{
 "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": []
  }
 ],
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   "file_extension": ".py",
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