using LLM for sentiment analysis
This commit is contained in:
Dobromir Popov
180
docker-compose.integration-example.yml
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180
docker-compose.integration-example.yml
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version: '3.8'
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services:
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# Your existing trading dashboard
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trading-dashboard:
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image: python:3.11-slim
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container_name: trading-dashboard
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ports:
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- "8050:8050" # Dash/Streamlit port
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volumes:
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- ./config:/config
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- ./models:/models
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environment:
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- MODEL_RUNNER_URL=http://docker-model-runner:11434
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- LLAMA_CPP_URL=http://llama-cpp-server:8000
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- DASHBOARD_PORT=8050
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depends_on:
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- docker-model-runner
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command: >
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sh -c "
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pip install dash requests &&
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python -c '
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import dash
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from dash import html, dcc
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import requests
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app = dash.Dash(__name__)
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def get_models():
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try:
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response = requests.get(\"http://docker-model-runner:11434/api/tags\")
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return response.json()
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except:
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return {\"models\": []}
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app.layout = html.Div([
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html.H1(\"Trading Dashboard with AI Models\"),
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html.Div([
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html.H3(\"Available Models:\"),
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html.Pre(str(get_models()))
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]),
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dcc.Input(id=\"prompt\", type=\"text\", placeholder=\"Enter your prompt...\"),
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html.Button(\"Generate\", id=\"generate-btn\"),
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html.Div(id=\"output\")
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])
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@app.callback(
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dash.dependencies.Output(\"output\", \"children\"),
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[dash.dependencies.Input(\"generate-btn\", \"n_clicks\")],
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[dash.dependencies.State(\"prompt\", \"value\")]
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)
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def generate_text(n_clicks, prompt):
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if n_clicks and prompt:
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try:
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response = requests.post(
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\"http://docker-model-runner:11434/api/generate\",
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json={\"model\": \"ai/smollm2:135M-Q4_K_M\", \"prompt\": prompt}
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)
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return response.json().get(\"response\", \"No response\")
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except Exception as e:
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return f\"Error: {str(e)}\"
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return \"Enter a prompt and click Generate\"
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if __name__ == \"__main__\":
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app.run_server(host=\"0.0.0.0\", port=8050, debug=True)
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'
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"
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networks:
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- model-runner-network
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# AI-powered trading analysis service
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trading-analysis:
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image: python:3.11-slim
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container_name: trading-analysis
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volumes:
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- ./config:/config
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- ./models:/models
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- ./data:/data
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environment:
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- MODEL_RUNNER_URL=http://docker-model-runner:11434
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- ANALYSIS_INTERVAL=300 # 5 minutes
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depends_on:
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- docker-model-runner
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command: >
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sh -c "
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pip install requests pandas numpy &&
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python -c '
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import time
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import requests
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import json
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def analyze_market():
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prompt = \"Analyze current market conditions and provide trading insights\"
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try:
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response = requests.post(
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\"http://docker-model-runner:11434/api/generate\",
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json={\"model\": \"ai/smollm2:135M-Q4_K_M\", \"prompt\": prompt}
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)
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analysis = response.json().get(\"response\", \"Analysis unavailable\")
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print(f\"[{time.strftime(\"%Y-%m-%d %H:%M:%S\")}] Market Analysis: {analysis[:200]}...\")
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except Exception as e:
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print(f\"[{time.strftime(\"%Y-%m-%d %H:%M:%S\")}] Error: {str(e)}\")
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print(\"Trading Analysis Service Started\")
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while True:
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analyze_market()
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time.sleep(300) # 5 minutes
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'
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"
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networks:
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- model-runner-network
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# Model performance monitor
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model-monitor:
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image: python:3.11-slim
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container_name: model-monitor
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ports:
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- "9091:9091" # Monitoring dashboard
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environment:
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- MODEL_RUNNER_URL=http://docker-model-runner:11434
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- MONITOR_PORT=9091
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depends_on:
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- docker-model-runner
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command: >
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sh -c "
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pip install flask requests psutil &&
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python -c '
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from flask import Flask, jsonify
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import requests
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import time
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import psutil
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app = Flask(__name__)
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start_time = time.time()
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@app.route(\"/health\")
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def health():
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return jsonify({
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\"status\": \"healthy\",
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\"uptime\": time.time() - start_time,
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\"cpu_percent\": psutil.cpu_percent(),
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\"memory\": psutil.virtual_memory()._asdict()
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})
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@app.route(\"/models\")
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def models():
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try:
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response = requests.get(\"http://docker-model-runner:11434/api/tags\")
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return jsonify(response.json())
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except Exception as e:
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return jsonify({\"error\": str(e)})
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@app.route(\"/performance\")
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def performance():
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try:
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# Test model response time
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start = time.time()
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response = requests.post(
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\"http://docker-model-runner:11434/api/generate\",
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json={\"model\": \"ai/smollm2:135M-Q4_K_M\", \"prompt\": \"test\"}
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)
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response_time = time.time() - start
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return jsonify({
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\"response_time\": response_time,
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\"status\": \"ok\" if response.status_code == 200 else \"error\"
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})
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except Exception as e:
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return jsonify({\"error\": str(e)})
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print(\"Model Monitor Service Started on port 9091\")
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app.run(host=\"0.0.0.0\", port=9091)
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'
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"
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networks:
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- model-runner-network
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networks:
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model-runner-network:
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external: true # Use the network created by the main compose file
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