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Keras va Flask yordamida matnli tarkibni bashorat qilish
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| bet | 180/182 | | Sana | 19.05.2024 | | Hajmi | 5,69 Mb. | | #244351 |
Bog'liq Python sun\'iy intellekt texnologiyasi Dasrlik 20242. Keras va Flask yordamida matnli tarkibni bashorat qilish
Kutubxonalarni o‘rnatish:
pip install Flask tensorflow
Matnli tarkibni bashorat qilish uchun Flask dasturi:
from flask import Flask, render_template, request
from tensorflow import keras
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
import numpy as np
app = Flask(__name__)
model = keras.models.load_model('your_text_model.h5')
tokenizer = Tokenizer()# Lug’atni yuklash
tokenizer.word_index = load_dictionary()
def process_text(text):
sequences = tokenizer.texts_to_sequences([text])
padded_sequences = pad_sequences(sequences, maxlen=100) # Misol: maxlen-maksimal ketma-ketlik uzunligi
return padded_sequences
def predict_text(text):
processed_text = process_text(text)
prediction = model.predict(processed_text)
return prediction[0][0]
@app.route('/', methods=['GET’, 'POST’])
def index():
if request.method == 'POST’:
input_text = request.form['input_text’]
prediction = predict_text(input_text)
return render_template('index.html', prediction=prediction, input_text=input_text)
return render_template('index.html')
if __name__ == '__main__':
app.run(debug=True)
Ushbu “your_text_model” ni almashtirishingiz kerak.h5 ' matnni bashorat qilish modeliga haqiqiy yo‘lda va lug’atni yuklash uchun load_dictionary() amalga oshirilishi kerak.
Ushbu misollar veb-loyihalarda neyron tarmoqlardan foydalanish uchun asosiy ramkani taqdim etadi. Haqiqiy rivojlanishda xavfsizlik, ishlashni optimallashtirish va qaramlikni boshqarish masalalarini ham ko‘rib chiqish.
Python veb-loyihalarida neyron tarmoqlardan foydalanishning ikkita misolini ko‘rib chiqaylik: tasvirni aniqlash va ma’lumotlarga asoslangan bashorat.
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