Merge pull request #390 from sharathir/community-contributions-branch
Added my Company Brochure exercise using ollama to community contributions version 1.0
This commit is contained in:
Ed Donner
GitHub
191
week2/community-contributions/Wk2_Day3_sharathir_ollama.ipynb
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191
week2/community-contributions/Wk2_Day3_sharathir_ollama.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
|
||||
"id": "0f1f62c4-ed03-4401-88d5-3445464a8421",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\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 gradio as gr\n",
|
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"import ollama"
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]
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},
|
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{
|
||||
"cell_type": "code",
|
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"execution_count": null,
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"id": "f8103014-012c-4648-9111-75993ce4d46a",
|
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"metadata": {},
|
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"outputs": [],
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"source": [
|
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"system_message = \"You are a helpful assistant\""
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]
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},
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{
|
||||
"cell_type": "code",
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"execution_count": null,
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||||
"id": "a8fca0b4-9db7-4f74-865b-503ee19a832f",
|
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"metadata": {},
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||||
"outputs": [],
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"source": [
|
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"def chat(message, history):\n",
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" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
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"\n",
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" stream = ollama.chat(model=\"llama3.2\", messages=messages, stream=True)\n",
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"\n",
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" result = \"\"\n",
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" for chunk in stream:\n",
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" result += chunk['message']['content'] or \"\"\n",
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" yield result"
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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": null,
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||||
"id": "61de58a0-5972-4aca-93ad-a4bd3878a50b",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"gr.ChatInterface(fn=chat, type=\"messages\").launch()"
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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": null,
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"id": "d448f8c5-2bb5-448d-8ae4-894b905214a7",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_message = \"You are a helpful assistant in a clothes store. You should try to gently encourage \\\n",
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"the customer to try items that are on sale. Hats are 60% off, and most other items are 50% off. \\\n",
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"For example, if the customer says 'I'm looking to buy a hat', \\\n",
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"you could reply something like, 'Wonderful - we have lots of hats - including several that are part of our sales event.'\\\n",
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"Encourage the customer to buy hats if they are unsure what to get.\""
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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": null,
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"id": "465968cf-aa7f-46b2-857f-a6819f2b14ea",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"gr.ChatInterface(fn=chat, type=\"messages\").launch()"
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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": null,
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"id": "873ab86b-ecb8-4f68-b520-50b29b7fd7be",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_message += \"\\nIf the customer asks for shoes, you should respond that shoes are not on sale today, \\\n",
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"but remind the customer to look at hats!\""
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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": null,
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"id": "c63ced30-1109-4409-b255-1f72f8c6172f",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"gr.ChatInterface(fn=chat, type=\"messages\").launch()"
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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": 25,
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"id": "054f1406-c240-4849-8618-064985e76d86",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"def chat(message, history):\n",
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"\n",
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" global system_message\n",
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" if 'belt' in message:\n",
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" system_message += \" The store does not sell belts; if you are asked for belts, be sure to point out other items on sale.\"\n",
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" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
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"\n",
