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* Copyright 2025 IBM Corp.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\nimport { AgentError, BaseAgent, BaseAgentRunOptions } from \"@/agents/base.js\";\nimport { AgentMeta } from \"@/agents/types.js\";\nimport { GetRunContext } from \"@/context.js\";\nimport { Callback, Emitter } from \"@/emitter/emitter.js\";\nimport { BaseMemory } from \"@/memory/base.js\";\nimport { ChatLLM, ChatLLMOutput } from \"@/llms/chat.js\";\nimport { isTruthy, last } from \"remeda\";\nimport { BaseMessage, Role } from \"@/llms/primitives/message.js\";\nimport {\n StreamlitAgentSystemPrompt,\n StreamlitAgentTemplates,\n} from \"@/agents/experimental/streamlit/prompts.js\";\nimport { BaseLLMOutput } from \"@/llms/base.js\";\nimport { TokenMemory } from \"@/memory/tokenMemory.js\";\nimport { findFirstPair } from \"@/internals/helpers/string.js\";\n\nexport interface StreamlitAgentInput {\n llm: ChatLLM;\n memory: BaseMemory;\n templates?: Partial;\n}\n\nexport interface StreamlitRunInput {\n prompt: string | null;\n}\n\ninterface Block {\n name: \"text\" | \"app\";\n content: string;\n start: number;\n end: number;\n}\n\ninterface Result {\n raw: string;\n blocks: Block[];\n}\n\nexport interface StreamlitRunOutput {\n result: Result;\n message: BaseMessage;\n memory: BaseMemory;\n}\n\nexport interface StreamlitEvents {\n newToken: Callback<{\n delta: string;\n state: Readonly<{\n content: string;\n }>;\n chunk: BaseLLMOutput;\n }>;\n}\n\nexport class StreamlitAgent extends BaseAgent {\n public emitter = new Emitter({\n namespace: [\"agent\", \"experimental\", \"streamlit\"],\n creator: this,\n });\n\n constructor(protected readonly input: StreamlitAgentInput) {\n super();\n }\n\n public get meta(): AgentMeta {\n return {\n name: `Streamlit`,\n tools: [],\n description: `StreamlitAgent is an experimental meta-app agent that uses \\`Meta LLaMa 3.1 70B\\` to build \\`IBM Granite 3 8B\\`-powered apps using Streamlit -- a simple UI framework for Python.`,\n };\n }\n\n set memory(memory: BaseMemory) {\n this.input.memory = memory;\n }\n\n public get memory() {\n return this.input.memory;\n }\n\n protected async _run(\n input: StreamlitRunInput,\n _options: BaseAgentRunOptions,\n run: GetRunContext,\n ): Promise {\n const { userMessage, runMemory } = await this.prepare(input);\n\n let content = \"\";\n for await (const chunk of this.input.llm.stream(runMemory.messages, {\n signal: run.signal,\n })) {\n const delta = chunk.getTextContent();\n content += delta;\n await run.emitter.emit(\"newToken\", { delta, state: { content }, chunk });\n }\n const result = this.parse(content);\n\n const assistantMessage = BaseMessage.of({\n role: Role.ASSISTANT,\n text: content,\n });\n await this.memory.addMany([userMessage, assistantMessage].filter(isTruthy));\n\n return {\n result,\n message: assistantMessage,\n memory: runMemory,\n };\n }\n\n protected async prepare(input: StreamlitRunInput) {\n const systemMessage = BaseMessage.of({\n role: Role.SYSTEM,\n text: (this.input.templates?.system ?? StreamlitAgentSystemPrompt).render({}),\n });\n\n const userMessage =\n input.prompt !== null || this.memory.isEmpty()\n ? BaseMessage.of({\n role: Role.USER,\n text: input.prompt ?? \"No message.\",\n meta: {\n createdAt: new Date(),\n },\n })\n : null;\n\n const inputMessages = [...this.memory.messages, userMessage].filter(isTruthy);\n\n const runMemory = new TokenMemory({\n llm: this.input.llm,\n capacityThreshold: 0.85,\n syncThreshold: 0.6,\n handlers: {\n removalSelector(msgs) {\n // First we remove messages from the past conversations\n const prevConversationMsg = msgs.find((msg) => inputMessages.includes(msg));\n if (prevConversationMsg && prevConversationMsg !== last(inputMessages)) {\n return prevConversationMsg;\n }\n\n const lastMsg = msgs.length > 3 && msgs.find((m) => m.role === Role.ASSISTANT);\n if (!lastMsg) {\n throw new AgentError(\"Cannot fit the current conversation into the context window!\");\n }\n return lastMsg;\n },\n },\n });\n await runMemory.addMany([systemMessage, ...inputMessages].filter(isTruthy));\n\n return { runMemory, userMessage };\n }\n\n protected parse(raw: string): Result {\n const blocks: Block[] = [];\n\n for (let i = 0; i < raw.length; ) {\n const text = raw.substring(i);\n\n const code = findFirstPair(text, [\"```python-app\\n\", \"\\n```\"], { allowOverlap: true });\n if (!code) {\n blocks.push({ start: i, end: i + text.length, content: text, name: \"text\" });\n break;\n }\n\n if (code.start > 0) {\n blocks.push({\n name: \"text\",\n start: i,\n end: i + code.start,\n content: text.substring(0, code.start),\n });\n }\n blocks.push({\n name: \"app\",\n content: code.inner,\n start: i + code.start,\n end: i + code.end,\n });\n i += code.end;\n }\n\n return {\n raw,\n blocks,\n };\n }\n\n createSnapshot() {\n return {\n ...super.createSnapshot(),\n input: this.input,\n };\n }\n}\n"]}