GPT_Power_ChatBot_With_Avatar.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shaunck96/AI-Generated-News/blob/main/GPT_Power_ChatBot_With_Avatar.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SQtpGA51GFGc",
        "outputId": "767575e5-f815-4d1a-a7a2-f4b5256fa53a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)\n",
            "Installing collected packages: orjson, mypy-extensions, marshmallow, jsonpointer, typing-inspect, jsonpatch, langsmith, dataclasses-json, langchain-core, langchain-community, langchain\n",
            "Successfully installed dataclasses-json-0.6.4 jsonpatch-1.33 jsonpointer-2.4 langchain-0.1.9 langchain-community-0.0.24 langchain-core-0.1.27 langsmith-0.1.9 marshmallow-3.21.0 mypy-extensions-1.0.0 orjson-3.9.15 typing-inspect-0.9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain_core"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lu_LNbIdGI9Q",
        "outputId": "1c291518-f088-4a9c-cc38-38c55a553a3c"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain_openai"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L9QCyf1-GKtd",
        "outputId": "ea374904-2ec5-48cc-aefb-102e644ee0bc"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: h11, tiktoken, httpcore, httpx, openai, langchain_openai\n",
            "Successfully installed h11-0.14.0 httpcore-1.0.4 httpx-0.27.0 langchain_openai-0.0.8 openai-1.12.0 tiktoken-0.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install  datasets[audio]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1eBZjbmIvt4",
        "outputId": "00e82ced-8b70-4d69-c85d-cc088782fb4a"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets[audio]\n",
            "  Downloading datasets-2.17.1-py3-none-any.whl (536 kB)\n",
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            "  Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
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            "Collecting multiprocess (from datasets[audio])\n",
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            "Requirement already satisfied: pooch>=1.0 in /usr/local/lib/python3.10/dist-packages (from librosa->datasets[audio]) (1.8.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets[audio]) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets[audio]) (2023.4)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0->soundfile>=0.12.1->datasets[audio]) (2.21)\n",
            "Requirement already satisfied: llvmlite<0.42,>=0.41.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba>=0.45.1->librosa->datasets[audio]) (0.41.1)\n",
            "Requirement already satisfied: platformdirs>=2.5.0 in /usr/local/lib/python3.10/dist-packages (from pooch>=1.0->librosa->datasets[audio]) (4.2.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets[audio]) (1.16.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.19.1->librosa->datasets[audio]) (3.3.0)\n",
            "Installing collected packages: dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.17.1 dill-0.3.8 multiprocess-0.70.16\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -rf /content/sample_data\n",
        "!mkdir /content/sample_data\n",
        "\n",
        "!git clone https://github.com/zabique/Wav2Lip\n",
        "\n",
        "!wget 'https://iiitaphyd-my.sharepoint.com/personal/radrabha_m_research_iiit_ac_in/_layouts/15/download.aspx?share=EdjI7bZlgApMqsVoEUUXpLsBxqXbn5z8VTmoxp55YNDcIA' -O '/content/Wav2Lip/checkpoints/wav2lip_gan.pth'\n",
        "a = !pip install https://raw.githubusercontent.com/AwaleSajil/ghc/master/ghc-1.0-py3-none-any.whl\n",
        "\n",
        "!cd Wav2Lip && pip install -r requirements.txt\n",
        "\n",
        "!wget \"https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth\" -O \"/content/Wav2Lip/face_detection/detection/sfd/s3fd.pth\"\n",
        "\n",
        "!pip install -q youtube-dl\n",
        "!pip install ffmpeg-python\n",
        "!pip install librosa==0.9.1\n",
        "\n",
        "\n",
        "from IPython.display import HTML, Audio\n",
        "from google.colab.output import eval_js\n",
        "from base64 import b64decode\n",
        "import numpy as np\n",
        "from scipy.io.wavfile import read as wav_read\n",
        "import io\n",
        "import ffmpeg\n",
