npm:hono@3\";\nimport { renderToString } from \"npm:react-dom/server\";\n\nconst openai = new OpenAI();\n\nconst jsxResponse = (jsx) => {\n return new Response(renderToString(jsx), { headers: { \"Content-Type\": \"text/html\" } });\n};\n\nconst clientCode = () => {\n document.getElementById(\"input\").addEventListener(\"submit\", async function(event) {\n event.preventDefault();\n\n const msgParent = document.createElement(\"div\");\n msgParent.className = \"flex justify-end\";\n document.querySelector(\".messages\").appendChild(msgParent);\n const msgDiv = document.createElement(\"div\");\n msgDiv.className = \"rounded-lg p-2 m-2 max-w-xl bg-blue-500 text-white\";\n msgDiv.textContent = event.target.message.value;\n msgParent.appendChild(msgDiv);\n // Clear the form input\n this.reset();\n\n const responseDiv = document.createElement(\"pre\");\n responseDiv.className = \"p-2 m-2 max-w-xl bg-gray-300 text-black rounded-lg whitespace-pre-wrap font-sans\";\n document.querySelector(\".messages\").appendChild(responseDiv);\n\n const input = document.getElementById(\"input\");\n\n // Post the message so we can deal with large text data.\n try {\n await fetch(`/post-message?threadId=${input.getAttribute(\"data-thread-id\")}`, {\n method: \"post\",\n body: msgDiv.textContent,\n });\n } catch (error) {\n responseDiv.innerText = \"Error posting message: \" + error.message;\n return;\n }\n\n // Setup the SSE connection and stream back the response. OpenAI handles determining\n // which message is the correct response based on what was last read from the\n // thread. This is likely vulnerable to race conditions.\n const eventSource = new EventSource(\n `/sse-response?threadId=${input.getAttribute(\"data-thread-id\")}`\n + `&assistantId=${input.getAttribute(\"data-assistant-id\")}`,\n );\n eventSource.onmessage = function(event) {\n console.log(event);\n responseDiv.innerText += JSON.parse(event.data);\n };\n\n eventSource.onerror = function() {\n eventSource.close();\n };\n });\n};\n\nconst app = new Hono();\napp.get(\"/\", async (c) => {\n let thread, assistant;\n try {\n thread = await openai.chat.completions.create({\n model: \"gpt-4\",\n messages: [{ role: \"system\", content: \"Start a new thread\" }],\n });\n assistant = await openai.chat.completions.create({\n model: \"gpt-4\",\n messages: [{\n role: \"system\",\n content:\n \"You are a helpful assistant who defines every instance of emojis, phrases, slang, and unfamiliar words in continuous prose style. The only input is what needs to be clarified and Clarifier will define it. It always avoids using number and bullet point format. It maintains a neutral tone without leaning towards formal, casual, or humorous styles. It asks for clarification when needed and then tries to make the best guess when defining terms. Clarifier includes cultural and situational significance in its definitions. If the language isn't English, it provides a translation. When there are multiple possible meanings, Clarifier provides all relevant definitions in a single response, with each definition getting a separate paragraph. Each definition gets the minimum of one paragraph. Clarifier provides at least 10 definitions each time.\",\n }],\n });\n } catch (error) {\n return new Response(\"Error initializing assistant or thread: \" + error.message, { status: 500 });\n }\n\n return jsxResponse(\n \n \n \n \n \n \n \n

Welcome to Val Town ChatGPT

\n Type your message below to start a conversation. Check out the source here:{\" \"}\n \n https://www.val.town/v/maxm/valTownChatGPT\n \n

