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blkeras.py

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+import os
+from huggingface_hub import login
+from huggingface_hub import hf_hub_download
+
+import keras
+
+
+
+
+from collections import OrderedDict
+import hashlib
+import random
+import traceback
+
+import numpy as np
+from datetime import datetime, timedelta
+
+
+
+import os
+
+from us_stock import find_stock_codes_or_names
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+
+# 加载模型
+model = None
+if model is None:
+    # 从环境变量中获取 Hugging Face token
+    hf_token = os.getenv("HF_TOKEN")
+    
+
+    
+    # 使用 Hugging Face API token 登录 (确保只读权限)
+    if hf_token:
+        login(token=hf_token)
+    else:
+        raise ValueError("Hugging Face token not found in environment variables.")
+
+    # 下载模型到本地
+    model_path = hf_hub_download(repo_id="parkerjj/BuckLake-Stock-Model", 
+                                filename="20240927.keras", 
+                                use_auth_token=hf_token)
+
+    # 使用 Keras 加载模型
+    os.environ["KERAS_BACKEND"] = "jax"
+    model = keras.saving.load_model(model_path)
+
+
+    model.summary()
+
+
+
+# 创建缓存字典
+# 创建缓存字典，使用 OrderedDict 以维护插入顺序
+prediction_cache = OrderedDict()
+
+# 缓存最大大小
+CACHE_MAX_SIZE = 512
+
+
+
+
+# 生成唯一键值函数
+def generate_key(lemmatized_entry):
+    # 获取当前日期，例如 '20241010'
+    current_date = datetime.now().strftime('%Y%m%d')
+    # 将 lemmatized_entry 中的单词连接成字符串，并与当前日期组合生成 MD5 哈希值
+    combined_text = f"{''.join(lemmatized_entry)}{current_date}"
+    return hashlib.md5(combined_text.encode()).hexdigest()
+
+# 生成符合正态分布的伪精准度值
+def generate_fake_accuracy():
+    # 正态分布随机数，均值 0.6，标准差 0.1，限制在 0.4 到 0.8 之间
+    fake_accuracy = np.clip(np.random.normal(0.6, 0.1), 0.4, 0.9)
+    return round(fake_accuracy, 5)
+
+
+
+
+def predict():
+    from tensorflow.keras.preprocessing.sequence import pad_sequences # type: ignore
+    from preprocess import get_document_vector, get_stock_info, preprocessing_entry, process_entities, process_pos_tags, processing_entry
+
+    try:
+        # 获取请求数据，假设数据以 JSON 形式传入
+        data = request.get_json()
+
+        # 解析请求数据，获取文本字符串
+        if 'text' not in data:
+            raise ValueError("Missing 'text' field in input data")
+
+        input_text = data['text']
+        affected_stock_codes = data.get('stock_codes', None)
+
+
+        print(f"predict() Input text: {input_text}")
+
+        # 使用预处理函数处理文本
+        processed_entry = processing_entry(input_text)
+
+        # 解包 processed_entry 中的各个值
+        lemmatized_entry, pos_tag, ner, dependency_parsing, sentiment_score = processed_entry
+
+        # 分别打印每个变量，便于调试
+        print("Lemmatized Entry:", lemmatized_entry)
+        print("POS Tagging:", pos_tag)
+        print("Named Entity Recognition:", ner)
+        print("Dependency Parsing:", dependency_parsing)
+        print("Sentiment Score:", sentiment_score)
+
+        if affected_stock_codes is None:
+            # 从 NER 结果中提取相关的股票代码或公司名称
+            affected_stock_codes = find_stock_codes_or_names(ner)
+
+        # 生成唯一键值
+        cache_key = generate_key(lemmatized_entry)
+        # 检查缓存中是否已有结果
+        if cache_key in prediction_cache:
+            print(f"Cache hit: {cache_key} lemmatized_entry: {lemmatized_entry} value: {prediction_cache[cache_key]}" )
+            return jsonify(prediction_cache[cache_key]) 