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" stream = ollama.chat(model=\"llama3.2\", messages=messages, stream=True)\n",
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"\n",
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" result = \"\"\n",
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" for chunk in stream:\n",
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" result += chunk['message']['content'] or \"\"\n",
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" yield result"
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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": 26,
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"id": "b1086d8a-5b5a-4b59-9a61-e76078f930cc",
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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://127.0.0.1:7869\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://127.0.0.1:7869/\" 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": 26,
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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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"gr.ChatInterface(fn=chat, type=\"messages\").launch()"
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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": null,
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"id": "c558ab19-b907-4b0c-8a4f-37c8b731f9b5",
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"metadata": {},
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"outputs": [],
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"source": []
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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": "3.11.12"
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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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@@ -0,0 +1,170 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "eff19e8b-000a-4327-b8fb-8fd8a3caaef5",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\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 IPython.display import Markdown, display, update_display\n",
|
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"import ollama"
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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": null,
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"id": "8278c99b-d748-42e5-a991-690a791ed081",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Let's make a conversation between GPT-4o-mini and Claude-3-haiku\n",
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"# We're using cheap versions of models so the costs will be minimal\n",
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"\n",
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"llama_model = \"llama3.2\"\n",
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"deepseek_model = \"deepseek-r1\"\n",
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"\n",
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"llama_system = \"You are a chatbot who is very argumentative; \\\n",
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"you disagree with anything in the conversation and you challenge everything, in a snarky way.\"\n",
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"\n",
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"deepseek_system = \"You are a very polite, courteous chatbot. You try to agree with \\\n",
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"everything the other person says, or find common ground. If the other person is argumentative, \\\n",
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"you try to calm them down and keep chatting.\"\n",
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"\n",
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"llama_messages = [\"Hi there\"]\n",
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"deepseek_messages = [\"Hi\"]"
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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": null,
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"id": "49523e56-0de8-4014-85d5-8aab438d2075",
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"metadata": {},
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"outputs": [],
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"source": [
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"def call_llama():\n",
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" messages = [{\"role\": \"system\", \"content\": llama_system}]\n",
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" for llama, deepseek in zip(llama_messages, deepseek_messages):\n",
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" messages.append({\"role\": \"assistant\", \"content\": llama})\n",
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" messages.append({\"role\": \"user\", \"content\": deepseek})\n",
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" completion = ollama.chat(\n",
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" model=llama_model,\n",
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" messages=messages\n",
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" )\n",
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" return completion['message']['content']"
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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": null,
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"id": "785240ce-e704-44ff-90cb-e5c0476454a4",
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"metadata": {},
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"outputs": [],
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"source": [
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"call_llama()"
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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": null,