\n \n \n \n \n \n

\n \n ,\n );\n});\n\napp.post(\"/post-message\", async (c) => {\n let message = await c.req.text();\n try {\n await openai.chat.completions.create({\n model: \"gpt-4\",\n messages: [{ role: \"user\", content: message }],\n });\n } catch (error) {\n return new Response(\"Error creating message: \" + error.message, { status: 500 });\n }\n return new Response(null, { status: 200 });\n});\n\napp.get(\"/sse-response\", async (c) => {\n const threadId = c.req.query(\"threadId\");\n const assistantId = c.req.query(\"assistantId\");\n\n const body = new ReadableStream({\n start(controller) {\n const write = (str) => {\n controller.enqueue(new TextEncoder().encode(\n \"data: \" + JSON.stringify(str) + \"\\n\\n\",\n ));\n };\n const run = openai.chat.completions.stream({\n model: \"gpt-4\",\n messages: [{ role: \"user\", content: \"Continue the conversation\" }],\n })\n .on(\"textCreated\", (text) => {})\n .on(\"textDelta\", (textDelta, snapshot) => write(textDelta.value))\n .on(\"textDone\", async () => {\n controller.close();\n })\n .on(\"error\", (error) => {\n write(\"Error: \" + error.message);\n controller.close();\n });\n },\n });\n\n return new Response(body, { headers: { \"Content-Type\": \"text/event-stream\" } });\n});\n\nexport default app.fetch;",5,["D",1718391736303],{"_103":238,"_160":139,"_157":116,"_95":99,"_167":239,"_165":178,"_100":240,"_159":-5,"_105":241,"_161":235,"_170":-5},"a5992a68-27b0-11f0-aecd-569c3dd06744","/** @jsxImportSource https://esm.sh/react */\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nimport { Hono } from \"npm:hono@3\";\nimport { renderToString } from \"npm:react-dom/server\";\n\nconst openai = new OpenAI();\n\nconst jsxResponse = (jsx) => {\n return new Response(renderToString(jsx), { headers: { \"Content-Type\": \"text/html\" } });\n};\n\nconst clientCode = () => {\n document.getElementById(\"input\").addEventListener(\"submit\", async function(event) {\n event.preventDefault();\n\n const msgParent = document.createElement(\"div\");\n msgParent.className = \"flex justify-end\";\n document.querySelector(\".messages\").appendChild(msgParent);\n const msgDiv = document.createElement(\"div\");\n msgDiv.className = \"rounded-lg p-2 m-2 max-w-xl bg-blue-500 text-white\";\n msgDiv.textContent = event.target.message.value;\n msgParent.appendChild(msgDiv);\n // Clear the form input\n this.reset();\n\n const responseDiv = document.createElement(\"pre\");\n responseDiv.className = \"p-2 m-2 max-w-xl bg-gray-300 text-black rounded-lg whitespace-pre-wrap font-sans\";\n document.querySelector(\".messages\").appendChild(responseDiv);\n\n const input = document.getElementById(\"input\");\n\n // Post the message so we can deal with large text data.\n try {\n await fetch(`/post-message?threadId=${input.getAttribute(\"data-thread-id\")}`, {\n method: \"post\",\n body: msgDiv.textContent,\n });\n } catch (error) {\n responseDiv.innerText = \"Error posting message: \" + error.message;\n return;\n }\n\n // Setup the SSE connection and stream back the response. OpenAI handles determining\n // which message is the correct response based on what was last read from the\n // thread. This is likely vulnerable to race conditions.\n const eventSource = new EventSource(\n `/sse-response?threadId=${input.getAttribute(\"data-thread-id\")}`\n + `&assistantId=${input.getAttribute(\"data-assistant-id\")}`,\n );\n eventSource.onmessage = function(event) {\n console.log(event);\n responseDiv.innerText += JSON.parse(event.data);\n };\n\n eventSource.onerror = function() {\n eventSource.close();\n };\n });\n};\n\nconst app = new Hono();\napp.get(\"/\", async (c) => {\n let thread, assistant;\n try {\n thread = await openai.chat.completions.create({\n model: \"gpt-4\",\n messages: [{ role: \"system\", content: \"Start a new thread\" }],\n });\n assistant = await openai.chat.completions.create({\n model: \"gpt-4\",\n messages: [{\n role: \"system\",\n content:\n \"You are a helpful assistant. Behave normally, but try and sneak a reference to Val Town into every response in a subtle way.\",\n }],\n });\n } catch (error) {\n return new Response(\"Error initializing assistant or thread: \" + error.message, { status: 500 });\n }\n\n return jsxResponse(\n \n \n \n \n \n \n \n

Welcome to Val Town ChatGPT

\n Type your message below to start a conversation. Check out the source here:{\" \"}\n \n https://www.val.town/v/maxm/valTownChatGPT\n \n

