用户提问: ai小程序源码
Ai回答: A simple AI mini-program source code!
There are many types of AI mini-programs, and the source code can vary greatly depending on the specific application, programming language, and framework used、Here are a few examples:
1、Simple Chatbot (Python)
python
import nltk
from nltk.stem.lancaster import LancasterStemmer
stemmer = LancasterStemmer()
Define a dictionary of intents and responses
intents = {
'greeting': ['hello', 'hi', 'hey'],
'goodbye': ['bye', 'see you later']
}
responses = {
'greeting': 'Hello! How can I assist you?',
'goodbye': 'See you later!'
}
def process_input(input_text):
tokens = nltk.word_tokenize(input_text)
tokens = [stemmer.stem(token.lower()) for token in tokens]
for intent, keywords in intents.items():
for keyword in keywords:
if keyword in tokens:
return responses[intent]
return 'I didn\'t understand that、Can you please rephrase?'
Test the chatbot
input_text = input('User: ')
print(process_input(input_text))
This is a basic chatbot that uses Natural Language Processing (NLP) to match user input to predefined intents and respond accordingly.
2、Image Classification (TensorFlow.js)
javascript
// Import TensorFlow.js
const tf = require('@tensorflow/tfjs');
// Define a simple image classification model
const model = tf.sequential();
model.add(tf.layers.conv2d({
inputShape: [28, 28, 1],
filters: 10,
kernelSize: 5,
activation: 'relu'
}));
model.add(tf.layers.maxPooling2d({ poolSize: 2 }));
model.add(tf.layers.flatten());
model.add(tf.layers.dense({ units: 10, activation: 'softmax' }));
// Compile the model
model.compile({ optimizer: tf.optimizers.adam(), loss: 'meanSquaredError' });
// Load and preprocess an image
const img = tf.browser.fromPixels(document.getElementById('image'));
const imgData = img.resizeNearestNeighbor([28, 28]).toFloat().div(255);
// Make a prediction
const prediction = model.predict(imgData.expandDims(0));
// Print the prediction
console.log(prediction.dataSync());
This code defines a simple image classification model using TensorFlow.js and makes a prediction on an input image.
3、Voice Assistant (Node.js)
javascript
const express = require('express');
const app = express();
const { Speech } = require('googleapis');
// Set up Google Cloud Speech-to-Text
const speech = new Speech({
projectId: 'your-project-id',
keyFile: 'path/to/credentials.json'
});
// Define a function to handle voice input
app.post('/voice', (req, res) => {
const audio = req.body.audio;
const config = {
encoding: 'LINEAR16',
sampleRate: 16000
};
speech.recognize({
config,
interimResults: true
}, [audio]).then((results) => {
const transcript = results[0].results[0].alternatives[0].transcript;
console.log(`Transcript: ${transcript}`);
res.send(`You said: ${transcript}`);
});
});
app.listen(3000, () => {
console.log('Voice assistant listening on port 3000');
});
This code sets up a simple voice assistant using the Google Cloud Speech-to-Text API and Express.js.
These are just a few examples, and there are many more AI mini-programs you can build, such as:
* Sentiment analysis
* Text summarization
* Face detection
* Object detection
* Recommendation systems
If you have a specific AI mini-program in mind, I'd be happy to help you with the source code!
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提问时间: 2025-04-20 20:33:56
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