锦中融合门户系统

import flask

from flask import request

import nltk

from nltk.stem import WordNetLemmatizer

from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.metrics.pairwise import cosine_similarity

app = flask.Flask(__name__)

lemmatizer = WordNetLemmatizer()

# 模拟一些文档

documents = [

"人工智能正在改变我们的生活。",

"融合门户系统提高了信息访问效率。",

"自然语言处理是AI的重要组成部分。",

"机器学习可以用于预测用户行为。",

"智能推荐系统能提升用户体验。"

]

# 初始化TF-IDF向量化器

vectorizer = TfidfVectorizer()

tfidf_matrix = vectorizer.fit_transform(documents)

@app.route('/search', methods=['POST'])

def search():

user_query = request.json.get('query')

if not user_query:

return {"error": "Missing query parameter"}, 400

# 对用户查询进行预处理

tokens = nltk.word_tokenize(user_query)

lemmatized_tokens = [lemmatizer.lemmatize(token) for token in tokens]

processed_query = ' '.join(lemmatized_tokens)

# 向量化查询

query_tfidf = vectorizer.transform([processed_query])

# 计算相似度

similarities = cosine_similarity(query_tfidf, tfidf_matrix).flatten()

# 找到最相关的文档

most_similar_index = similarities.argmax()

result = {

"query": user_query,

"most_similar_document": documents[most_similar_index],

"similarity_score": similarities[most_similar_index]

}

return flask.jsonify(result)

if __name__ == '__main__':

app.run(debug=True)

import flask

from flask import request, jsonify

import numpy as np

import tensorflow as tf

from PIL import Image

import io

app = flask.Flask(__name__)

# 加载预训练模型

model = tf.keras.models.load_model('models/imagenet_mobilenetv2.h5')

@app.route('/image-classify', methods=['POST'])

def classify_image():

file = request.files['image']

if not file:

return {"error": "No image provided"}, 400

try:

img = Image.open(io.BytesIO(file.read()))

img = img.resize((224, 224))

img_array = np.array(img) / 255.0

img_array = np.expand_dims(img_array, axis=0)

predictions = model.predict(img_array)

predicted_class = np.argmax(predictions)

# 假设我们有一个类别标签映射

class_labels = ['cat', 'dog', 'car', 'tree', 'house']

result = {

"predicted_class": class_labels[predicted_class],

"confidence": float(predictions[0][predicted_class])

}

return jsonify(result)

except Exception as e:

return {"error": str(e)}, 500

if __name__ == '__main__':

app.run(debug=True)