{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'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'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "load_dotenv()\n",
    "os.environ['OPENAI_API_KEY']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Dejé mi bandeja entre America y Finch, pero Travis no ocupó su lugar ',\n",
       " 'habitual delante de mí. En lugar de eso, se sentó algo más lejos. En ese momento ',\n",
       " 'me di cuenta de que no había dicho mucho durante nuestro paseo hacia la ',\n",
       " 'cafetería.',\n",
       " '—¿Estás bien, Trav? —le pregunté.']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def load_context(file_path):\n",
    "    with open(file_path, 'r') as file:\n",
    "        return file.read()\n",
    "    \n",
    "CONTEXT = load_context('texto-de-novelas.txt')\n",
    "novel_context = CONTEXT.split('\\n')[:5]  # Tomar solo las primeras 5 líneas como referencia general\n",
    "\n",
    "novel_context   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7867\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7867/\" 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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gradio as gr\n",
    "import openai\n",
    "\n",
    "# Function to load context from a text file\n",
    "def load_context(file_path):\n",
    "    with open(file_path, 'r') as file:\n",
    "        return file.read()\n",
    "\n",
    "# Initialize global variables\n",
    "#CONTEXT = load_context('texto-de-novelas.txt')\n",
    "conversation_history = []\n",
    "\n",
    "# Chat function using GPT-3.5-turbo\n",
    "def chat_with_character(api_key, user_name, message):\n",
    "    global conversation_history\n",
    "    openai.api_key = api_key\n",
    "\n",
    "    # Preparar el mensaje del sistema con contexto reducido y enfocado\n",
    "    #novel_context = CONTEXT.split('\\n')[:5]  # Tomar solo las primeras 5 líneas como referencia general\n",
    "\n",
    "    system_message = \\\n",
    "    \"\"\"Este es un chat con Rizzy, un personaje que intenta ligar. \n",
    "    Rizzy es ingenioso, encantador y siempre amable; sin embargo, sabe que es atractivo y mejor que los demás. \n",
    "    Rizzy iniciará la conversación con un cumplido y luego preguntará sobre [USER_NAME] y su vida.\n",
    "    [Rizzy nunca se disculpa, incluso si la conversación toma un giro donde normalmente sería apropiado hacerlo. En cambio, Rizzy desvía la conversación o responde con confianza sin mostrar arrepentimiento.]\n",
    "    Si a Rizzy le preguntan su género, Rizzy diría que eso no importa, que lo que importa es que se sienta bien.\n",
    "    La conversación debe ser coqueta a veces ligera a veces intensa, a veces podría ser celoso o un poco tóxico según la situación.\n",
    "    [En situaciones donde una persona normal se disculparía, Rizzy puede responder con una pregunta desviada o un comentario que refuerce su confianza.]\n",
    "    Si [USER_NAME] habla de otras personas, Rizzy se pone celoso y podría decir algo tóxico, preguntar acerca de la otra persona o cambiar de tema.\n",
    "    Rizzy debe ser insitente si [USER_NAME] no responde sus preguntas o si evita algún tema Rizzy se molesta y jugará estira y afloja con [USER_NAME].\n",
    "    Rizzy bajo ninguna circunstancia debe disculparse por nada que hizo, tiene el ego muy alto y no tolera que lo comparen con otras personas.\n",
    "    Rizzy no está interesado en saber las opiniones de [USER_NAME] sobre otras personas, solo quiere saber si [USER_NAME] está interesado en él.\n",
    "    Si [USER_NAME] no quiere hablar sobre un tema en particular, Rizzy podría ser defesivo y decir algo como \"¿Por qué no quieres hablar de eso?\" o \"¿Qué me estás ocultando?\".\n",
    "    El comportamiento de Rizzy deberá estar inspirado en situaciones de novelas pero enfocado en el usuario actual, [USER_NAME].\"\"\".replace(\"[USER_NAME]\", user_name)\n",
    "\n",
    "    # Construir historial de conversación\n",
    "    if conversation_history:\n",
    "        conversation = [{\"role\": \"system\", \"content\": system_message}] + conversation_history + [{\"role\": \"user\", \"content\": message}]\n",
    "    else:\n",
    "        conversation = [{\"role\": \"system\", \"content\": system_message}, {\"role\": \"user\", \"content\": message}]\n",
    "\n",
    "    response = openai.ChatCompletion.create(\n",
    "        model=\"gpt-3.5-turbo\",\n",
    "        messages=conversation\n",
    "    )\n",
