{
"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": [
""
],
"text/plain": [
""
]
},
"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
}