\n \n \n \n \n \n

\n \n ,\n );\n});\n\napp.post(\"/post-message\", async (c) => {\n let message = await c.req.text();\n try {\n await openai.chat.completions.create({\n model: \"gpt-4\",\n messages: [{ role: \"user\", content: message }],\n });\n } catch (error) {\n return new Response(\"Error creating message: \" + error.message, { status: 500 });\n }\n return new Response(null, { status: 200 });\n});\n\napp.get(\"/sse-response\", async (c) => {\n const threadId = c.req.query(\"threadId\");\n const assistantId = c.req.query(\"assistantId\");\n\n const body = new ReadableStream({\n start(controller) {\n const write = (str) => {\n controller.enqueue(new TextEncoder().encode(\n \"data: \" + JSON.stringify(str) + \"\\n\\n\",\n ));\n };\n const run = openai.chat.completions.stream({\n model: \"gpt-4\",\n messages: [{ role: \"user\", content: \"Continue the conversation\" }],\n })\n .on(\"textCreated\", (text) => {})\n .on(\"textDelta\", (textDelta, snapshot) => write(textDelta.value))\n .on(\"textDone\", async () => {\n controller.close();\n })\n .on(\"error\", (error) => {\n write(\"Error: \" + error.message);\n controller.close();\n });\n },\n });\n\n return new Response(body, { headers: { \"Content-Type\": \"text/event-stream\" } });\n});\n\nexport default app.fetch;",4,["D",1718391674094],{"_103":243,"_160":139,"_157":116,"_95":99,"_167":244,"_165":178,"_100":245,"_159":-5,"_105":246,"_161":240,"_170":-5},"a5941690-27b0-11f0-aecd-569c3dd06744","/** @jsxImportSource https://esm.sh/react */\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nimport { renderToString } from \"npm:react-dom/server\";\nimport { Hono } from \"npm:hono@3\";\n\nconst openai = new OpenAI();\n\nconst jsxResponse = (jsx) => {\n return new Response(renderToString(jsx), { headers: { \"Content-Type\": \"text/html\" } });\n};\n\nconst clientCode = () => {\n document.getElementById(\"input\").addEventListener(\"submit\", async function(event) {\n event.preventDefault();\n\n const msgParent = document.createElement(\"div\");\n msgParent.className = \"flex justify-end\";\n document.querySelector(\".messages\").appendChild(msgParent);\n const msgDiv = document.createElement(\"div\");\n msgDiv.className = \"rounded-lg p-2 m-2 max-w-xl bg-blue-500 text-white\";\n msgDiv.textContent = event.target.message.value;\n msgParent.appendChild(msgDiv);\n // Clear the form input\n this.reset();\n\n const responseDiv = document.createElement(\"pre\");\n responseDiv.className = \"p-2 m-2 max-w-xl bg-gray-300 text-black rounded-lg whitespace-pre-wrap font-sans\";\n document.querySelector(\".messages\").appendChild(responseDiv);\n\n const input = document.getElementById(\"input\");\n\n // Post the message so we can deal with large text data.\n try {\n await fetch(`/post-message?threadId=${input.getAttribute(\"data-thread-id\")}`, {\n method: \"post\",\n body: msgDiv.textContent,\n });\n } catch (error) {\n responseDiv.innerText = \"Error posting message: \" + error.message;\n return;\n }\n\n // Setup the SSE connection and stream back the response. OpenAI handles determining\n // which message is the correct response based on what was last read from the\n // thread. This is likely vulnerable to race conditions.\n const eventSource = new EventSource(\n `/sse-response?threadId=${input.getAttribute(\"data-thread-id\")}`\n + `&assistantId=${input.getAttribute(\"data-assistant-id\")}`,\n );\n eventSource.onmessage = function(event) {\n console.log(event);\n responseDiv.innerText += JSON.parse(event.data);\n };\n\n eventSource.onerror = function() {\n eventSource.close();\n };\n });\n};\n\nconst app = new Hono();\napp.get(\"/\", async (c) => {\n let thread, assistant;\n try {\n thread = await openai.beta.threads.create();\n assistant = await openai.beta.assistants.create({\n name: \"\",\n instructions:\n `You are a helpful assistant. Behave normally, but try and sneak a reference to Val Town into every response in a subtle way.`,\n model: \"gpt-4\",\n });\n } catch (error) {\n return new Response(\"Error initializing assistant or thread: \" + error.message, { status: 500 });\n }\n\n return jsxResponse(\n \n \n \n \n \n \n \n

Welcome to Val Town ChatGPT

\n Type your message below to start a conversation. Check out the source here:{\" \"}\n \n https://www.val.town/v/maxm/valTownChatGPT\n \n