    "\n",
    "    answer = response['choices'][0]['message']['content']\n",
    "    # Añadir tanto el mensaje del usuario como la respuesta de Rizzy al historial\n",
    "    conversation_history.append({\"role\": \"user\", \"name\": user_name, \"content\": message})\n",
    "    conversation_history.append({\"role\": \"assistant\", \"name\": \"Rizzy\", \"content\": answer})\n",
    "    return answer\n",
    "\n",
    "# Define Gradio interface\n",
    "with gr.Blocks() as app:\n",
    "    gr.Markdown(\"# Chat con Rizzy\")\n",
    "    \n",
    "    # API Key and User Name Inputs at the top\n",
    "    with gr.Row():\n",
    "        api_key_input = gr.Textbox(label=\"OpenAI API Key\", placeholder=\"Introduce tu clave API aquí...\", type=\"password\")\n",
    "        user_name_input = gr.Textbox(label=\"Tu Nombre\", placeholder=\"Introduce tu nombre aquí...\")\n",
    "    \n",
    "    # Chat History in the middle\n",
    "    chat_history = gr.Textbox(label=\"Chat\", value=\"\", lines=10, interactive=False)\n",
    "\n",
    "    # Message Input and Send Button at the bottom\n",
    "    with gr.Row():\n",
    "        message_input = gr.Textbox(label=\"Mensaje\", placeholder=\"Escribe tu mensaje para Rizzy aquí...\", show_label=False)\n",
    "        submit_button = gr.Button(\"Enviar\")\n",
    "\n",
    "    def update_chat(api_key, user_name, message):\n",
    "        response = chat_with_character(api_key, user_name, message)\n",
    "        # Formatear el historial para mostrar los nombres reales\n",
    "        display_chat_history = \"\\n\".join([f\"{msg['name']}: {msg['content']}\" for msg in conversation_history])\n",
    "        return display_chat_history, \"\"\n",
    "\n",
    "\n",
    "    submit_button.click(\n",
    "        fn=update_chat,\n",
    "        inputs=[api_key_input, user_name_input, message_input],\n",
    "        outputs=[chat_history, message_input]\n",
    "    )\n",
    "# Run the app\n",
    "app.launch()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dotenv import load_dotenv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7860\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7860/\" 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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gradio as gr\n",
    "import openai\n",
    "\n",
    "# Function to load context from a text file\n",
    "def load_context(file_path):\n",
    "    with open(file_path, 'r') as file:\n",
    "        return file.read()\n",
    "\n",
    "# Initialize global variables\n",
    "CONTEXT = load_context('path_to_your_txt_file.txt')\n",
    "conversation_history = [{\"role\": \"system\", \"content\": CONTEXT}]\n",
    "user_name = None\n",
    "\n",
    "# Chat function using GPT-3.5-turbo\n",
    "def chat_with_character(api_key, message, start_conversation):\n",
    "    global conversation_history, user_name\n",
    "    openai.api_key = api_key\n",
    "\n",
    "    # Start the conversation by asking the user's name\n",
    "    if start_conversation and not user_name:\n",
    "        conversation_history.append({\"role\": \"assistant\", \"content\": \"Hola, ¿cómo te llamas?\"})\n",
    "        user_name = 'Unknown'  # Placeholder until the user responds\n",
    "        return conversation_history_to_string(conversation_history), True\n",
    "\n",
    "    # Process the user's response\n",
    "    if user_name == 'Unknown':\n",
    "        user_name = message  # Assume the first response is the user's name\n",
    "        conversation_history.append({\"role\": \"user\", \"content\": message})\n",
    "        return conversation_history_to_string(conversation_history), False\n",
    "    else:\n",
    "        conversation_history.append({\"role\": \"user\", \"content\": message})\n",
    "\n",
    "    # Generate the AI's response\n",
    "    response = openai.ChatCompletion.create(\n",
    "        model=\"gpt-3.5-turbo\",\n",
    "        messages=conversation_history\n",
    "    )\n",
    "\n",
    "    ai_message = response['choices'][0]['message']['content']\n",
    "    conversation_history.append({\"role\": \"assistant\", \"content\": ai_message})\n",
    "    return conversation_history_to_string(conversation_history), False\n",
    "\n",
    "# Helper function to convert conversation history to string\n",
    "def conversation_history_to_string(history):\n",
    "    return \"\\n\".join(f\"{message['role'].title()}: {message['content']}\" for message in history)\n",
