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.gitattributes

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+demo[[:space:]](2).mp4 filter=lfs diff=lfs merge=lfs -text
+example1.png filter=lfs diff=lfs merge=lfs -text
+example10.png filter=lfs diff=lfs merge=lfs -text
+example2.png filter=lfs diff=lfs merge=lfs -text
+example3.png filter=lfs diff=lfs merge=lfs -text
+example4.png filter=lfs diff=lfs merge=lfs -text
+example5.png filter=lfs diff=lfs merge=lfs -text
+example6.png filter=lfs diff=lfs merge=lfs -text
+example7.png filter=lfs diff=lfs merge=lfs -text
+example8.png filter=lfs diff=lfs merge=lfs -text
+example9.png filter=lfs diff=lfs merge=lfs -text

LICENSE

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+Copyright (c) 2024. All rights reserved.
+
+PROPRIETARY SOFTWARE LICENSE AGREEMENT
+
+This software, including all associated files, code, neural network architecture, interfaces, and documentation ("Software") is proprietary and confidential.
+
+1. OWNERSHIP AND RESTRICTIONS
+- All intellectual property rights, including but not limited to copyrights, trade secrets, and proprietary information relating to the Software remain with the copyright holder
+- The neural network model, its weights, and training methodology are strictly confidential and proprietary
+- The web interface is for evaluation purposes only
+
+2. PROHIBITED ACTIVITIES
+You may not, and you may not permit others to:
+- Copy, modify, or create derivative works of the Software
+- Reverse engineer, decompile, or attempt to extract the source code
+- Use the Software architecture to train similar models
+- Redistribute, sell, rent, lease, sublicense, or transfer any rights to the Software
+- Access or attempt to access the proprietary neural network model without authorization
+
+3. INTERFACE ACCESS
+- The web interface is provided for evaluation purposes only
+- Access to the interface does not grant any rights to the underlying proprietary technology
+- Analysis results remain property of the copyright holder
+
+4. NO WARRANTY
+The Software is provided "AS IS" without warranty of any kind, express or implied.
+
+5. LIMITATION OF LIABILITY
+In no event shall the copyright holder be liable for any claim, damages or other liability arising from the use or distribution of the Software.
+
+ALL RIGHTS RESERVED.