        "\n",
        "from IPython.display import clear_output\n",
        "from langchain.prompts.chat import (\n",
        "    ChatPromptTemplate,\n",
        "    HumanMessagePromptTemplate,\n",
        "    SystemMessagePromptTemplate,\n",
        ")\n",
        "from langchain_core.messages import HumanMessage, SystemMessage\n",
        "from langchain_openai import ChatOpenAI\n",
        "from transformers import pipeline\n",
        "import torch\n",
        "from datasets import load_dataset\n",
        "import soundfile as sf\n",
        "import numpy as np\n",
        "import os\n",
        "clear_output()\n",
        "print(\"\\nDone\")"
      ],
      "metadata": {
        "id": "E-T50uBCKq64"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "R15l03RJFPX4"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_and_save_audio(text, audio_embedding):\n",
        "    speaker_embedding = audio_embedding\n",
        "    result = synthesizer(text, forward_params={\"speaker_embeddings\": speaker_embedding})\n",
        "    merged_audio = result[\"audio\"]\n",
        "    file_path = os.path.join(\"./audio/generated_conversations.wav\")\n",
        "    sf.write(file_path, merged_audio, samplerate=22050)\n",
        "\n",
        "def get_speaker_embedding(index):\n",
        "    return torch.tensor(embeddings_dataset[index][\"xvector\"]).unsqueeze(0)\n",
        "\n",
        "synthesizer = pipeline(\"text-to-speech\", model=\"microsoft/speecht5_tts\")\n",
        "embeddings_dataset = load_dataset(\"Matthijs/cmu-arctic-xvectors\", split=\"validation\")\n",
        "audio_embedding = get_speaker_embedding(7306)\n"
      ],
      "metadata": {
        "id": "mSsDnWLBIkRj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Chatbot:\n",
        "    def __init__(self):\n",
        "        self.memory = []  # Stores conversation history\n",
        "        self.chat_model =  chat = ChatOpenAI(temperature=0,\n",
        "                                             openai_api_key=\"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\")\n",
        "        self.system_prompt = \"\"\n",
        "        self.instruction = \"\"\n",
        "        self.query = \"\"\n",
        "        self.prompt_template = messages = [\n",
        "    SystemMessage(\n",
        "        content=self.system_prompt\n",
        "    ),\n",
        "    HumanMessage(\n",
        "        content=self.instruction+self.query\n",
        "    ),\n",
        "]\n",
        "        self.synthesizer = pipeline(\"text-to-speech\", model=\"microsoft/speecht5_tts\")\n",
        "        self.embeddings_dataset = load_dataset(\"Matthijs/cmu-arctic-xvectors\", split=\"validation\")\n",
        "        self.audio_embedding = self.get_speaker_embedding(7306)\n",
        "        self.file_path = os.path.join(\"/content/personna/audio/response.wav\")\n",
        "\n",
        "    def generate_and_save_audio(self, text):\n",
        "        result = self.synthesizer(text.content, forward_params={\"speaker_embeddings\": self.audio_embedding})\n",
        "        sf.write(self.file_path, result[\"audio\"], samplerate=22050)\n",
        "\n",
        "    def speech_to_animation(self):\n",
        "      output_path = \"/content/Wav2Lip/results/result_voice.mp4\"\n",
        "      !cd /content/Wav2Lip && python inference.py --checkpoint_path checkpoints/wav2lip_gan.pth --face \"/content/personna/french_translator_animation.mp4\" --audio \"/content/personna/audio/response.wav\"\n",
        "      from IPython.display import HTML\n",
        "      from base64 import b64encode\n",
        "      mp4 = open(output_path,'rb').read()\n",
        "      data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
        "      HTML(f\"\"\"\n",
        "      <video width=\"50%\" height=\"50%\" controls>\n",
        "            <source src=\"{data_url}\" type=\"video/mp4\">\n",
        "      </video>\"\"\")\n",
        "\n",
        "    def get_speaker_embedding(self, index):\n",
        "        return torch.tensor(self.embeddings_dataset[index][\"xvector\"]).unsqueeze(0)\n",
        "\n",
        "    #def receive_input(self, user_input):\n",
        "    #    \"\"\"Receive input from the user and store it in memory.\"\"\"\n",