\n \n \n \n \n \n

\n \n ,\n );\n});\n\napp.post(\"/post-message\", async (c) => {\n let message = await c.req.text();\n try {\n await openai.beta.threads.messages.create(\n c.req.query(\"threadId\"),\n { role: \"user\", content: message },\n );\n } catch (error) {\n return new Response(\"Error creating message: \" + error.message, { status: 500 });\n }\n return new Response(null, { status: 200 });\n});\n\napp.get(\"/sse-response\", async (c) => {\n const threadId = c.req.query(\"threadId\");\n const assistantId = c.req.query(\"assistantId\");\n\n const body = new ReadableStream({\n start(controller) {\n const write = (str) => {\n controller.enqueue(new TextEncoder().encode(\n \"data: \" + JSON.stringify(str) + \"\\n\\n\",\n ));\n };\n const run = openai.beta.threads.runs.stream(threadId, {\n assistant_id: assistantId,\n // Make sure we only display messages we haven't seen yet.\n truncation_strategy: { type: \"auto\" },\n })\n .on(\"textCreated\", (text) => {})\n .on(\"textDelta\", (textDelta, snapshot) => write(textDelta.value))\n .on(\"textDone\", async () => {\n controller.close();\n })\n .on(\"error\", (error) => {\n write(\"Error: \" + error.message);\n controller.close();\n });\n },\n });\n\n return new Response(body, { headers: { \"Content-Type\": \"text/event-stream\" } });\n});\n\nexport default app.fetch;\n",3,["D",1718391657419],{"_103":248,"_160":139,"_157":116,"_95":99,"_167":249,"_165":178,"_100":250,"_159":-5,"_105":251,"_161":245,"_170":-5},"a58f6898-27b0-11f0-aecd-569c3dd06744","/** @jsxImportSource https://esm.sh/react */\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nimport { Hono } from \"npm:hono@3\";\nimport { renderToString } from \"npm:react-dom/server\";\n\nconst openai = new OpenAI();\n\nconst jsxResponse = (jsx) => {\n return new Response(renderToString(jsx), { headers: { \"Content-Type\": \"text/html\" } });\n};\n\nconst clientCode = () => {\n document.getElementById(\"input\").addEventListener(\"submit\", async function(event) {\n event.preventDefault();\n\n const msgParent = document.createElement(\"div\");\n msgParent.className = \"flex justify-end\";\n document.querySelector(\".messages\").appendChild(msgParent);\n const msgDiv = document.createElement(\"div\");\n msgDiv.className = \"rounded-lg p-2 m-2 max-w-xl bg-blue-500 text-white\";\n msgDiv.textContent = event.target.message.value;\n msgParent.appendChild(msgDiv);\n // Clear the form input\n this.reset();\n\n const responseDiv = document.createElement(\"pre\");\n responseDiv.className = \"p-2 m-2 max-w-xl bg-gray-300 text-black rounded-lg whitespace-pre-wrap font-sans\";\n document.querySelector(\".messages\").appendChild(responseDiv);\n\n const input = document.getElementById(\"input\");\n\n // Post the message so we can deal with large text data.\n try {\n await fetch(`/post-message?threadId=${input.getAttribute(\"data-thread-id\")}`, {\n method: \"post\",\n body: msgDiv.textContent,\n });\n } catch (error) {\n responseDiv.innerText = \"Error posting message: \" + error.message;\n return;\n }\n\n // Setup the SSE connection and stream back the response. OpenAI handles determining\n // which message is the correct response based on what was last read from the\n // thread. This is likely vulnerable to race conditions.\n const eventSource = new EventSource(\n `/sse-response?threadId=${input.getAttribute(\"data-thread-id\")}`\n + `&assistantId=${input.getAttribute(\"data-assistant-id\")}`,\n );\n eventSource.onmessage = function(event) {\n console.log(event);\n responseDiv.innerText += JSON.parse(event.data);\n };\n\n eventSource.onerror = function() {\n eventSource.close();\n };\n });\n};\n\nconst app = new Hono();\napp.get(\"/\", async (c) => {\n let thread, assistant;\n try {\n thread = await openai.beta.threads.create();\n assistant = await openai.beta.assistants.create({\n name: \"\",\n instructions:\n `You are a helpful assistant. Behave normally, but try and sneak a reference to Val Town into every response in a subtle way.`,\n model: \"gpt-4\",\n });\n } catch (error) {\n return new Response(\"Error initializing assistant or thread: \" + error.message, { status: 500 });\n }\n\n return jsxResponse(\n \n \n \n \n \n \n \n

Welcome to Val Town ChatGPT

\n Type your message below to start a conversation. Check out the source here:{\" \"}\n \n https://www.val.town/v/maxm/valTownChatGPT\n \n