README.md

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+---
+title: AiTradingCrypto
+emoji: 🏃
+colorFrom: gray
+colorTo: green
+sdk: gradio
+sdk_version: 5.7.1
+app_file: app.py
+pinned: false
+license: other
+short_description: New-Generation AI Computer Vision for Cryptocurrency Trading
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import cv2
+from PIL import Image
+import logging
+from huggingface_hub import hf_hub_download
+from huggingface_hub import login
+import matplotlib.pyplot as plt
+import matplotlib
+matplotlib.use('Agg')  
+
+# Настройка логирования
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Проверка наличия токена
+if "HUGGINGFACE_TOKEN" not in os.environ:
+    logger.error("HUGGINGFACE_TOKEN not found in environment variables!")
+else:
+    logger.info("HUGGINGFACE_TOKEN found")
+    # Аутентификация с использованием токена
+    login(token=os.environ["HUGGINGFACE_TOKEN"])
+    logger.info("Logged in to Hugging Face")
+
+# Определение размера изображения
+IMG_SHAPE = (479, 1221, 3)
+
+class SecureModel:
+    _instance = None
+    
+    def __init__(self):
+        try:
+            logger.info("Attempting to download model files...")
+            
+            # Загружаем файл модели
+            model_path = hf_hub_download(
+                repo_id="Dianor/trading-model-private",
+                filename="trading_modelbeta0.7.keras",
+                token=os.environ["HUGGINGFACE_TOKEN"]
+            )
+            
+            # Загружаем файл с кастомными слоями
+            layers_path = hf_hub_download(
+                repo_id="Dianor/trading-model-private",
+                filename="custom_trading_layers.py",
+                token=os.environ["HUGGINGFACE_TOKEN"]
+            )
+            
+            logger.info(f"Files downloaded successfully")
+            
+            # Импортируем кастомные слои из скачанного модуля
+            import importlib.util
+            import sys
+            
+            # Загружаем модуль с кастомными слоями
+            spec = importlib.util.spec_from_file_location("custom_trading_layers", layers_path)
+            custom_module = importlib.util.module_from_spec(spec)
+            sys.modules["custom_trading_layers"] = custom_module
+            spec.loader.exec_module(custom_module)
+            
+            # Получаем словарь custom_objects
+            custom_objects = custom_module.get_custom_objects()
+            
+            # Обновляем глобальные объекты TensorFlow
+            tf.keras.utils.get_custom_objects().update(custom_objects)
+            
+            # Загружаем модель
+            try:
+                self.model = tf.keras.models.load_model(
+                    model_path,
+                    custom_objects=custom_objects,
+                    compile=False
+                )
+            except Exception as load_error:
+                logger.warning(f"Direct load failed: {load_error}")
+                self.model = tf.keras.models.load_model(
+                    model_path,
+                    custom_objects=custom_objects,
+                    compile=False
+                )
+                self.model.compile(
+                    optimizer='adam',
+                    loss={
+                        'long_signal': 'binary_crossentropy',
+                        'short_signal': 'binary_crossentropy'
+                    },
+                    metrics=['accuracy']
+                )
+                
+            logger.info("Model loaded successfully")
+        except Exception as e:
+            logger.error(f"Failed to load model: {str(e)}")
+            raise
+
+    @classmethod
+    def get_instance(cls):
+        if cls._instance is None:
+            cls._instance = cls()
+        return cls._instance.model
+
+def preprocess_image(image):
+    try:
+        logger.info(f"Starting preprocessing. Input shape: {image.shape}")
+        
+        # Конвертируем в RGB если нужно (если изображение в BGR)
+        if len(image.shape) == 3 and image.shape[2] == 3:
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            
+        image = cv2.resize(image, (IMG_SHAPE[1], IMG_SHAPE[0]))  
+        
+        # Используем ту же нормализацию
+        image = image.astype('float32') / 255.0
+        
+        logger.info(f"Preprocessed shape: {image.shape}")
+        logger.info(f"Value range: [{image.min():.3f}, {image.max():.3f}]")
+        
+        return image
+        
+    except Exception as e:
+        logger.error(f"Error in preprocess_image: {str(e)}")
+        raise
+
+def analyze_trading_chart(input_image):
+    try:
+        model = SecureModel.get_instance()
+        
+        # Сохраняем оригинал для отображения
+        display_image = input_image.copy()
+        
+        # Логируем исходное изображение
+        logger.info(f"Raw input shape: {input_image.shape}")
+        logger.info(f"Raw input range: [{input_image.min()}, {input_image.max()}]")
+        
+        # Изменяем размер изображения до требуемого 
+        resized_image = cv2.resize(input_image, (1221, 479), interpolation=cv2.INTER_NEAREST)
+        
+        # Нормализуем изображение
+        image = resized_image.astype('float32') / 255.0
+        image = np.expand_dims(image, axis=0)  # Добавляем batch dimension
+        
+        # Логируем после обработки
+        logger.info(f"Processed input shape: {image.shape}")
+        logger.info(f"Processed input range: [{image.min():.3f}, {image.max():.3f}]")
+        
+        # Делаем предсказание
+        predictions = model.predict(image, verbose=0)
+        
+        # Логируем сырые предсказания
+        logger.info(f"Raw predictions: {predictions}")
+        
+        long_signal = float(predictions['long_signal'][0][0])
+        short_signal = float(predictions['short_signal'][0][0])
+        
+        logger.info(f"Final predictions: LONG={long_signal:.3f}, SHORT={short_signal:.3f}")
+        
+        # Создаем визуализацию
+        plt.style.use('dark_background')
+        fig = plt.figure(figsize=(15, 10), facecolor='#1E222D')
+        gs = fig.add_gridspec(2, 1, height_ratios=[3, 1], hspace=0.3)
+        
+        # График цены
+        ax1 = fig.add_subplot(gs[0])
+        ax1.imshow(display_image)  # Показываем оригинальное изображение
+        ax1.set_title('Trading Chart Analysis', color='#B7BDD7', pad=10, fontsize=14)
+        ax1.axis('off')
+        
+        # Панель сигналов
+        ax2 = fig.add_subplot(gs[1])
