Merge pull request #84 from l-jacques/ljacques-w2d2
Exercise week2 day 2: offer multi llms dropdown and reusable AISystem.py with stream option
This commit is contained in:
Ed Donner
GitHub
81
week2/community-contributions/AISystem.py
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81
week2/community-contributions/AISystem.py
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from enum import Enum, auto
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from openai import OpenAI
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import anthropic
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def formatPrompt(role, content):
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return {"role": role, "content": content}
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class AI(Enum):
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OPEN_AI = "OPEN_AI"
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CLAUDE = "CLAUDE"
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GEMINI = "GEMINI"
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OLLAMA = "OLLAMA"
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class AISystem:
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def __init__(self, processor, system_string="", model="", type=AI.OPEN_AI):
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"""
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Initialize the ChatSystem with a system string and empty messages list.
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:param system_string: Optional initial system string description
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"""
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self.processor = processor
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self.system = system_string
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self.model = model
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self.messages = []
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self.type = type
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def call(self, message):
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self.messages.append(message)
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toSend = self.messages
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if self.type == AI.CLAUDE:
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message = self.processor.messages.create(
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model=self.model,
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system=self.system,
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messages=self.messages,
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max_tokens=500
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)
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return message.content[0].text
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else:
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toSend.insert(0,self.system)
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completion = self.processor.chat.completions.create(
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model=self.model,
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messages= toSend
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)
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return completion.choices[0].message.content
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def stream(self, message, usingGradio=False):
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self.messages.append(message)
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if self.type == AI.CLAUDE:
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result = self.processor.messages.stream(
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model=self.model,
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system=self.system,
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messages=self.messages,
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temperature=0.7,
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max_tokens=500
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)
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response_chunks = ""
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with result as stream:
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for text in stream.text_stream:
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if usingGradio:
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response_chunks += text or ""
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yield response_chunks
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else:
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yield text
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else:
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toSend = self.messages
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toSend.insert(0,self.system)
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stream = self.processor.chat.completions.create(
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model=self.model,
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messages= toSend,
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stream=True
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)
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response_chunks = ""
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for chunk in stream:
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if usingGradio:
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response_chunks += chunk.choices[0].delta.content or "" # need to yield the total cumulative results to gradio and not chunk by chunk
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yield response_chunks
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else:
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yield chunk.choices[0].delta.content
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "a473d607-073d-4963-bdc4-aba654523681",
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"metadata": {},
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"source": [
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"## Day 2 Exercise\n",
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"building upon the day1 exercise to offer a multi models via dropdown.\n",
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"externalized the common methods into a AISystem.py file to be reused down the line"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f761729f-3bd5-4dd7-9e63-cbe6b4368a66",
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"metadata": {},
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"source": [
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"## Load env, check for api keys and load up the connections"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "fedb3d94-d096-43fd-8a76-9fdbc2d0d78e",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"OpenAI API Key exists and begins sk-proj-\n",
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"Anthropic API Key exists and begins sk-ant-\n",
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"Google API Key exists and begins AIzaSyC-\n"
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]
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}
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],
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"source": [
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"import os\n",
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"from enum import Enum, auto\n",
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"from dotenv import load_dotenv\n",
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"from openai import OpenAI\n",
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"import anthropic\n",
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"from AISystem import formatPrompt, AI, AISystem\n",
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"import gradio as gr # oh yeah!\n",
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"\n",
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"# Load environment variables in a file called .env\n",
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"# Print the key prefixes to help with any debugging\n",
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"\n",
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"load_dotenv()\n",
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"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
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"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
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"google_api_key = os.getenv('GOOGLE_API_KEY')\n",
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"\n",
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"if openai_api_key:\n",
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" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
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"else:\n",
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" print(\"OpenAI API Key not set\")\n",
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" \n",
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"if anthropic_api_key:\n",
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" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
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"else:\n",
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" print(\"Anthropic API Key not set\")\n",
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"\n",
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"if google_api_key:\n",
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" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
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"else:\n",
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" print(\"Google API Key not set\")\n",
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"\n",
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"openai = OpenAI()\n",
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"\n",
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"claude = anthropic.Anthropic()\n",
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"\n",
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"gemini_via_openai_client = OpenAI(\n",
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" api_key=google_api_key, \n",
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" base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\"\n",
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")\n",
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"ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n",
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"openai_model = \"gpt-4o-mini\"\n",
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"claude_model = \"claude-3-haiku-20240307\"\n",
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"gemini_model = \"gemini-1.5-flash\"\n",
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"ollama_model = \"llama3.2\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "17f7987b-2bdf-434a-8fce-6c367f148dde",
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"metadata": {},
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"source": [
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"## Create the systems for each llms"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "f92eef29-325e-418c-a444-879d83d5fbc9",
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"metadata": {},
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"outputs": [],
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"source": [
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"geminiSys = AISystem(gemini_via_openai_client,\n",
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" formatPrompt(\"system\",\"You are a chatbot. you always try to make conversation and get more in depth\"), \n",
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" gemini_model,\n",
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" AI.GEMINI)\n",
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"\n",
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"openAiSys = AISystem(openai,\n",
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" formatPrompt(\"system\",\"You are a chatbot. you always try to make conversation and get more in depth\"), \n",
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" openai_model,\n",
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" AI.OPEN_AI)\n",
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"\n",
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"claudeSys = AISystem(claude,\n",
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" \"You are a chatbot. you always try to make conversation and get more in depth\", \n",
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" claude_model,\n",
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" AI.CLAUDE)\n",
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"\n",
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"ollamaSys = AISystem(ollama_via_openai,\n",
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" formatPrompt(\"system\",\"You are a chatbot. you always try to make conversation and get more in depth\"), \n",
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" ollama_model,\n",
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" AI.OLLAMA)\n",
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"sys_dict = { AI.GEMINI: geminiSys, AI.OPEN_AI: openAiSys, AI.CLAUDE: claudeSys, AI.OLLAMA: ollamaSys}\n",
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"\n",
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"def stream_model(prompt, model):\n",
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" aiSystem = sys_dict.get(AI[model.upper()])\n",
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" yield from aiSystem.stream(formatPrompt(\"user\",prompt), True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f8ecd283-92b2-454d-b1ae-8016d41e3026",
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"metadata": {},
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"source": [
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"## Create the gradio interface linking with the AI enum for the dropdown"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "9db8ed67-280a-400d-8543-4ab95863ce51",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"* Running on local URL: http://127.0.0.1:7873\n",
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"\n",
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"To create a public link, set `share=True` in `launch()`.\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div><iframe src=\"http://127.0.0.1:7873/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": []
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\n",
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"view = gr.Interface(\n",
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" fn=stream_model,\n",
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" inputs=[gr.Textbox(label=\"Your prompt:\", lines=6) , gr.Dropdown(choices=[ai.value for ai in AI], label=\"Select model\")],\n",
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" outputs=[gr.Markdown(label=\"Response:\")],\n",
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" flagging_mode=\"never\"\n",
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")\n",
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"view.launch()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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