\n \n \n \n \n \n

\n \n ,\n );\n});\n\napp.post(\"/post-message\", async (c) => {\n let message = await c.req.text();\n try {\n await openai.beta.threads.messages.create(\n c.req.query(\"threadId\"),\n { role: \"user\", content: message },\n );\n } catch (error) {\n return new Response(\"Error creating message: \" + error.message, { status: 500 });\n }\n return new Response(null, { status: 200 });\n});\n\napp.get(\"/sse-response\", async (c) => {\n const threadId = c.req.query(\"threadId\");\n const assistantId = c.req.query(\"assistantId\");\n\n const body = new ReadableStream({\n start(controller) {\n const write = (str) => {\n controller.enqueue(new TextEncoder().encode(\n \"data: \" + JSON.stringify(str) + \"\\n\\n\",\n ));\n };\n const run = openai.beta.threads.runs.stream(threadId, {\n assistant_id: assistantId,\n // Make sure we only display messages we haven't seen yet.\n truncation_strategy: { type: \"auto\" },\n })\n .on(\"textCreated\", (text) => {})\n .on(\"textDelta\", (textDelta, snapshot) => write(textDelta.value))\n .on(\"textDone\", async () => {\n controller.close();\n })\n .on(\"error\", (error) => {\n write(\"Error: \" + error.message);\n controller.close();\n });\n },\n });\n\n return new Response(body, { headers: { \"Content-Type\": \"text/event-stream\" } });\n});\n\nexport default app.fetch;",2,["D",1718391571717],{"_103":253,"_160":139,"_157":116,"_95":99,"_167":254,"_165":178,"_100":162,"_159":-5,"_105":255,"_161":250,"_170":-5},"a58990ee-27b0-11f0-aecd-569c3dd06744","/** @jsxImportSource https://esm.sh/react */\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nimport { renderToString } from \"npm:react-dom/server\";\n\n// Use Val Town's OpenAI client\nconst openai = new OpenAI();\nimport { Hono } from \"npm:hono@3\";\n\nconst jsxResponse = (jsx) => {\n return new Response(renderToString(jsx), { headers: { \"Content-Type\": \"text/html\" } });\n};\n\nconst clientCode = () => {\n document.getElementById(\"input\").addEventListener(\"submit\", async function(event) {\n event.preventDefault();\n\n const msgParent = document.createElement(\"div\");\n msgParent.className = \"flex justify-end\";\n document.querySelector(\".messages\").appendChild(msgParent);\n const msgDiv = document.createElement(\"div\");\n msgDiv.className = \"rounded-lg p-2 m-2 max-w-xl bg-blue-500 text-white\";\n msgDiv.textContent = event.target.message.value;\n msgParent.appendChild(msgDiv);\n // Clear the form input\n this.reset();\n\n const responseDiv = document.createElement(\"pre\");\n responseDiv.className = \"p-2 m-2 max-w-xl bg-gray-300 text-black rounded-lg whitespace-pre-wrap font-sans\";\n document.querySelector(\".messages\").appendChild(responseDiv);\n\n const input = document.getElementById(\"input\");\n\n // Post the message so we can deal with large text data.\n await fetch(`/post-message?threadId=${input.getAttribute(\"data-thread-id\")}`, {\n method: \"post\",\n body: msgDiv.textContent,\n });\n\n // Setup the SSE connection and stream back the response. OpenAI handles determining\n // which message is the correct response based on what was last read from the\n // thread. This is likely vulnerable to race conditions.\n const eventSource = new EventSource(\n `/sse-response?threadId=${input.getAttribute(\"data-thread-id\")}`\n + `&assistantId=${input.getAttribute(\"data-assistant-id\")}`,\n );\n eventSource.onmessage = function(event) {\n console.log(event);\n responseDiv.innerText += JSON.parse(event.data);\n };\n\n eventSource.onerror = function() {\n eventSource.close();\n };\n });\n};\n\nconst app = new Hono();\napp.get(\"/\", async (c) => {\n const thread = await openai.beta.threads.create();\n const assistant = await openai.beta.assistants.create({\n name: \"\",\n instructions:\n `You are a helpful assistant. Behave normally, but try and sneak a reference to Val Town into every response in a subtle way.`,\n model: \"gpt-4\",\n });\n\n return jsxResponse(\n \n \n \n \n \n \n \n

Welcome to Val Town ChatGPT

\n Type your message below to start a conversation. Check out the source here:{\" \"}\n \n https://www.val.town/v/maxm/valTownChatGPT\n \n