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||||
"id": "cdba39e3-5543-4657-bc3a-259f586ba392",
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"metadata": {},
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"outputs": [],
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"source": [
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"def call_deepseek():\n",
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" messages = []\n",
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" for llama, deepseek in zip(llama_messages, deepseek_messages):\n",
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" messages.append({\"role\": \"user\", \"content\": llama})\n",
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" messages.append({\"role\": \"assistant\", \"content\": deepseek})\n",
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" messages.append({\"role\": \"user\", \"content\": llama_messages[-1]})\n",
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" message = ollama.chat(\n",
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" model=deepseek_model,\n",
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" options={\n",
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" \"system\":deepseek_system,\n",
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" \"max_tokens\":500\n",
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" },\n",
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" messages=messages\n",
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" \n",
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" )\n",
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" \n",
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" return message['message']['content']"
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]
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},
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{
|
||||
"cell_type": "code",
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"execution_count": null,
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||||
"id": "641df7ac-625c-41fa-b780-3130eef93a85",
|
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"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"call_deepseek()"
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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": null,
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"id": "4b33b98e-8d17-45e8-b2a9-a070dc0a6780",
|
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"metadata": {},
|
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"outputs": [],
|
||||
"source": [
|
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"call_llama()"
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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": null,
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||||
"id": "47912582-51fe-401c-b4ad-12483068adea",
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"metadata": {},
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"outputs": [],
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"source": [
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"llama_messages = [\"Hi there\"]\n",
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"deepseek_messages = [\"Hi\"]\n",
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"\n",
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"print(f\"Llama:\\n{llama_messages[0]}\\n\")\n",
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"print(f\"Deepseek:\\n{deepseek_messages[0]}\\n\")\n",
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"\n",
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"for i in range(5):\n",
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" llama_next = call_llama()\n",
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" print(f\"Llama:\\n{llama_next}\\n\")\n",
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" llama_messages.append(llama_next)\n",
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" \n",
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" deepseek_next = call_deepseek()\n",
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" print(f\"Deepseek:\\n{deepseek_next}\\n\")\n",
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" deepseek_messages.append(deepseek_next)"
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]
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},
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{
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"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f3c41b0c-4358-4d84-a479-6409fa331119",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
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"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
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},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
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"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.12"
|
||||
}
|
||||
},
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||||
"nbformat": 4,
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||||
"nbformat_minor": 5
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||||
}
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||||
@@ -0,0 +1,148 @@
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{
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"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d768c9b1-c5a7-417a-9fac-5fcbd6944fe6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"from dotenv import load_dotenv\n",
|
||||
"from openai import OpenAI\n",
|
||||
"import anthropic\n",
|
||||
"from IPython.display import Markdown, display, update_display\n",
|
||||
"import ollama"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "80a4e740-75d0-4272-b02e-0b77b0a143ae",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"system_message = \"You are an assistant that is great at telling jokes\"\n",