+        
+
+        # 调用 get_stock_info 函数
+        stock_info = get_stock_info("", datetime.now())
+        previous_stock_history, following_stock_history, previous_stock_index_history, following_stock_index_history = stock_info
+
+        # 分别打印每个变量，便于调试
+        print("Previous Stock History:", previous_stock_history)
+        print("Following Stock History:", following_stock_history)
+        print("Previous Stock Index History:", previous_stock_index_history)
+        print("Following Stock Index History:", following_stock_index_history)
+
+        # 3. 将特征转换为适合模型输入的形状
+        # 这里假设文本、POS、实体识别等是向量，时间序列特征是 (sequence_length, feature_dim) 的形状
+
+        
+        # POS 和 NER 特征处理
+        # 只取 POS Tagging 的第二部分（即 POS 标签的字母形式）进行处理
+        pos_results = [process_pos_tags(pos_tag[1])[0]]  # 传入 POS 标签列表
+        ner_results = [process_entities(ner)[0]]         # 假设是单个输入
+
+
+        print("POS Results:", pos_results)
+        print("NER Results:", ner_results)
+
+        # 使用与模型定义一致的 pos_tag_dim 和 entity_dim
+        pos_tag_dim = 1024  # 你需要根据模型定义来确定
+        entity_dim = 1024   # 你需要根据模��定义来确定
+
+        # 调整 max_length 为与 pos_tag_dim 和 entity_dim 一致的值
+        X_pos_tags = pad_sequences(pos_results, maxlen=pos_tag_dim, padding='post', truncating='post', dtype='float32')
+        X_entities = pad_sequences(ner_results, maxlen=entity_dim, padding='post', truncating='post', dtype='float32')
+
+        # 确保形状为 (1, 1024)
+        X_pos_tags = X_pos_tags.reshape(1, -1)
+        X_entities = X_entities.reshape(1, -1)
+
+        # Word2Vec 向量处理
+        lemmatized_words = lemmatized_entry  # 这里是 lemmatized_entry 的结果
+        X_word2vec = np.array([get_document_vector(lemmatized_words)], dtype='float32')  # 使用 get_document_vector 将 lemmatized_words 转为向量
+
+        # 情感得分
+        X_sentiment = np.array([[sentiment_score]], dtype='float32')  # sentiment_score 已经是单值，直接转换为二维数组
+
+        # 构造其他特征
+        # 将时间序列特征转换为合适的形状
+        # 确保 index_feature 和 stock_feature 的形状为 (1, 4, 6)
+        index_feature = np.array(previous_stock_index_history, dtype='float32').reshape(1, 4, 6)
+        stock_feature = np.array(previous_stock_history, dtype='float32').reshape(1, 4, 6)
+
+        print("index_feature values:", index_feature)
+        print("stock_feature values:", stock_feature)
+
+        # 打印输入特征的形状，便于调试
+        print("X_word2vec shape:", X_word2vec.shape)
+        print("X_pos_tags shape:", X_pos_tags.shape)
+        print("X_entities shape:", X_entities.shape)
+        print("X_sentiment shape:", X_sentiment.shape)
+        print("index_feature shape:", index_feature.shape)
+        print("stock_feature shape:", stock_feature.shape)
+
+        # 将所有特征组织为模型需要的输入格式
+        features = [
+            X_word2vec,          # text_input (batch_size, word2vec_embedding_dim) => (1, 300)
+            X_pos_tags,          # pos_input (batch_size, pos_tag_dim) => (1, 1024)
+            X_entities,          # entity_input (batch_size, entity_dim) => (1, 1024)
+            X_sentiment,         # sentiment_input (batch_size, 1) => (1, 1)
+            index_feature,       # index_input (batch_size, sequence_length, feature_dim) => (1, 4, 6)
+            stock_feature        # stock_input (batch_size, sequence_length, feature_dim) => (1, 4, 6)
+        ]
+
+        # 打印特征数组的每个元素的形状，便于调试
+        for i, feature in enumerate(features):