        "     #   self.memory.append({\"user\": user_input})\n",
        "    #    response = self.generate_response(user_input)\n",
        "    #    self.memory.append({\"bot\": response})\n",
        "    #    return response\n",
        "\n",
        "    def generate_response(self, user_input):\n",
        "        \"\"\"Generate a response based on the user input. Placeholder for actual logic.\"\"\"\n",
        "        self.system_prompt = \"You are a helpful assistant that translates English to French.\"\n",
        "        self.instruction = \"Please translate the following sentence into French: \"\n",
        "        self.query = \"Context from prior conversation: \"+str(self.memory)\n",
        "        self.query=self.query+\"User Query: \"+user_input+\"Response: \"\n",
        "        response = self.chat_model(self.prompt_template)\n",
        "        self.generate_and_save_audio(response)\n",
        "        self.speech_to_animation()\n",
        "        return response\n",
        "\n",
        "    def get_context(self, n=1):\n",
        "        \"\"\"Retrieve the last 'n' interactions from memory.\"\"\"\n",
        "        return self.memory[-2*n:]\n",
        "\n",
        "    def receive_input(self, user_input):\n",
        "        \"\"\"Receive input from the user, generate response, and display live streaming.\"\"\"\n",
        "        # Append user input to memory\n",
        "        self.memory.append({\"user\": user_input})\n",
        "\n",
        "        # Generate response\n",
        "        response = self.generate_response(user_input)\n",
        "\n",
        "        # Append bot response to memory\n",
        "        self.memory.append({\"bot\": response})\n",
        "\n",
        "        # Display live streaming of avatar responses\n",
        "        self.display_live_streaming()\n",
        "\n",
        "    def display_live_streaming(self):\n",
        "        \"\"\"Display live streaming of avatar responses.\"\"\"\n",
        "        # Clear previous output\n",
        "        display(HTML(\"<div id='live_stream'></div>\"))\n",
        "\n",
        "        def update_stream():\n",
        "            while True:\n",
        "                # Generate response\n",
        "                response = self.generate_response(user_input)\n",
        "\n",
        "                # Display avatar animation\n",
        "                self.speech_to_animation()\n",
        "\n",
        "                # Display the response\n",
        "                display(HTML(f\"<p>{response}</p>\"))\n",
        "\n",
        "                # Wait for some time before the next update\n",
        "                time.sleep(2)  # Adjust as needed\n",
        "\n",
        "        # Start the thread for live streaming update\n",
        "        streaming_thread = threading.Thread(target=update_stream)\n",
        "        streaming_thread.start()\n",
        "\n",
        "persona_dict = {}\n",
        "\n",
        "bot = Chatbot()\n",
        "while True:\n",
        "    query = input(\"You: \")  # Take user input\n",
        "    bot.receive_input(query)  # Pass user input to the bot for processing\n",
        "persona_dict = {}\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IxUCMQs0FPRz",
        "outputId": "987928f9-d819-43e9-e707-fe7c17ff2a47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You: I want to eat some food\n",
            "Using cuda for inference.\n",
            "Reading video frames...\n",
            "Number of frames available for inference: 360\n",
            "/content/Wav2Lip/audio.py:100: FutureWarning: Pass sr=16000, n_fft=800 as keyword args. From version 0.10 passing these as positional arguments will result in an error\n",
            "  return librosa.filters.mel(hp.sample_rate, hp.n_fft, n_mels=hp.num_mels,\n",
            "(80, 132)\n",
            "Length of mel chunks: 45\n",
            "  0% 0/1 [00:00<?, ?it/s]\n",
            "  0% 0/3 [00:00<?, ?it/s]\u001b[A\n",
            " 33% 1/3 [00:24<00:49, 24.78s/it]\u001b[A\n",
            " 67% 2/3 [00:26<00:11, 11.47s/it]\u001b[A"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nMarLBTPQZl8"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}