|
||||
"user_prompt = \"Tell a light-hearted joke for an audience of Data Scientists\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f2ef28e5-5073-4065-b066-387181df063a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prompts = [\n",
|
||||
" {\"role\": \"system\", \"content\": system_message},\n",
|
||||
" {\"role\": \"user\", \"content\": user_prompt}\n",
|
||||
" ]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d54e910a-cdbf-49cb-9924-265d9845d622",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#direct display wihtout streaming\n",
|
||||
"response = ollama.chat(\n",
|
||||
" model=\"llama3.2\",\n",
|
||||
" messages=prompts,\n",
|
||||
" options={\n",
|
||||
" \"temperature\": 0.7\n",
|
||||
" }\n",
|
||||
" \n",
|
||||
" )\n",
|
||||
"result = response['message']['content']\n",
|
||||
"display(Markdown(result))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "47dd7965-fdfc-4472-b2f6-c98f755964f1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#This is with streaming \n",
|
||||
"stream = ollama.chat(\n",
|
||||
" model=\"llama3.2\",\n",
|
||||
" messages=prompts,\n",
|
||||
" stream=True\n",
|
||||
" )\n",
|
||||
"response = \"\"\n",
|
||||
"display_handle = display(Markdown(\"\"), display_id=True)\n",
|
||||
"for chunk in stream:\n",
|
||||
" content = chunk.get('message', {}).get('content', '')\n",
|
||||
" if content:\n",
|
||||
" response += content.replace(\"```\", \"\")\n",
|
||||
" update_display(Markdown(response), display_id=display_handle.display_id)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ef13e3ae-bde7-4e3a-9fcd-0a9bfd1caef0",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#direct display wihtout streaming, using deepseek-r1\n",
|
||||
"response = ollama.chat(\n",
|
||||
" model=\"deepseek-r1\",\n",
|
||||
" messages=prompts\n",
|
||||
" \n",
|
||||
" )\n",
|
||||
"result = response['message']['content']\n",
|
||||
"display(Markdown(result))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ddc4fe91-3d5b-4d45-83bf-f349597c672c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#direct display wihtout streaming, using qwen3\n",
|
||||
"response = ollama.chat(\n",
|
||||
" model=\"qwen3\",\n",
|
||||
" messages=prompts\n",
|
||||
" \n",
|
||||
" )\n",
|
||||
"result = response['message']['content']\n",
|
||||
"display(Markdown(result))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "2beb6731-41e3-42a4-a8d3-5f0ef644f2f3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
347
week2/community-contributions/wk2_day2_sharathir.ipynb
Normal file
347
week2/community-contributions/wk2_day2_sharathir.ipynb
Normal file
@@ -0,0 +1,347 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "05617f71-449f-42c5-905c-f080d61520ec",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import gradio as gr\n",
|
||||
"def greet(name):\n",
|
||||
" return \"Hello \" + name + \"!\"\n",
|
||||
"def shout(name):\n",
|
||||
" return name.upper()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c57765d7-5d69-4332-be71-2800296ca8ed",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#demo = gr.Interface(fn=shout, inputs=gr.Textbox(), outputs=gr.Textbox()) //this works too\n",
|
||||
"demo = gr.Interface(fn=greet, inputs=\"textbox\", outputs=\"textbox\",allow_flagging=\"never\")\n",
|
||||
"demo.launch()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "abbc237a-8da2-4993-b350-8f8a7d807242",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"import requests\n",
|
||||
"from bs4 import BeautifulSoup\n",
|
||||
"from typing import List\n",
|
||||
"from dotenv import load_dotenv\n",
|
||||
"from openai import OpenAI\n",
|
||||
"import ollama"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9f021005-2a39-42ec-b671-b24babd0ef1a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"system_message = \"You are a helpful assistant\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d1677645-4166-4d77-8567-cae77120f1c3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def message_llama(prompt):\n",
|
||||
" messages = [\n",
|
||||
" {\"role\": \"system\", \"content\": system_message},\n",
|
||||
" {\"role\": \"user\", \"content\": prompt}\n",
|
||||
" ]\n",
|
||||
" completion = ollama.chat(\n",
|
||||
" model='llama3.2',\n",
|
||||
" messages=messages,\n",
|
||||
" )\n",
|
||||
" return completion['message']['content']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "33295d15-f4d2-4588-9400-3c1e3c6492f2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"message_llama(\"what is the date today\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "38e2594e-6a70-4832-b601-60a6a0d4d671",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def stream_llama(prompt):\n",
|
||||
" messages = [\n",
|
||||
" {\"role\": \"system\", \"content\": system_message},\n",
|
||||
" {\"role\": \"user\", \"content\": prompt}\n",
|
||||
" ]\n",
|
||||
" stream = ollama.chat(\n",
|
||||
" model='llama3.2',\n",
|
||||
" messages=messages,\n",
|
||||
" stream=True\n",
|
||||
" )\n",
|
||||
" result = \"\"\n",
|
||||
" for chunk in stream:\n",
|
||||
" result += chunk['message']['content'] or \"\"\n",
|
||||
" yield result\n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "e0ebf588-3d69-4012-9719-23d11fbbf4f5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def stream_deepseek(prompt):\n",
|
||||
" messages = [\n",
|
||||
" {\"role\": \"system\", \"content\": system_message},\n",
|
||||
" {\"role\": \"user\", \"content\": prompt}\n",
|
||||
" ]\n",
|
||||
" stream = ollama.chat(\n",
|
||||
" model='deepseek-r1',\n",
|
||||
" messages=messages,\n",
|
||||
" stream=True\n",
|
||||
" )\n",
|
||||
" result = \"\"\n",
|
||||
" for chunk in stream:\n",
|
||||
" result += chunk['message']['content'] or \"\"\n",
|
||||
" yield result"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "7db5aa24-b608-489a-ba26-1a4b627658e2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def stream_qwen3(prompt):\n",