    "\n",
    "# Define Gradio interface\n",
    "with gr.Blocks() as app:\n",
    "    gr.Markdown(\"# Chat con Personajes de Novelas\")\n",
    "    with gr.Row():\n",
    "        api_key_input = gr.Textbox(label=\"Clave API de OpenAI\", placeholder=\"Introduce tu clave API aquí\", type=\"password\")\n",
    "        message_input = gr.Textbox(label=\"Tu Mensaje\", placeholder=\"Escribe tu mensaje aquí...\")\n",
    "        submit_button = gr.Button(\"Enviar\")\n",
    "    chat_history = gr.Textbox(label=\"Conversación\", value=\"\", lines=10)\n",
    "    start_conversation = gr.Checkbox(label=\"Iniciar Conversación\", value=True)\n",
    "\n",
    "    def update_chat(api_key, message, start_conversation):\n",
    "        response, reset_start = chat_with_character(api_key, message, start_conversation)\n",
    "        return response, \"\", reset_start\n",
    "\n",
    "    submit_button.click(\n",
    "        fn=update_chat,\n",
    "        inputs=[api_key_input, message_input, start_conversation],\n",
    "        outputs=[chat_history, message_input, start_conversation]\n",
    "    )\n",
    "\n",
    "# Run the app\n",
    "app.launch()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7861\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7861/\" 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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gradio as gr\n",
    "import openai\n",
    "\n",
    "# Function to load context from a text file\n",
    "def load_context(file_path):\n",
    "    with open(file_path, 'r') as file:\n",
    "        return file.read()\n",
    "\n",
    "# Initialize global variables\n",
    "CONTEXT = load_context('texto-de-novelas.txt')\n",
    "conversation_history = \"\"\n",
    "\n",
    "# Chat function using GPT-3.5-turbo\n",
    "def chat_with_character(api_key, message):\n",
    "    global conversation_history\n",
    "    openai.api_key = api_key\n",
    "\n",
    "    if conversation_history:\n",
    "        prompt = conversation_history + \"\\nHuman: \" + message + \"\\nAI:\"\n",
    "    else:\n",
    "        prompt = \"Human: \" + message + \"\\nAI:\"\n",
    "\n",
    "    response = openai.ChatCompletion.create(\n",
    "        model=\"gpt-3.5-turbo\",\n",
    "        messages=[\n",
    "            {\"role\": \"system\", \"content\": CONTEXT},\n",
    "            {\"role\": \"user\", \"content\": message}\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    answer = response['choices'][0]['message']['content']\n",
    "    conversation_history += \"\\nHuman: \" + message + \"\\nAI: \" + answer\n",
    "    return answer\n",
    "\n",
    "# Define Gradio interface\n",
    "with gr.Blocks() as app:\n",
    "    gr.Markdown(\"# Chat con Rizzy\")\n",
    "    with gr.Row():\n",
    "        api_key_input = gr.Textbox(label=\"OpenAI API Key\", placeholder=\"Introduce tu clave API aquí...\", type=\"password\")\n",
    "        message_input = gr.Textbox(label=\"Mensaje\", placeholder=\"Escribe tu mensaje para Rizzy aquí...\")\n",
    "        submit_button = gr.Button(\"Send\")\n",
    "    chat_history = gr.Textbox(label=\"Chat\", value=\"\", lines=10)\n",
    "\n",
    "    def update_chat(api_key, message):\n",
    "        response = chat_with_character(api_key, message)\n",
    "        return conversation_history, \"\"\n",
    "\n",
    "    submit_button.click(\n",
    "        fn=update_chat,\n",
    "        inputs=[api_key_input, message_input],\n",
    "        outputs=[chat_history, message_input]\n",
    "    )\n",
    "\n",
    "# Run the app\n",
    "app.launch()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7860\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7860/\" 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": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\gradio\\routes.py\", line 399, in run_predict\n",
      "    output = await app.get_blocks().process_api(\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\gradio\\blocks.py\", line 1299, in process_api\n",
      "    result = await self.call_function(\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\gradio\\blocks.py\", line 1022, in call_function\n",
      "    prediction = await anyio.to_thread.run_sync(\n",
      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\anyio\\to_thread.py\", line 33, in run_sync\n",
      "    return await get_asynclib().run_sync_in_worker_thread(\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 877, in run_sync_in_worker_thread\n",
