bot.py

from pyrogram import Client, filters
import torch
import nltk
import pickle
from nltk.stem.lancaster import LancasterStemmer
from pathlib import Path
import numpy as np
import random
import json
from model import ChatBot


stemmer = LancasterStemmer()

with open("data\intents.json") as file:
    data = json.load(file)

RES_SAVE_PATH = Path("resources\data.pickle")
MODEL_PATH = Path("models\model.pth")

try:
    with open(RES_SAVE_PATH, "rb") as f:
        words, labels, training, output = pickle.load(f)
        print(f"Loaded data from pickle file. Vocabulary size: {len(words)}")
except Exception as e:
    print(f"This the error geting when loading the data: -->{e}")

def bag_of_words(s, words):
    bag = torch.zeros(len(words))

    s_words = nltk.word_tokenize(s)
    s_words = [stemmer.stem(word.lower()) for word in s_words]

    indices = [i for i, w in enumerate(words) if w in s_words]
    bag[indices] = 1

    out = torch.Tensor(bag)

    return torch.unsqueeze(out, 0)
out =bag_of_words(s="hai", words=words)
print(out.shape)

model = ChatBot(input_size=len(words), hidden_size=8, output_size=38)
model.load_state_dict(torch.load(f=MODEL_PATH))

app = Client("trytopro_bot", api_id=api_id, api_hash=api_hash, bot_token=bot_token)
@app.on_message(filters.text & filters.private)
async def echo(client, message):
    print(message.text)
    user_input=bag_of_words(message.text, words)
    model.eval()
    with torch.inference_mode():
        result = model(user_input)
    result_index = torch.argmax(result)
    test=result[0,result_index.item()]
    print(test)
    tag = labels[result_index]

    for intent in data["intents"]:
        if intent['tag'] == tag:
            responses = intent['responses']

    if test.item() > 8.0:
        await message.reply(random.choice(responses))
    else:
        await message.reply("SORRY I DID NOT UNDERSTAND")

app.run()