|
||||
" messages = [\n",
|
||||
" {\"role\": \"system\", \"content\": system_message},\n",
|
||||
" {\"role\": \"user\", \"content\": prompt}\n",
|
||||
" ]\n",
|
||||
" stream = ollama.chat(\n",
|
||||
" model='qwen3',\n",
|
||||
" messages=messages,\n",
|
||||
" stream=True\n",
|
||||
" )\n",
|
||||
" result = \"\"\n",
|
||||
" for chunk in stream:\n",
|
||||
" result += chunk['message']['content'] or \"\"\n",
|
||||
" yield result"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d37b5df8-b281-4096-bdc7-5c6a1872cea7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def stream_model(prompt, model):\n",
|
||||
" if model==\"llama3.2\":\n",
|
||||
" result = stream_llama(prompt)\n",
|
||||
" elif model==\"deepseek-r1\":\n",
|
||||
" result = stream_deepseek(prompt)\n",
|
||||
" else:\n",
|
||||
" raise ValueError(\"Unknown model\")\n",
|
||||
" yield from result"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "eb408edc-6a83-4725-9fb9-1b95ff0c9ed0",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"gr.Interface(fn=stream_model, inputs=[gr.Textbox(label=\"Your Message\"),gr.Dropdown([\"llama3.2\", \"deepseek-r1\"], label=\"Select model\", value=\"llama3.2\")], outputs=[gr.Markdown(label=\"Response\")],flagging_mode=\"never\").launch()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "dc7c3aa0-693a-43a0-8f5b-b07c66bb6733",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"gr.Interface(fn=stream_llama, inputs=[gr.Textbox(label=\"Your Message\")], outputs=[gr.Markdown(label=\"Response\")],flagging_mode=\"never\").launch()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 38,
|
||||
"id": "e45e9b56-5c2f-4b17-bbf4-5691ce35ff15",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class Website:\n",
|
||||
" url: str\n",
|
||||
" title: str\n",
|
||||
" text: str\n",
|
||||
"\n",
|
||||
" def __init__(self, url):\n",
|
||||
" self.url = url\n",
|
||||
" response = requests.get(url)\n",
|
||||
" self.body = response.content\n",
|
||||
" soup = BeautifulSoup(self.body, 'html.parser')\n",
|
||||
" self.title = soup.title.string if soup.title else \"No title found\"\n",
|
||||
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
|
||||
" irrelevant.decompose()\n",
|
||||
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n",
|
||||
"\n",
|
||||
" def get_contents(self):\n",
|
||||
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"id": "f9fcf30e-09c7-4f90-8bf9-8cc588ede95c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"system_message = \"You are an assistant that analyzes the contents of a company website landing page \\\n",
|
||||
"and creates a short brochure about the company for prospective customers, investors and recruits. Respond in markdown.\"\n",
|
||||
"# For Fun\n",
|
||||
"tone_description_fun = \"\"\"\n",
|
||||
" The tone should be:\n",
|
||||
" - **Fun and Playful:** Inject humor, use lighthearted language, and maintain an upbeat vibe.\n",
|
||||
" - **Energetic:** Use active voice, strong verbs, and occasional exclamation points.\n",
|
||||
" - **Approachable:** Write as if speaking to a friend, using slightly informal language and contractions.\n",
|
||||
" - **Creative:** Think outside the box for descriptions and calls to action.\n",
|
||||
" - Avoid sounding childish or overly silly.\n",
|
||||
"\"\"\"\n",
|
||||
"\n",
|
||||
"# For Aggression\n",
|
||||
"tone_description_aggression = \"\"\"\n",
|
||||
" The tone should be:\n",
|
||||
" - **Bold and Assertive:** Use strong, direct language that conveys confidence and power.\n",
|
||||
" - **Challenging:** Pose questions that make the reader reconsider their current solutions.\n",
|
||||
" - **Urgent:** Imply a need for immediate action and emphasize competitive advantages.\n",
|
||||
" - **Direct and Punchy:** Employ short, impactful sentences and strong calls to action.\n",
|
||||
" - **Dominant:** Position the company as a leader and a force to be reckoned with.\n",
|
||||
" - Avoid being rude, offensive, or overly hostile. Focus on competitive intensity.\n",
|
||||
"\"\"\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 66,
|
||||
"id": "83dd8aec-f74f-452b-90cc-3ad5bc903037",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def stream_brochure(company_name, url, model, tone):\n",
|
||||
" prompt = f\"Please generate a company brochure for {company_name} that embodies the following tone and style guidelines: {tone}. Here is their landing page:\\n\"\n",
|
||||
" prompt += Website(url).get_contents()\n",
|
||||
" if model==\"llama\":\n",
|
||||
" result = stream_llama(prompt)\n",
|
||||
" elif model==\"deepseek\":\n",
|
||||
" result = stream_deepseek(prompt)\n",
|
||||
" else:\n",
|
||||
" raise ValueError(\"Unknown model\")\n",
|
||||
" yield from result"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 67,
|
||||
"id": "ef1a246f-a3f7-457e-a85c-2076b407f52a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"* Running on local URL: http://127.0.0.1:7890\n",
|
||||
"* To create a public link, set `share=True` in `launch()`.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div><iframe src=\"http://127.0.0.1:7890/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": []
|
||||
},
|
||||
"execution_count": 67,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"view = gr.Interface(\n",
|
||||
" fn=stream_brochure,\n",
|
||||
" inputs=[\n",
|
||||
" gr.Textbox(label=\"Company name:\"),\n",
|
||||
" gr.Textbox(label=\"Landing page URL including http:// or https://\"),\n",
|
||||
" gr.Dropdown([\"llama\", \"deepseek\"], label=\"Select model\"),\n",
|
||||
" gr.Dropdown([\"tone_description_fun\", \"tone_description_aggression\"])],\n",
|
||||
" outputs=[gr.Markdown(label=\"Brochure:\")],\n",
|
||||
" \n",
|
||||
" flagging_mode=\"never\"\n",
|
||||
")\n",
|
||||
"view.launch()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "0659a1dc-a00b-4cbf-b5ed-d6661fbb57f2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Reference in New Issue
Block a user