gogo2/docker-compose.integration-example.yml

version: '3.8'

services:
  # Your existing trading dashboard
  trading-dashboard:
    image: python:3.11-slim
    container_name: trading-dashboard
    ports:
      - "8050:8050"  # Dash/Streamlit port
    volumes:
      - ./config:/config
      - ./models:/models
    environment:
      - MODEL_RUNNER_URL=http://docker-model-runner:11434
      - LLAMA_CPP_URL=http://llama-cpp-server:8000
      - DASHBOARD_PORT=8050
    depends_on:
      - docker-model-runner
    command: >
      sh -c "
      pip install dash requests &&
      python -c '
      import dash
      from dash import html, dcc
      import requests

      app = dash.Dash(__name__)

      def get_models():
          try:
              response = requests.get(\"http://docker-model-runner:11434/api/tags\")
              return response.json()
          except:
              return {\"models\": []}

      app.layout = html.Div([
          html.H1(\"Trading Dashboard with AI Models\"),
          html.Div([
              html.H3(\"Available Models:\"),
              html.Pre(str(get_models()))
          ]),
          dcc.Input(id=\"prompt\", type=\"text\", placeholder=\"Enter your prompt...\"),
          html.Button(\"Generate\", id=\"generate-btn\"),
          html.Div(id=\"output\")
      ])

      @app.callback(
          dash.dependencies.Output(\"output\", \"children\"),
          [dash.dependencies.Input(\"generate-btn\", \"n_clicks\")],
          [dash.dependencies.State(\"prompt\", \"value\")]
      )
      def generate_text(n_clicks, prompt):
          if n_clicks and prompt:
              try:
                  response = requests.post(
                      \"http://docker-model-runner:11434/api/generate\",
                      json={\"model\": \"ai/smollm2:135M-Q4_K_M\", \"prompt\": prompt}
                  )
                  return response.json().get(\"response\", \"No response\")
              except Exception as e:
                  return f\"Error: {str(e)}\"
          return \"Enter a prompt and click Generate\"

      if __name__ == \"__main__\":
          app.run_server(host=\"0.0.0.0\", port=8050, debug=True)
      '
      "
    networks:
      - model-runner-network

  # AI-powered trading analysis service
  trading-analysis:
    image: python:3.11-slim
    container_name: trading-analysis
    volumes:
      - ./config:/config
      - ./models:/models
      - ./data:/data
    environment:
      - MODEL_RUNNER_URL=http://docker-model-runner:11434
      - ANALYSIS_INTERVAL=300  # 5 minutes
    depends_on:
      - docker-model-runner
    command: >
      sh -c "
      pip install requests pandas numpy &&
      python -c '
      import time
      import requests
      import json

      def analyze_market():
          prompt = \"Analyze current market conditions and provide trading insights\"
          try:
              response = requests.post(
                  \"http://docker-model-runner:11434/api/generate\",
                  json={\"model\": \"ai/smollm2:135M-Q4_K_M\", \"prompt\": prompt}
              )
              analysis = response.json().get(\"response\", \"Analysis unavailable\")
              print(f\"[{time.strftime(\"%Y-%m-%d %H:%M:%S\")}] Market Analysis: {analysis[:200]}...\")
          except Exception as e:
              print(f\"[{time.strftime(\"%Y-%m-%d %H:%M:%S\")}] Error: {str(e)}\")

      print(\"Trading Analysis Service Started\")
      while True:
          analyze_market()
          time.sleep(300)  # 5 minutes
      '
      "
    networks:
      - model-runner-network

  # Model performance monitor
  model-monitor:
    image: python:3.11-slim
    container_name: model-monitor
    ports:
      - "9091:9091"  # Monitoring dashboard
    environment:
      - MODEL_RUNNER_URL=http://docker-model-runner:11434
      - MONITOR_PORT=9091
    depends_on:
      - docker-model-runner
    command: >
      sh -c "
      pip install flask requests psutil &&
      python -c '
      from flask import Flask, jsonify
      import requests
      import time
      import psutil

      app = Flask(__name__)
      start_time = time.time()

      @app.route(\"/health\")
      def health():
          return jsonify({
              \"status\": \"healthy\",
              \"uptime\": time.time() - start_time,
              \"cpu_percent\": psutil.cpu_percent(),
              \"memory\": psutil.virtual_memory()._asdict()
          })

      @app.route(\"/models\")
      def models():
          try:
              response = requests.get(\"http://docker-model-runner:11434/api/tags\")
              return jsonify(response.json())
          except Exception as e:
              return jsonify({\"error\": str(e)})

      @app.route(\"/performance\")
      def performance():
          try:
              # Test model response time
              start = time.time()
              response = requests.post(
                  \"http://docker-model-runner:11434/api/generate\",
                  json={\"model\": \"ai/smollm2:135M-Q4_K_M\", \"prompt\": \"test\"}
              )
              response_time = time.time() - start

              return jsonify({
                  \"response_time\": response_time,
                  \"status\": \"ok\" if response.status_code == 200 else \"error\"
              })
          except Exception as e:
              return jsonify({\"error\": str(e)})

      print(\"Model Monitor Service Started on port 9091\")
      app.run(host=\"0.0.0.0\", port=9091)
      '
      "
    networks:
      - model-runner-network

networks:
  model-runner-network:
    external: true  # Use the network created by the main compose file