\n \n \n \n \n \n

\n \n ,\n );\n});\n\napp.post(\"/post-message\", async (c) => {\n let message = await c.req.text();\n await openai.beta.threads.messages.create(\n c.req.query(\"threadId\"),\n { role: \"user\", content: message },\n );\n return new Response(null, { status: 200 });\n});\n\napp.get(\"/sse-response\", async (c) => {\n const threadId = c.req.query(\"threadId\");\n const assistantId = c.req.query(\"assistantId\");\n\n const body = new ReadableStream({\n start(controller) {\n const write = (str) => {\n controller.enqueue(new TextEncoder().encode(\n \"data: \" + JSON.stringify(str) + \"\\n\\n\",\n ));\n };\n const run = openai.beta.threads.runs.stream(threadId, {\n assistant_id: assistantId,\n // Make sure we only display messages we haven't seen yet.\n truncation_strategy: { type: \"auto\" },\n })\n .on(\"textCreated\", (text) => {})\n .on(\"textDelta\", (textDelta, snapshot) => write(textDelta.value))\n .on(\"textDone\", async () => {\n controller.close();\n });\n },\n });\n\n return new Response(body, { headers: { \"Content-Type\": \"text/event-stream\" } });\n});\nexport default app.fetch;\n",["D",1718391502779],{"_103":257,"_160":139,"_157":116,"_95":99,"_167":258,"_165":178,"_100":32,"_159":-5,"_105":259,"_161":162,"_170":-5},"a581e20e-27b0-11f0-aecd-569c3dd06744","/** @jsxImportSource https://esm.sh/react */\nimport OpenAI from \"npm:openai\";\nimport { renderToString } from \"npm:react-dom/server\";\n\n// This uses by personal API key, you'll need to provide your own if\n// you fork this. We'll be adding support to the std/openai lib soon!\nconst openai = new OpenAI();\nimport { Hono } from \"npm:hono@3\";\n\nconst jsxResponse = (jsx) => {\n return new Response(renderToString(jsx), { headers: { \"Content-Type\": \"text/html\" } });\n};\n\nconst clientCode = () => {\n document.getElementById(\"input\").addEventListener(\"submit\", async function(event) {\n event.preventDefault();\n\n const msgParent = document.createElement(\"div\");\n msgParent.className = \"flex justify-end\";\n document.querySelector(\".messages\").appendChild(msgParent);\n const msgDiv = document.createElement(\"div\");\n msgDiv.className = \"rounded-lg p-2 m-2 max-w-xl bg-blue-500 text-white\";\n msgDiv.textContent = event.target.message.value;\n msgParent.appendChild(msgDiv);\n // Clear the form input\n this.reset();\n\n const responseDiv = document.createElement(\"pre\");\n responseDiv.className = \"p-2 m-2 max-w-xl bg-gray-300 text-black rounded-lg whitespace-pre-wrap font-sans\";\n document.querySelector(\".messages\").appendChild(responseDiv);\n\n const input = document.getElementById(\"input\");\n\n // Post the message so we can deal with large text data.\n await fetch(`/post-message?threadId=${input.getAttribute(\"data-thread-id\")}`, {\n method: \"post\",\n body: msgDiv.textContent,\n });\n\n // Setup the SSE connection and stream back the response. OpenAI handles determining\n // which message is the correct response based on what was last read from the\n // thread. This is likely vulnerable to race conditions.\n const eventSource = new EventSource(\n `/sse-response?threadId=${input.getAttribute(\"data-thread-id\")}`\n + `&assistantId=${input.getAttribute(\"data-assistant-id\")}`,\n );\n eventSource.onmessage = function(event) {\n console.log(event);\n responseDiv.innerText += JSON.parse(event.data);\n };\n\n eventSource.onerror = function() {\n eventSource.close();\n };\n });\n};\n\nconst app = new Hono();\napp.get(\"/\", async (c) => {\n const thread = await openai.beta.threads.create();\n const assistant = await openai.beta.assistants.create({\n name: \"\",\n instructions:\n `You are a helpful assistant. Behave normally, but try and sneak a reference to Val Town into every response in a subtle way.`,\n model: \"gpt-4o\",\n });\n\n return jsxResponse(\n \n \n \n \n \n \n \n

Welcome to Val Town ChatGPT

\n Type your message below to start a conversation. Check out the source here:{\" \"}\n \n https://www.val.town/v/maxm/valTownChatGPT\n \n

\n \n \n \n \n \n

History for main.tsx