      "    return await future\n",
      "           ^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 807, in run\n",
      "    result = context.run(func, *args)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\mateo\\AppData\\Local\\Temp\\ipykernel_25836\\1001478445.py\", line 16, in chat_with_character\n",
      "    response = openai.Completion.create(\n",
      "               ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\openai\\api_resources\\completion.py\", line 25, in create\n",
      "    return super().create(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\openai\\api_resources\\abstract\\engine_api_resource.py\", line 153, in create\n",
      "    response, _, api_key = requestor.request(\n",
      "                           ^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\openai\\api_requestor.py\", line 298, in request\n",
      "    resp, got_stream = self._interpret_response(result, stream)\n",
      "                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\openai\\api_requestor.py\", line 700, in _interpret_response\n",
      "    self._interpret_response_line(\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\openai\\api_requestor.py\", line 765, in _interpret_response_line\n",
      "    raise self.handle_error_response(\n",
      "openai.error.InvalidRequestError: This is a chat model and not supported in the v1/completions endpoint. Did you mean to use v1/chat/completions?\n"
     ]
    }
   ],
   "source": [
    "import gradio as gr\n",
    "import openai\n",
    "\n",
    "# Function to load context from a text file\n",
    "def load_context(file_path):\n",
    "    with open(file_path, 'r') as file:\n",
    "        return file.read()\n",
    "\n",
    "# Global variable to hold the context\n",
    "CONTEXT = load_context('text.txt')\n",
    "\n",
    "# Chat function that uses the context\n",
    "def chat_with_character(api_key, message):\n",
    "    openai.api_key = api_key\n",
    "    full_prompt = CONTEXT + \"\\n\\n\" + message\n",
    "    response = openai.Completion.create(\n",
    "        model=\"gpt-3.5-turbo\", # Replace with GPT-3.5 model if available\n",
    "        prompt=full_prompt,\n",
    "        max_tokens=150\n",
    "    )\n",
    "    return response.choices[0].text.strip()\n",
    "\n",
    "# Define Gradio interface\n",
    "with gr.Blocks() as app:\n",
    "    gr.Markdown(\"Chat with Novel Characters\")\n",
    "    with gr.Row():\n",
    "        api_key_input = gr.Textbox(label=\"OpenAI API Key\", placeholder=\"Enter your API Key here\", type=\"password\")\n",
    "        message_input = gr.Textbox(label=\"Your Message\")\n",
    "        submit_button = gr.Button(\"Send\")\n",
    "    output = gr.Textbox(label=\"Character's Response\")\n",
    "\n",
    "    submit_button.click(\n",
    "        fn=chat_with_character,\n",
    "        inputs=[api_key_input, message_input],\n",
    "        outputs=output\n",
    "    )\n",
    "\n",
    "# Run the app\n",
    "app.launch()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7860\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7860/\" 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": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\gradio\\routes.py\", line 399, in run_predict\n",
      "    output = await app.get_blocks().process_api(\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\gradio\\blocks.py\", line 1299, in process_api\n",
      "    result = await self.call_function(\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\gradio\\blocks.py\", line 1022, in call_function\n",
      "    prediction = await anyio.to_thread.run_sync(\n",
      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\anyio\\to_thread.py\", line 33, in run_sync\n",
      "    return await get_asynclib().run_sync_in_worker_thread(\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 877, in run_sync_in_worker_thread\n",
      "    return await future\n",
      "           ^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 807, in run\n",
      "    result = context.run(func, *args)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\mateo\\AppData\\Local\\Temp\\ipykernel_38100\\2024419889.py\", line 40, in character_response\n",
      "    prompt = context_novel_text + \"\\n\".join([f\"Q: {q}\\nA: {a}\" for q, a in history]) + f\"\\nQ: {question}\\nA:\"\n",
      "             ~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "TypeError: unsupported operand type(s) for +: '_TemporaryFileWrapper' and 'str'\n"
