Bereitstellung eines KI-Traffic-Congestion-Predictor mit AWS Bedrock: Ein vollständiger Überblick

# Create a new virtual environment
python -m venv bedrock-env
source bedrock-env/bin/activate  # On Windows use: bedrock-env\Scripts\activate

# Install required packages
pip install boto3 pandas numpy scikit-learn streamlit plotly

import boto3
import json
import numpy as np
import pandas as pd
from typing import Dict, Any

class TrafficPredictor:
    def __init__(self):
        self.bedrock = boto3.client(
            service_name='bedrock-runtime',
            region_name='us-east-1'  # Change to your region
        )

    def prepare_features(self, input_data: Dict[str, Any]) -> pd.DataFrame:
        # Convert input data to model features
        hour = input_data['hour']
        day = input_data['day']

        features = pd.DataFrame({
            'hour_sin': [np.sin(2 * np.pi * hour/24)],
            'hour_cos': [np.cos(2 * np.pi * hour/24)],
            'day_sin': [np.sin(2 * np.pi * day/7)],
            'day_cos': [np.cos(2 * np.pi * day/7)],
            'temperature': [input_data['temperature']],
            'precipitation': [input_data['precipitation']],
            'special_event': [input_data['special_event']],
            'road_work': [input_data['road_work']],
            'vehicle_count': [input_data['vehicle_count']]
        })
        return features

    def predict(self, input_data: Dict[str, Any]) -> float:
        features = self.prepare_features(input_data)

        # Prepare prompt for Claude
        prompt = f"""
        Based on the following traffic conditions, predict the congestion level (0-10):
        - Time: {input_data['hour']}:00
        - Day of week: {input_data['day']}
        - Temperature: {input_data['temperature']}°C
        - Precipitation: {input_data['precipitation']}mm
        - Special event: {'Yes' if input_data['special_event'] else 'No'}
        - Road work: {'Yes' if input_data['road_work'] else 'No'}
        - Vehicle count: {input_data['vehicle_count']}

        Return only the numerical prediction.
        """

        # Call Bedrock
        response = self.bedrock.invoke_model(
            modelId='anthropic.claude-v2',
            body=json.dumps({
                "prompt": prompt,
                "max_tokens": 10,
                "temperature": 0
            })
        )

        # Parse response
        response_body = json.loads(response['body'].read())
        prediction = float(response_body['completion'].strip())

        return np.clip(prediction, 0, 10)

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from bedrock_integration import TrafficPredictor
from typing import Dict, Any

app = FastAPI()
predictor = TrafficPredictor()

class PredictionInput(BaseModel):
    hour: int
    day: int
    temperature: float
    precipitation: float
    special_event: bool
    road_work: bool
    vehicle_count: int

@app.post("/predict")
async def predict_traffic(input_data: PredictionInput) -> Dict[str, float]:
    try:
        prediction = predictor.predict(input_data.dict())
        return {"congestion_level": prediction}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

import boto3
import json

def create_infrastructure():
    # Create ECR repository
    ecr = boto3.client('ecr')
    try:
        ecr.create_repository(repositoryName='traffic-predictor')
    except ecr.exceptions.RepositoryAlreadyExistsException:
        pass

    # Create ECS cluster
    ecs = boto3.client('ecs')
    ecs.create_cluster(clusterName='traffic-predictor-cluster')

    # Create task definition
    task_def = {
        'family': 'traffic-predictor',
        'containerDefinitions': [{
            'name': 'traffic-predictor',
            'image': f'{ecr.describe_repositories()["repositories"][0]["repositoryUri"]}:latest',
            'memory': 512,
            'cpu': 256,
            'essential': True,
            'portMappings': [{
                'containerPort': 8000,
                'hostPort': 8000,
                'protocol': 'tcp'
            }]
        }],
        'requiresCompatibilities': ['FARGATE'],
        'networkMode': 'awsvpc',
        'cpu': '256',
        'memory': '512'
    }

    ecs.register_task_definition(**task_def)

FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "8000"]

fastapi
uvicorn
boto3
pandas
numpy
scikit-learn

# Build and push Docker image
aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin $AWS_ACCOUNT_ID.dkr.ecr.us-east-1.amazonaws.com
docker build -t traffic-predictor .
docker tag traffic-predictor:latest $AWS_ACCOUNT_ID.dkr.ecr.us-east-1.amazonaws.com/traffic-predictor:latest
docker push $AWS_ACCOUNT_ID.dkr.ecr.us-east-1.amazonaws.com/traffic-predictor:latest

# Create infrastructure
python infrastructure.py

import streamlit as st
import requests
import plotly.graph_objects as go
import plotly.express as px

API_ENDPOINT = "your-api-endpoint"

def predict_traffic(input_data):
    response = requests.post(f"{API_ENDPOINT}/predict", json=input_data)
    return response.json()["congestion_level"]

# Rest of the Streamlit code remains the same, but replace direct model calls
# with API calls using predict_traffic()

curl -X POST "your-api-endpoint/predict" \
     -H "Content-Type: application/json" \
     -d '{"hour":12,"day":1,"temperature":25,"precipitation":0,"special_event":false,"road_work":false,"vehicle_count":1000}'