Teach you to use Python to connect with Baidu AI interface to create intelligent applications

Teach you how to use Python to connect with Baidu AI interface to create intelligent applications

With the rapid development of artificial intelligence, more and more applications have begun Introduce intelligent functions to improve user experience and work efficiency. Baidu AI is the leading artificial intelligence service provider in China, providing a wealth of interfaces and development tools, allowing developers to easily use artificial intelligence technology to build intelligent applications.

This article will teach you how to use the Python programming language to interface with Baidu AI interface to achieve some interesting intelligent functions. Next, we take image recognition, speech recognition and natural language processing as examples to show step by step how to connect with Baidu AI interface and give corresponding code examples.

1. Image recognition

Baidu AI’s image recognition interface can identify objects, scenes, text, etc. in pictures. First, you need to create an account on the Baidu AI official website and apply for an API Key and Secret Key for image recognition. Then, use Python's requests library to send an HTTP request, send the image to be recognized to the Baidu AI interface, and parse the returned JSON result.

The following is a simple sample code for identifying objects in pictures:

import requests

def image_recognition(image_path):
    url = "https://aip.baidubce.com/rest/2.0/image-classify/v2/advanced_general"
    access_token = "your_access_token"  # 替换成你的API Key和Secret Key
    headers = {'Content-Type': 'application/x-www-form-urlencoded'}
    params = {'access_token': access_token}
    data = {'image': open(image_path, 'rb')}
    
    response = requests.post(url, headers=headers, params=params, files=data)
    result = response.json()
    
    if 'error_code' in result:
        print("图像识别失败：" + result['error_msg'])
    else:
        for item in result['result']:
            print(item['keyword'] + " (置信度：" + str(item['score']) + ")")

You only need to replace "your_access_token" in the above code with the API Key and Secret you applied for Key, then call the image_recognition function and pass in the path of the image to be recognized to get the recognition result.

2. Speech recognition

Baidu AI’s speech recognition interface can convert speech into text. In order to use this interface, you need to apply for a speech recognition API Key and Secret Key first. Then, use Python's PyAudio library to record audio, send the recorded audio data to the Baidu AI interface, and parse the returned JSON result.

The following is a simple sample code for converting audio files to text:

import requests
import json
import base64
import time
import pyaudio
import wave

def audio_recognition(audio_path):
    url = "https://aip.baidubce.com/rpc/2.0/aasr/v1/recognize"
    access_token = "your_access_token"  # 替换成你的API Key和Secret Key
    headers = {'Content-Type': 'application/json'}
    params = {'access_token': access_token}
    data = {
        'speech': base64.b64encode(open(audio_path, 'rb').read()).decode('utf-8'),
        'len': os.path.getsize(audio_path),
        'rate': 16000
    }
    
    response = requests.post(url, headers=headers, params=params, data=json.dumps(data))
    result = response.json()
    
    if 'error_code' in result:
        print("语音识别失败：" + result['error_msg'])
    else:
        for item in result['result']:
            print(item['word'])

Replace "your_access_token" in the above code with the API Key and Secret Key you applied for, Then call the audio_recognition function and pass in the path of the audio file to be recognized to get the recognition result.

3. Natural Language Processing

Baidu AI’s natural language processing interface can implement text segmentation, part-of-speech tagging, sentiment analysis and other functions. To use this interface, you need to apply for a natural language processing API Key and Secret Key. Then, use Python's requests library to send HTTP requests, send the text data to be processed to the Baidu AI interface, and parse the returned JSON results.

The following is a simple sample code for sentiment analysis of text:

import requests
import json

def sentiment_analysis(text):
    url = "https://aip.baidubce.com/rpc/2.0/nlp/v1/sentiment_classify"
    access_token = "your_access_token"  # 替换成你的API Key和Secret Key
    headers = {'Content-Type': 'application/json'}
    params = {'access_token': access_token}
    data = {'text': text}
    
    response = requests.post(url, headers=headers, params=params, data=json.dumps(data))
    result = response.json()
    
    if 'error_code' in result:
        print("情感分析失败：" + result['error_msg'])
    else:
        sentiment = result['items'][0]['sentiment']
        confidence = result['items'][0]['confidence']
        print("情感类型：" + sentiment + " (置信度：" + str(confidence) + ")")

Replace "your_access_token" in the above code with the API Key and Secret Key you applied for, and then Call the sentiment_analysis function and pass in the text to be analyzed to obtain the sentiment analysis results.

Through the above examples, you can see that it is very simple to use Python to connect with Baidu AI interface. Baidu AI provides a wealth of interfaces, covering many fields such as image recognition, speech recognition, and natural language processing. By making proper use of these interfaces, you can easily add intelligent functions to your applications and improve user experience and work efficiency. I hope you can give full play to the advantages of Baidu AI in actual development and create more intelligent applications.

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