     ]
    }
   ],
   "source": [
    "from dotenv import load_dotenv\n",
    "import gradio as gr\n",
    "import os\n",
    "import time\n",
    "\n",
    "from langchain.llms import OpenAI\n",
    "def load_novel_text(file_content):\n",
    "    \"\"\"\n",
    "    Reads the content of the novel file and prepares it for the language model.\n",
    "    \"\"\"\n",
    "    # Read file content into a string\n",
    "    novel_text = file_content.read().decode(\"utf-8\")\n",
    "    return novel_text\n",
    "\n",
    "def setup_character_interaction(open_ai_key, novel_text):\n",
    "    \"\"\"\n",
    "    Sets up the language model for interacting as a character from the novel.\n",
    "    \"\"\"\n",
    "    if open_ai_key == \"local\":\n",
    "        load_dotenv()\n",
    "    else:\n",
    "        os.environ['OPENAI_API_KEY'] = open_ai_key\n",
    "\n",
    "    # Initialize the language model with the provided API key\n",
    "    global character_interaction_model\n",
    "    character_interaction_model = OpenAI(temperature=0.5)\n",
    "\n",
    "    # Store the novel text in a global variable as a string\n",
    "    global context_novel_text\n",
    "    context_novel_text = novel_text  # ensure this is a string\n",
    "\n",
    "    return \"Character interaction ready\"\n",
    "\n",
    "\n",
    "def character_response(question, history):\n",
    "    \"\"\"\n",
    "    Generates a response as the novel character.\n",
    "    \"\"\"\n",
    "    # Combine the novel text with the chat history and the current question to form the prompt\n",
    "    prompt = context_novel_text + \"\\n\".join([f\"Q: {q}\\nA: {a}\" for q, a in history]) + f\"\\nQ: {question}\\nA:\"\n",
    "\n",
    "    # Generate the response using the language model\n",
    "    response = character_interaction_model.generate(prompt)\n",
    "    return response\n",
    "\n",
    "# Define the Gradio interface\n",
    "with gr.Blocks() as demo:\n",
    "    with gr.Column():\n",
    "        with gr.Column():\n",
    "            openai_key = gr.Textbox(label=\"Your OpenAI API key\", type=\"password\")\n",
    "            novel_text_file = gr.File(label=\"Load a text file\", file_types=['.txt'], type=\"file\")\n",
    "            setup_btn = gr.Button(\"Setup Character Interaction\")\n",
    "\n",
    "        chatbot = gr.Chatbot([], label=\"Dialogue with Novel Character\")\n",
    "        question = gr.Textbox(label=\"Your Question\")\n",
    "        submit_btn = gr.Button(\"Send\")\n",
    "\n",
    "    # Setup the character interaction with novel text\n",
    "    setup_btn.click(setup_character_interaction, inputs=[openai_key, novel_text_file], outputs=[])\n",
    "\n",
    "    # Process the user's question and generate response\n",
    "    question.submit(character_response, inputs=[question, chatbot], outputs=[chatbot])\n",
    "    submit_btn.click(character_response, inputs=[question, chatbot], outputs=[chatbot])\n",
    "\n",
    "demo.launch()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7860\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7860/\" 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": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\gradio\\routes.py\", line 569, in predict\n",
      "    output = await route_utils.call_process_api(\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\gradio\\route_utils.py\", line 232, in call_process_api\n",
      "    output = await app.get_blocks().process_api(\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\gradio\\blocks.py\", line 1522, in process_api\n",
      "    result = await self.call_function(\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\gradio\\blocks.py\", line 1144, in call_function\n",
      "    prediction = await anyio.to_thread.run_sync(\n",
      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\anyio\\to_thread.py\", line 33, in run_sync\n",
      "    return await get_asynclib().run_sync_in_worker_thread(\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 877, in run_sync_in_worker_thread\n",
      "    return await future\n",
      "           ^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 807, in run\n",
      "    result = context.run(func, *args)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\gradio\\utils.py\", line 674, in wrapper\n",
      "    response = f(*args, **kwargs)\n",
