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369ed63 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | import boto3
import json
import os
from dotenv import load_dotenv
from models import Priority, Category
load_dotenv()
_bedrock = None
def get_bedrock_client():
global _bedrock
if _bedrock is None:
_bedrock = boto3.client(
"bedrock-runtime",
region_name=os.getenv("AWS_REGION", "us-east-1"),
)
return _bedrock
def invoke_model(prompt: str) -> str:
model_id = os.getenv("BEDROCK_MODEL_ID", "amazon.titan-text-express-v1")
body = json.dumps({
"inputText": prompt,
"textGenerationConfig": {
"maxTokenCount": 512,
"temperature": 0.3,
"topP": 0.9,
},
})
try:
response = get_bedrock_client().invoke_model(modelId=model_id, body=body)
result = json.loads(response["body"].read())
return result["results"][0]["outputText"].strip()
except Exception as e:
return f"AI service unavailable: {str(e)}"
def classify_ticket(subject: str, description: str) -> dict:
prompt = f"""Analyze this customer support ticket and respond ONLY with a JSON object.
Subject: {subject}
Description: {description}
Respond with exactly this JSON format (no extra text):
{{
"category": "<one of: billing, technical, account, shipping, general>",
"priority": "<one of: low, medium, high, critical>",
"confidence": <float between 0.0 and 1.0>
}}"""
raw = invoke_model(prompt)
try:
start = raw.find("{")
end = raw.rfind("}") + 1
data = json.loads(raw[start:end])
return {
"category": Category(data.get("category", "general")),
"priority": Priority(data.get("priority", "medium")),
"confidence": float(data.get("confidence", 0.7)),
}
except Exception:
return {"category": Category.general, "priority": Priority.medium, "confidence": 0.5}
def generate_resolution(subject: str, description: str, category: str) -> str:
prompt = f"""You are a helpful customer support AI. Provide a clear, concise resolution for this ticket.
Category: {category}
Subject: {subject}
Issue: {description}
Write a professional response (2-4 sentences) that directly addresses the customer's issue with actionable steps."""
return invoke_model(prompt)
def analyze_ticket(subject: str, description: str) -> dict:
classification = classify_ticket(subject, description)
resolution = generate_resolution(subject, description, classification["category"])
return {**classification, "ai_resolution": resolution}
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