{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import pickle\n",
    "from language import Language\n",
    "from utility import Encoder, Decoder, encoderBlock, decoderBlock, MultiHeadAttention, Head, FeedForward\n",
    "import warnings\n",
    "from typing import List\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['लॉक्सलाक्राक्यालालासी']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = 'a' * 1\n",
    "generate([s])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'^थ्रालाष्राप्टोार्फ्रास्रफ्फ्फ्'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_lang.decode(o.tolist()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([20,  4,  5, 12,  4,  3])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = \"pankaj\"\n",
    "torch.tensor(input_lang.encode(s), device=device, dtype=torch.long)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7864\n",
      "\n",
      "Could not create share link. Please check your internet connection or our status page: https://status.gradio.app.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7864/\" 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"
    }
   ],
   "source": [
    "import requests\n",
    "import gradio as gr\n",
    "\n",
    "# Define the API endpoint\n",
    "API_URL = \"http://127.0.0.1:8000/trans\"\n",
    "\n",
    "# Function to call the FastAPI backend\n",
    "def predict(user_input):\n",
    "    # Prepare the data to send to the FastAPI API\n",
    "    payload = {\"query\": user_input}\n",
    "    \n",
    "    # Make a request to the FastAPI backend\n",
    "    response = requests.post(API_URL, json=payload)\n",
    "    \n",
    "    # Get the response JSON\n",
    "    result = response.json()\n",
    "    \n",
    "    # Extract the answer \n",
    "    return \" \".join(result[\"response\"])\n",
    "    \n",
    "\n",
    "# Launch the Gradio interface\n",
    "if __name__ == \"__main__\":\n",
    "    gr.Interface(predict,\n",
    "                 inputs=['textbox'],\n",
    "                 outputs=['text']).launch(share=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gradio as gr\n",
    "\n",
    "def greet(name, intensity):\n",
    "    return \"Hello, \" + name + \"!\" * int(intensity)\n",
    "\n",
    "demo = gr.Interface(\n",
    "    fn=greet,\n",
    "    inputs=[\"text\", \"slider\"],\n",
    "    outputs=[\"text\"],\n",
    ")\n",
    "\n",
    "demo.launch()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "transliteration",
   "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": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}