+        ax2.set_facecolor('#1E222D')
+        
+        bar_positions = [0, 1]
+        signal_values = [long_signal, short_signal]
+        colors = ['#26a69a', '#ef5350']
+        labels = ['Long Signal', 'Short Signal']
+        
+        bars = ax2.bar(bar_positions, signal_values, color=colors)
+        ax2.set_xticks(bar_positions)
+        ax2.set_xticklabels(labels, color='#B7BDD7', fontsize=12)
+        ax2.set_ylim(0, 1)
+        ax2.set_ylabel('Signal Strength', color='#B7BDD7', fontsize=12)
+        ax2.grid(True, alpha=0.2)
+        ax2.tick_params(colors='#B7BDD7')
+        
+        # Добавляем значения над барами
+        for bar in bars:
+            height = bar.get_height()
+            ax2.text(bar.get_x() + bar.get_width()/2., height,
+                    f'{height:.3f}',
+                    ha='center', va='bottom', color='#B7BDD7',
+                    fontsize=12)
+        
+        ax2.axhline(y=0.8, color='white', linestyle='--', alpha=0.5, label='Signal Threshold')
+        ax2.legend(loc='upper right', bbox_to_anchor=(0.98, 0.98))
+        
+        # Конвертируем график в изображение
+        fig.canvas.draw()
+        buf = fig.canvas.buffer_rgba()
+        img = np.asarray(buf)
+        plt.close(fig)
+        
+        return img
+        
+    except Exception as e:
+        logger.error(f"Error in analyze_trading_chart: {str(e)}")
+        logger.exception("Full traceback:")
+        return display_image
+
+# Создаем интерфейс с табами
+def create_interface():
+    with gr.Blocks(theme=gr.themes.Default()) as demo:
+        gr.Markdown("""
+        # 🚀 Revolutionary Neural Vision Trading
+
+        ## Next-Generation AI Computer Vision for Cryptocurrency Trading
+
+        Introducing the world's first neural network that trades cryptocurrency through pure visual comprehension—a breakthrough technology that sees charts just like professional traders do.
+
+        This revolutionary AI doesn't rely on traditional indicators or mathematical patterns. Instead, it employs advanced computer vision to interpret market dynamics visually, analyzing real-time price action with human-like perception but machine-level precision.
+
+        The system provides confidence-based entry signals, automatically executing trades when conviction reaches 0.9 or higher—mimicking the decision-making process of elite traders while eliminating emotional bias.
+
+        ---
+
+        ### Unprecedented Market Understanding:
+
+        ❗️ Visual price action analysis based on pure Computer Vision
+
+        ❗️ Dynamic support/resistance identification through visual context
+
+        ❗️ Real-time decision making focused on the critical last candle
+
+        ---
+
+        Try it now! Upload your TradingView/Binance dark theme chart, or use our examples and experience trading intelligence that exists nowhere else in the market.
+
+        **💼 Limited partnership opportunities available for qualified investors. Contact us to join the visual trading revolution.**
+        """)
+        
+        with gr.Tabs():
+            # Таб анализа графиков
+            with gr.Tab("Signal Analysis"):
+                with gr.Row():
+                    # ��евая колонка для ввода
+                    with gr.Column(scale=1):
+                        gr.Markdown("""
+                        ### Upload Your Trading Chart
+                        Or use example charts below
+                        """)
+                        input_image = gr.Image(type="numpy", height=400)
+                    
+                    # Правая колонка для вывода
+                    with gr.Column(scale=1):
+                        gr.Markdown("""
+                        ### Analysis Results
+                        - Long Signal (Green): Upward movement probability
+                        - Short Signal (Red): Downward movement probability
+                        """)
+                        output_image = gr.Image(type="numpy", height=400)
+                
+                analyze_btn = gr.Button("Analyze Chart", size="lg")
+                analyze_btn.click(
+                    fn=analyze_trading_chart,
+                    inputs=input_image,
+                    outputs=output_image
+                )
+                
+                gr.Markdown("### Example Charts")
+                gr.Examples(
+                    examples=[
+                        "example1.png", "example2.png", "example3.png", "example4.png", 
+                        "example5.png", "example6.png", "example7.png", "example8.png", 
+                        "example9.png", "example10.png"
+                    ],
+                    inputs=input_image,
+                    outputs=output_image,
+                    fn=analyze_trading_chart,
+                    cache_examples=True,
+                    examples_per_page=10  
+                )
+            
+            # Таб с демонстрационным видео
+            with gr.Tab("Trading Demo"):
+                gr.Markdown("""
+                ## Trading System Backtesting Demo
+                Watch how our AI trading system performs in different market conditions.
+                """)
+                
+                with gr.Row():
+                    with gr.Column(scale=1, min_width=800):
+                        gr.Video("demo.mp4")
+                
+                gr.Markdown("""
+                ### What you're seeing in the demo:
+                - Real-time trading decisions
+                - Signal generation and execution
+                - Performance metrics and profit visualization
+                - Risk management in action
+                """)
+
+    return demo
+
+if __name__ == "__main__":
+    try:
+        logger.info("Initializing model at startup...")
+        SecureModel.get_instance()
+        logger.info("Model initialized successfully")
+    except Exception as e:
+        logger.error(f"Failed to initialize model at startup: {str(e)}")
+    
+    demo = create_interface()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False
+    )

demo (2).mp4

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+oid sha256:9d9083447574f6c6916869939eab2b6a44a03b03b08cc62b31da3d72260b0ab6
+size 33122571

gitattributes

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requirements.txt

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+tensorflow-cpu==2.18.0
+gradio==3.50.2
+numpy==1.26.0
+Pillow==10.1.0
+opencv-python-headless==4.8.1.78
+huggingface-hub>=0.19.4
+keras==3.5.0