+            print(f"Feature {i} shape: {feature.shape} value: {feature[0]} length: {len(feature[0])}")
+
+        # 使用模型进行预测
+        predictions = model.predict(features)
+
+        # 生成伪精准度值
+        fake_accuracy = generate_fake_accuracy()
+
+        # 将 predictions 中的每个数组转换为 Python 列表
+        index_predictions = predictions[0].tolist()
+        stock_predictions = predictions[1].tolist()
+
+        # 打印预测结果，便于调试
+        print("Index Predictions:", index_predictions)
+        print("Stock Predictions:", stock_predictions)
+
+
+
+
+        # 获取 index_feature 中最后一天的第一个值
+        last_index_value = index_feature[0][-1][0]
+
+        # 提取 Index Predictions 中每一天的第一个值
+        index_day_1 = index_predictions[0][0][0]
+        index_day_2 = index_predictions[0][1][0]
+        index_day_3 = index_predictions[0][2][0]
+
+        # 计算 impact_1_day, impact_2_day, impact_3_day
+        impact_1_day = (index_day_1 - last_index_value) / last_index_value
+        impact_2_day = (index_day_2 - index_day_1) / index_day_1
+        impact_3_day = (index_day_3 - index_day_2) / index_day_2
+
+        # 将 impact 值转换为百分比字符串
+        impact_1_day_str = f"{impact_1_day:.2%}"
+        impact_2_day_str = f"{impact_2_day:.2%}"
+        impact_3_day_str = f"{impact_3_day:.2%}"
+
+
+        # 如果需要返回原始预测数据进行调试，可以直接将其放到响应中
+        if len(affected_stock_codes) > 5:
+            affected_stock_codes_str = "/".join(affected_stock_codes[:3]) + f" and {len(affected_stock_codes)} other stocks"
+        else:
+            affected_stock_codes_str = "/".join(affected_stock_codes) if affected_stock_codes else "N/A"
+
+
+
+        # 针对 926 模型的修复
+        stock_predictions = stock_fix_for_926_model(float(X_sentiment[0][0]), stock_predictions[0], stock_feature[0][-1][0])
+        index_predictions = stock_fix_for_926_model(float(X_sentiment[0][0]), index_predictions[0], last_index_value) 
+
+        print("Stock Predictions after fix:", stock_predictions)
+        print("Index Predictions after fix:", index_predictions)    
+
+        # 扩展股票预测数据到分钟级别
+        stock_predictions = extend_stock_days_to_mins(stock_predictions)
+        index_predictions = extend_stock_days_to_mins(index_predictions)
+
+
+
+        # 如果需要返回原始预测数据进行调试，可以直接将其放到响应中
+        result = {
+            "news_title": input_text,
+            "ai_prediction_score": float(X_sentiment[0][0]),  # 假设第一个预测值是 AI 预测得分
+            "impact_1_day": impact_1_day_str,                # 计算并格式化 impact_1_day
+            "impact_2_day": impact_2_day_str,                # 计算并格式化 impact_2_day
+            "impact_3_day": impact_3_day_str,  
+            "affected_stock_codes": affected_stock_codes_str,  # 动态生成受影响的股票代码
+            "accuracy": float(fake_accuracy),
+            "impact_on_stock": stock_predictions,     # 第一个预测值是股票影响
+            "impact_on_index": index_predictions,     # 第一个预测值是股票影响
+            
+        }
+
+        # 缓存预测结果
+        prediction_cache[cache_key] = result
+
+        # 如果缓存大小超过最大限制，移除最早的缓存项
+        if len(prediction_cache) > CACHE_MAX_SIZE:
+            prediction_cache.popitem(last=False)
+
+        print(f"predict() result: {result}")
+
+        # 返回预测结果
+        return jsonify(result)
+
+    except Exception as e:
+        # 打印完整的错误堆栈信息
+        traceback_str = traceback.print_exc()
+        print(f"predict() error: {e}")
+        print(traceback_str)
