Introduced GPT/AI service, added dashboard widgets, drag and drop, export import.

services/gpt_service/main.py

@@ -1,10 +1,93 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel, Field
+from typing import List, Dict, Any, Literal
+from contextlib import asynccontextmanager
 from gpt_service import GPTService
+import logging
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+gpt_service = None
 
 
-def main():
-    GPTService()
-    print("Hello from gpt-service!")
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """Lifespan event handler for startup and shutdown."""
+    global gpt_service
+    # Startup
+    logger.info("Initializing GPT Service...")
+    gpt_service = GPTService()
+    logger.info("GPT Service initialized successfully")
+    yield
+    # Shutdown (cleanup if needed)
+    logger.info("Shutting down GPT Service...")
 
 
-if __name__ == "__main__":
-    main()
+app = FastAPI(lifespan=lifespan)
+
+
+class TelemetryDataPoint(BaseModel):
+    device_id: str
+    metric: str
+    value: float
+    timestamp: str
+
+class AnalyzeRequest(BaseModel):
+    telemetry_data: List[TelemetryDataPoint] = Field(..., description="Array of telemetry data points")
+    device_info: Dict[str, Any] = Field(default_factory=dict, description="Device metadata")
+    prompt_type: Literal["anomaly_detection", "trend_summary", "custom"] = Field(
+        default="trend_summary",
+        description="Type of analysis to perform"
+    )
+    custom_prompt: str | None = Field(None, description="Custom prompt for 'custom' type")
+
+class AnalyzeResponse(BaseModel):
+    analysis: str
+    prompt_type: str
+    data_points_analyzed: int
+
+
+@app.get("/health")
+async def health_check():
+    return {"status": "healthy", "service": "gpt_service"}
+
+
+@app.post("/analyze", response_model=AnalyzeResponse)
+async def analyze_telemetry(request: AnalyzeRequest):
+    """
+    Analyze telemetry data using GPT model.
+    Receives formatted telemetry data from Django and returns AI insights.
+    """
+    try:
+        if not gpt_service:
+            raise HTTPException(status_code=503, detail="GPT service not initialized")
+        
+        if not request.telemetry_data:
+            raise HTTPException(status_code=400, detail="No telemetry data provided")
+        
+        logger.info(f"Analyzing {len(request.telemetry_data)} telemetry points with prompt_type={request.prompt_type}")
+        
+        # Convert Pydantic models to dicts for GPTService
+        telemetry_dicts = [point.model_dump() for point in request.telemetry_data]
+        
+        # Call GPT service analysis
+        analysis_result = await gpt_service.analyze(
+            telemetry_data=telemetry_dicts,
+            device_info=request.device_info,
+            prompt_type=request.prompt_type,
+            custom_prompt=request.custom_prompt
+        )
+        
+        return AnalyzeResponse(
+            analysis=analysis_result,
+            prompt_type=request.prompt_type,
+            data_points_analyzed=len(request.telemetry_data)
+        )
+        
+    except ValueError as e:
+        logger.error(f"Validation error: {str(e)}")
+        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        logger.error(f"Analysis failed: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")