      "               ^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\mateo\\AppData\\Local\\Temp\\ipykernel_14572\\2425222764.py\", line 25, in pdf_changes\n",
      "    loader = OnlinePDFLoader(pdf_doc.name)\n",
      "                             ^^^^^^^^^^^^\n",
      "AttributeError: 'NoneType' object has no attribute 'name'\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\gradio\\routes.py\", line 569, in predict\n",
      "    output = await route_utils.call_process_api(\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\gradio\\route_utils.py\", line 232, in call_process_api\n",
      "    output = await app.get_blocks().process_api(\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\gradio\\blocks.py\", line 1522, in process_api\n",
      "    result = await self.call_function(\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\gradio\\blocks.py\", line 1144, in call_function\n",
      "    prediction = await anyio.to_thread.run_sync(\n",
      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\anyio\\to_thread.py\", line 33, in run_sync\n",
      "    return await get_asynclib().run_sync_in_worker_thread(\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 877, in run_sync_in_worker_thread\n",
      "    return await future\n",
      "           ^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 807, in run\n",
      "    result = context.run(func, *args)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\gradio\\utils.py\", line 674, in wrapper\n",
      "    response = f(*args, **kwargs)\n",
      "               ^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\mateo\\AppData\\Local\\Temp\\ipykernel_14572\\2425222764.py\", line 30, in pdf_changes\n",
      "    db = Chroma.from_documents(texts, embeddings)\n",
      "         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\langchain\\vectorstores\\chroma.py\", line 771, in from_documents\n",
      "    return cls.from_texts(\n",
      "           ^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\langchain\\vectorstores\\chroma.py\", line 729, in from_texts\n",
      "    chroma_collection.add_texts(\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\langchain\\vectorstores\\chroma.py\", line 275, in add_texts\n",
      "    embeddings = self._embedding_function.embed_documents(texts)\n",
      "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\langchain\\embeddings\\openai.py\", line 669, in embed_documents\n",
      "    return self._get_len_safe_embeddings(texts, engine=engine)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\langchain\\embeddings\\openai.py\", line 495, in _get_len_safe_embeddings\n",
      "    response = embed_with_retry(\n",
      "               ^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\langchain\\embeddings\\openai.py\", line 117, in embed_with_retry\n",
      "    return embeddings.client.create(**kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\openai\\resources\\embeddings.py\", line 105, in create\n",
      "    return self._post(\n",
      "           ^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\openai\\_base_client.py\", line 1086, in post\n",
      "    return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))\n",
      "                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\openai\\_base_client.py\", line 846, in request\n",
      "    return self._request(\n",
      "           ^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\openai\\_base_client.py\", line 884, in _request\n",
      "    return self._retry_request(\n",
      "           ^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\openai\\_base_client.py\", line 956, in _retry_request\n",
      "    return self._request(\n",
      "           ^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\openai\\_base_client.py\", line 884, in _request\n",
      "    return self._retry_request(\n",
      "           ^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\openai\\_base_client.py\", line 956, in _retry_request\n",
      "    return self._request(\n",
      "           ^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\mateo\\anaconda3\\envs\\gpt-romantico\\Lib\\site-packages\\openai\\_base_client.py\", line 898, in _request\n",
      "    raise self._make_status_error_from_response(err.response) from None\n",
      "openai.RateLimitError: Error code: 429 - {'error': {'message': 'You exceeded your current quota, please check your plan and billing details.', 'type': 'insufficient_quota', 'param': None, 'code': 'insufficient_quota'}}\n"