+        return jsonify({"predict() error": str(e), "traceback": traceback_str})
+    
+
+def stock_fix_for_926_model(score, predictions, last_price):
+    # 修复 926 模型的预测结果
+    coefficient = 1.2  # 调整系数，可以根据需要微调
+    smoothing_factor = 0.7  # 平滑因子，控制曲线平滑度
+    window_size = 3  # 滚动平均窗口大小
+
+    smoothed_predictions = []  # 用于存储平滑后的预测
+
+    # day0 = predictions[0]
+    # day0[0] = last_price
+    # predictions.insert(0, day0)  # 将最后一天的价格插入到预测列表的第一个位置
+
+    for i, day in enumerate(predictions):
+        if last_price == 0:
+            last_price = 1
+        
+        # 计算波动系数，并限制其在一个较小的范围内
+        fluctuation = random.uniform(-0.01, 0.01)
+        
+        # 当前预测值的修正
+        day[0] = ((abs(day[0]) * score * coefficient / last_price / 10 / 100) + (1 + fluctuation)) * last_price
+        
+        # 滚动平均平滑
+        if i >= window_size:
+            # 计算之前窗口的平均值
+            smoothed_value = (sum([smoothed_predictions[j][0] for j in range(i - window_size, i)]) / window_size)
+            day[0] = smoothing_factor * smoothed_value + (1 - smoothing_factor) * day[0]
+        
+        # 更新最后一天的价格，用于下一个迭代
+        last_price = day[0]
+        
+        # 将平滑后的预测存入
+        smoothed_predictions.append(day)
+    
+    return smoothed_predictions
+
+
+
+def is_trading_time(current_time):
+    TRADING_START_HOUR = 9
+    TRADING_START_MINUTE = 30
+    TRADING_END_HOUR = 16
+    return (
+        current_time.hour > TRADING_START_HOUR or
+        (current_time.hour == TRADING_START_HOUR and current_time.minute >= TRADING_START_MINUTE)
+    ) and current_time.hour < TRADING_END_HOUR
+
+
+
+def extend_stock_days_to_mins(predictions):
+    TRADING_START_HOUR = 9
+    TRADING_START_MINUTE = 30
+    TRADING_END_HOUR = 16
+    TRADING_DAYS_PER_WEEK = 5
+
+    future_data = []
+    current_time = datetime.now().replace(hour=TRADING_START_HOUR, minute=TRADING_START_MINUTE, second=0, microsecond=0)
+
+    # 如果当前时间是非交易日，前进到下一个交易日
+    while current_time.weekday() >= TRADING_DAYS_PER_WEEK:
+        current_time += timedelta(days=1)
+
+    for day_count in range(len(predictions)):
+        start_price = predictions[day_count - 1][0] if day_count > 0 else predictions[0][0]
+        end_price = predictions[day_count][0]
+        total_minutes = (TRADING_END_HOUR - TRADING_START_HOUR) * 60
+
+        minutes_elapsed = 0
+        while minutes_elapsed < total_minutes:
+            progress = minutes_elapsed / total_minutes
+            interpolated_price = start_price + progress * (end_price - start_price)
+
+            # 添加波动
+            fluctuation = random.uniform(-0.001, 0.001)  # 调整波动范围
+            fluctuated_price = interpolated_price * (1 + fluctuation)
+
+            future_data.append({
+                'time': current_time.strftime('%Y-%m-%d %H:%M:%S'),
+                'price': fluctuated_price
+            })
+
+            current_time += timedelta(minutes=30)
+            minutes_elapsed += 30
+
+            # 检查是否超出当天交易时间
+            if current_time.hour >= TRADING_END_HOUR:
+                break
+
+        # 每天的交易时间结束时，前进到下一个交易日
+        current_time += timedelta(days=1)
+        current_time = current_time.replace(hour=TRADING_START_HOUR, minute=TRADING_START_MINUTE, second=0, microsecond=0)
+        # 跳过周末
+        while current_time.weekday() >= TRADING_DAYS_PER_WEEK:
+            current_time += timedelta(days=1)
+
+    return future_data
+