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modified: README.md
 new file: example.js new file: hug-1.js new file: hug.js
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‎README.md‎

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# Huggingface-API
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An unofficial reverse engineered NodeJS package of Huggingface AI.
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Extremely lightweight and flexible ;)
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# 😊 How To Use?
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```javascript
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const hug = require("./hug")
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const { env } = require("process")
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env.TOKEN = "MY_TOKEN_HERE" // From cookies
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env.HFCHAT = "MY_HFCHAT_HERE" // From cookies
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hug("Hello there")
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.then(data => console.log(data))
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```
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```md
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> Hello! How can I assist you today?
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```
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Get your `hf-chat` and `token` value from cookies and your'e good to go.
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Note: Unofficial and reverse engineered Huggingface API. This software cannot be used for any unlawful activities and commercial purpose.

‎example.js‎

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const hug = require("./hug")
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const { env } = require("process")
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env.TOKEN = "MY_TOKEN_HERE" // From cookies
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env.HFCHAT = "MY_HFCHAT_HERE" // From cookies
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hug("Hello there")
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.then(data => console.log(data))

‎hug-1.js‎

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const { randomUUID } = require("crypto")
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const process = require("process")
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const readLine = require("readline")
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const rl = readLine.createInterface({
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input: process.stdin,
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output: process.stdout
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})
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const token = process.env.TOKEN
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const hfchat = process.env.HFCHAT
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const cookie = `token=${token}; hf-chat=${hfchat}`
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function hug(msg) {
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return new Promise((resolve) => {
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if (!token) return "Huggingface token not provided"
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if (!hfchat) return "hfchat not provided"
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fetch("https://huggingface.co/chat/conversation", {
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"headers": {
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"content-type": "application/json",
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"cookie": cookie
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},
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"body": "{\"model\":\"OpenAssistant/oasst-sft-6-llama-30b-xor\"}",
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"method": "POST"
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})
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.then(data => data.json())
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.then(data => {
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fetch(`https://huggingface.co/chat/conversation/${data.conversationId}/__data.json?x-sveltekit-invalidated=1_1`, {
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"headers": {
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"cookie": cookie
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},
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"body": null,
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"method": "GET"
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})
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.then(() => {
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fetch(`https://huggingface.co/chat/conversation/${data.conversationId}`, {
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"headers": {
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"content-type": "application/json",
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"cookie": cookie
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},
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"body": JSON.stringify({
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"inputs": msg,
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"parameters": {
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"temperature": 0.2,
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"truncate": 1000,
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"max_new_tokens": 1024,
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"stop": [
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"</s>"
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],
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"top_p": 0.95,
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"repetition_penalty": 1.2,
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"top_k": 50,
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"return_full_text": false
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},
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"stream": true,
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"options": {
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"id": randomUUID(),
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"response_id": randomUUID(),
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"is_retry": false,
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"use_cache": false,
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"web_search_id": ""
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}
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}),
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"method": "POST"
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}).then(dataa => dataa.text())
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.then(dataa => {
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dataa = JSON.parse(dataa.slice(dataa.lastIndexOf('data:{"token":{') + 'data:'.length)).generated_text
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resolve(dataa)
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})
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})
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})
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})
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}
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function ask() {
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rl.question("\033[35mYou: \033[34m> \033[36m", (msg) => {
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hug(msg).then(data => {
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console.log("😊 : \033[34m" + data)
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ask()
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})
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})
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}
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ask()

‎hug.js‎

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const { randomUUID } = require("crypto")
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const { env } = require("process")
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const token = env.TOKEN
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const hfchat = env.HFCHAT
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const cookie = `token=${token}; hf-chat=${hfchat}`
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function hug(msg) {
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return new Promise((resolve) => {
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if (!token) return "Huggingface token not provided"
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if (!hfchat) return "hfchat not provided"
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fetch("https://huggingface.co/chat/conversation", {
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"headers": {
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"content-type": "application/json",
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"cookie": cookie
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},
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"body": "{\"model\":\"OpenAssistant/oasst-sft-6-llama-30b-xor\"}",
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"method": "POST"
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})
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.then(data => data.json())
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.then(data => {
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fetch(`https://huggingface.co/chat/conversation/${data.conversationId}/__data.json?x-sveltekit-invalidated=1_1`, {
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"headers": {
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"cookie": cookie
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},
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"body": null,
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"method": "GET"
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})
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.then(() => {
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fetch(`https://huggingface.co/chat/conversation/${data.conversationId}`, {
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"headers": {
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"content-type": "application/json",
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"cookie": cookie
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},
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"body": JSON.stringify({
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"inputs": msg,
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"parameters": {
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"temperature": 0.2,
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"truncate": 1000,
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"max_new_tokens": 1024,
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"stop": [
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"</s>"
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],
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"top_p": 0.95,
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"repetition_penalty": 1.2,
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"top_k": 50,
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"return_full_text": false
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},
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"stream": true,
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"options": {
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"id": randomUUID(),
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"response_id": randomUUID(),
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"is_retry": false,
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"use_cache": false,
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"web_search_id": ""
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}
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}),
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"method": "POST"
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}).then(dataa => dataa.text())
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.then(dataa => {
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dataa = JSON.parse(dataa.slice(dataa.lastIndexOf('data:{"token":{') + 'data:'.length)).generated_text
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resolve(dataa)
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})
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})
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})
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})
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}
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module.exports = hug

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