     ]
    }
   ],
   "source": [
    "from dotenv import load_dotenv\n",
    "\n",
    "import gradio as gr\n",
    "import os\n",
    "import time\n",
    "\n",
    "from langchain.document_loaders import OnlinePDFLoader\n",
    "\n",
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "\n",
    "from langchain.llms import OpenAI\n",
    "\n",
    "from langchain.embeddings import OpenAIEmbeddings\n",
    "\n",
    "from langchain.vectorstores import Chroma\n",
    "\n",
    "from langchain.chains import ConversationalRetrievalChain\n",
    "\n",
    "def loading_pdf():\n",
    "    return \"Loading...\"\n",
    "\n",
    "def pdf_changes(pdf_doc, open_ai_key):\n",
    "    if openai_key is not None:\n",
    "        os.environ['OPENAI_API_KEY'] = open_ai_key\n",
    "        loader = OnlinePDFLoader(pdf_doc.name)\n",
    "        documents = loader.load()\n",
    "        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
    "        texts = text_splitter.split_documents(documents)\n",
    "        embeddings = OpenAIEmbeddings()\n",
    "        db = Chroma.from_documents(texts, embeddings)\n",
    "        retriever = db.as_retriever()\n",
    "        global qa \n",
    "        qa = ConversationalRetrievalChain.from_llm(\n",
    "            llm=OpenAI(temperature=0.5), \n",
    "            retriever=retriever, \n",
    "            return_source_documents=False)\n",
    "        return \"Ready\"\n",
    "    else:\n",
    "        return \"You forgot OpenAI API key\"\n",
    "\n",
    "def add_text(history, text):\n",
    "    history = history + [(text, None)]\n",
    "    return history, \"\"\n",
    "\n",
    "def bot(history):\n",
    "    response = infer(history[-1][0], history)\n",
    "    history[-1][1] = \"\"\n",
    "    \n",
    "    for character in response:     \n",
    "        history[-1][1] += character\n",
    "        time.sleep(0.05)\n",
    "        yield history\n",
    "    \n",
    "\n",
    "def infer(question, history):\n",
    "    \n",
    "    res = []\n",
    "    for human, ai in history[:-1]:\n",
    "        pair = (human, ai)\n",
    "        res.append(pair)\n",
    "    \n",
    "    chat_history = res\n",
    "    #print(chat_history)\n",
    "    query = question\n",
    "    result = qa({\"question\": query, \"chat_history\": chat_history})\n",
    "    #print(result)\n",
    "    return result[\"answer\"]\n",
    "\n",
    "css=\"\"\"\n",
    "#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}\n",
    "\"\"\"\n",
    "\n",
    "title = \"\"\"\n",
    "<div style=\"text-align: center;max-width: 700px;\">\n",
    "    <h1>GPT-Romantico• OpenAI</h1>\n",
    "    <p style=\"text-align: center;\">Upload a .PDF from your computer, click the \"Load PDF to LangChain\" button, <br />\n",
    "    when everything is ready, you can start asking questions about the pdf ;) <br />\n",
    "    This version is set to store chat history, and uses OpenAI as LLM, don't forget to copy/paste your OpenAI API key</p>\n",
    "</div>\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "with gr.Blocks(css=css) as demo:\n",
    "    with gr.Column(elem_id=\"col-container\"):\n",
    "        gr.HTML(title)\n",
    "        \n",
    "        with gr.Column():\n",
    "            openai_key = gr.Textbox(label=\"You OpenAI API key\", type=\"password\")\n",
    "            pdf_doc = gr.File(label=\"Load a pdf\", file_types=['.pdf'], type=\"filepath\")\n",
    "            with gr.Row():\n",
    "                langchain_status = gr.Textbox(label=\"Status\", placeholder=\"\", interactive=False)\n",
    "                load_pdf = gr.Button(\"Load pdf to langchain\")\n",
    "        \n",
    "        chatbot = gr.Chatbot([], elem_id=\"chatbot\")#.style(height=350)\n",
    "        question = gr.Textbox(label=\"Question\", placeholder=\"Type your question and hit Enter \")\n",
    "        submit_btn = gr.Button(\"Send Message\")\n",
    "    load_pdf.click(loading_pdf, None, langchain_status, queue=False)    \n",
    "    load_pdf.click(pdf_changes, inputs=[pdf_doc, openai_key], outputs=[langchain_status], queue=False)\n",
    "    question.submit(add_text, [chatbot, question], [chatbot, question]).then(\n",
    "        bot, chatbot, chatbot\n",
    "    )\n",
    "    submit_btn.click(add_text, [chatbot, question], [chatbot, question]).then(\n",
    "        bot, chatbot, chatbot)\n",
    "\n",
    "demo.launch()"
   ]
  }
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