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{
"cells": [
{
"cell_type": "code",
"execution_count": 8,
"id": "01032c82-239c-48bf-82dc-cf91a2dd4698",
"metadata": {},
"outputs": [],
"source": [
"import ollama\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "d17e8da0-72e6-4d11-9f99-64d83d6bb77b",
"metadata": {},
"outputs": [],
"source": [
"system_prompt_llama = \"You are an assistant that solves a quadratic equation provided. Just write the steps and the final answer and nothing else\"\n",
"user_prompt_llama = \"This is the quadratic equation you have to solve: \"\n",
"user_prompt_deepseek = \"This is the quadratic equation you have to solve: \"\n",
"system_prompt_deepseek = \"You are an assistant that solves a quadratic equation provided. Just write the steps and the final answer and nothing else\""
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "e5ed88b2-090f-4364-b334-c6ff66676d82",
"metadata": {},
"outputs": [],
"source": [
"default_equation = \"x² - 5x + 6\""
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "3aa1ed93-15ea-4d97-a00d-dbda21450ad7",
"metadata": {},
"outputs": [],
"source": [
"people_dict = {}"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "9361e9e6-99eb-45b0-ba31-3cf1b0865936",
"metadata": {},
"outputs": [],
"source": [
"def llama(equation):\n",
" message_ = [{'role':'system', 'content':system_prompt_llama}, {'role':'user', 'content':user_prompt_llama+equation}]\n",
" res = ollama.chat(model=\"llama3.2\", messages=message_)\n",
" return res['message']['content']\n",
" \n",
"def deepseek(equation):\n",
" message_ = [{'role':'system', 'content':system_prompt_deepseek}, {'role':'user', 'content':user_prompt_deepseek+equation}]\n",
" res = ollama.chat(model=\"deepseek-r1:1.5b\", messages=message_)\n",
" return res['message']['content']\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "e6ddaf09-996f-4cfa-af98-49bf1d7d5ab3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7872\n",
"* Running on public URL: https://7a723fc085cc9ad458.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"https://7a723fc085cc9ad458.gradio.live\" 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": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with gr.Blocks() as ui:\n",
" gr.Markdown(\"## This is a quadratic equation solver\")\n",
" with gr.Row():\n",
" llama_question = gr.Textbox(label=\"Llama's Question\",value=default_equation,lines=2)\n",
" deepseek_question = gr.Textbox(label=\"Deepseek's Question\",value=default_equation,lines=2)\n",
" with gr.Row():\n",
" llama_answer = gr.TextArea(label=\"Llama's Answer\",lines=10)\n",
" deepseek_answer = gr.TextArea(label=\"Deepseek's Answer\",lines=10)\n",
" with gr.Row():\n",
" llama_solver = gr.Button(\"Solve Llama\")\n",
" deepseek_solver = gr.Button(\"Solve Deepseek\")\n",
" llama_solver.click(llama, inputs=[llama_question], outputs=[llama_answer]) \n",
" deepseek_solver.click(deepseek, inputs=[deepseek_question], outputs=[deepseek_answer]) \n",
"\n",
"ui.launch(share=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "0b9ea66f-a97f-4de2-bc60-fd7abbb4cc25",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7862\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7862/\" 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": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import gradio as gr\n",
"\n",
"# A simple function that reacts to a selected mood\n",
"def respond_to_mood(mood):\n",
" return f\"You selected '{mood}'. I hope your day goes well!\"\n",
"\n",
"\n",
"# Gradio Interface\n",
"with gr.Blocks() as ui:\n",
" with gr.Row():\n",
" area = gr.Textbox(label=\"Answer here\")\n",
" inputs=gr.Radio([\"Happy\", \"Sad\", \"Excited\", \"Angry\"], label=\"How are you feeling today?\")\n",
" inputs.change(respond_to_mood, inputs=[inputs], outputs=[area])\n",
"ui.launch()\n",
" \n",
" \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d5b05061-61da-4d2a-8d30-8cff97b7db35",
"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": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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