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Start fixing forecast service API 7

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This commit is contained in:
Urtzi Alfaro
2025-07-29 17:50:01 +02:00
parent 30bc3db4fa
commit fe88b696c2
3 changed files with 73 additions and 38 deletions
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13
services/forecasting/app/api/forecasts.py
View File

@@ -33,26 +33,19 @@ forecasting_service = ForecastingService()
async def create_single_forecast(
request: ForecastRequest,
db: AsyncSession = Depends(get_db),
tenant_id: str = Path(..., description="Tenant ID"),
current_user: dict = Depends(get_current_user_dep)
tenant_id: str = Path(..., description="Tenant ID")
):
"""Generate a single product forecast"""
try:
# Verify tenant access
if str(request.tenant_id) != tenant_id:
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="Access denied to this tenant"
)
# Generate forecast
forecast = await forecasting_service.generate_forecast(request, db)
forecast = await forecasting_service.generate_forecast(tenant_id, request, db)
# Convert to response model
return ForecastResponse(
id=str(forecast.id),
tenant_id=str(forecast.tenant_id),
tenant_id=tenant_id,
product_name=forecast.product_name,
location=forecast.location,
forecast_date=forecast.forecast_date,

16
services/forecasting/app/services/forecasting_service.py
View File

@@ -38,19 +38,19 @@ class ForecastingService:
self.model_client = ModelClient()
self.data_client = DataClient()
async def generate_forecast(self, request: ForecastRequest, db: AsyncSession) -> Forecast:
async def generate_forecast(self, tenant_id: str, request: ForecastRequest, db: AsyncSession) -> Forecast:
"""Generate a single forecast for a product"""
start_time = datetime.now()
try:
logger.info("Generating forecast",
tenant_id=request.tenant_id,
tenant_id=tenant_id,
product=request.product_name,
date=request.forecast_date)
# Get the latest trained model for this tenant/product
model_info = await self._get_latest_model(
request.tenant_id,
tenant_id,
request.product_name,
)
@@ -58,7 +58,7 @@ class ForecastingService:
raise ValueError(f"No trained model found for {request.product_name}")
# Prepare features for prediction
features = await self._prepare_forecast_features(request)
features = await self._prepare_forecast_features(tenant_id, request)
# Generate prediction using ML service
prediction_result = await self.prediction_service.predict(
@@ -69,7 +69,7 @@ class ForecastingService:
# Create forecast record
forecast = Forecast(
tenant_id=uuid.UUID(request.tenant_id),
tenant_id=uuid.UUID(tenant_id),
product_name=request.product_name,
forecast_date=datetime.combine(request.forecast_date, datetime.min.time()),
@@ -115,7 +115,7 @@ class ForecastingService:
# Publish event
await publish_forecast_completed({
"forecast_id": str(forecast.id),
"tenant_id": request.tenant_id,
"tenant_id": tenant_id,
"product_name": request.product_name,
"predicted_demand": forecast.predicted_demand
})
@@ -256,7 +256,7 @@ class ForecastingService:
logger.error("Error getting latest model", error=str(e))
raise
async def _prepare_forecast_features(self, request: ForecastRequest) -> Dict[str, Any]:
async def _prepare_forecast_features(self, tenant_id: str, request: ForecastRequest) -> Dict[str, Any]:
"""Prepare features for forecasting model"""
features = {
@@ -269,7 +269,7 @@ class ForecastingService:
features["is_holiday"] = await self._is_spanish_holiday(request.forecast_date)
weather_data = await self._get_weather_forecast(request.tenant_id, 1)
weather_data = await self._get_weather_forecast(tenant_id, 1)
features.update(weather_data)
return features

78
tests/test_onboarding_flow.sh
View File

@@ -563,31 +563,73 @@ echo ""
# STEP 5: ONBOARDING COMPLETION (DASHBOARD ACCESS)
# =================================================================
echo -e "${STEP_ICONS[4]} ${PURPLE}STEP 5: ONBOARDING COMPLETION${NC}"
echo "Simulating completion and dashboard access"
echo ""
log_step "5.1. Testing basic dashboard functionality"
# Use a real product name from our CSV for forecasting
FIRST_PRODUCT=$(echo "$REAL_PRODUCTS" | sed 's/"//g' | cut -d',' -f1)
# Get a real product name from CSV (fix the product extraction)
FIRST_PRODUCT=$(head -2 "$REAL_CSV_FILE" | tail -1 | cut -d',' -f3 | sed 's/"//g' | head -1)
FORECAST_RESPONSE=$(curl -s -X POST "$API_BASE/api/v1/tenants/$TENANT_ID/forecasts/single" \
if [ -z "$FIRST_PRODUCT" ]; then
FIRST_PRODUCT="Pan Integral" # Fallback product name
log_warning "Could not extract product from CSV, using fallback: $FIRST_PRODUCT"
else
log_success "Using product from CSV: $FIRST_PRODUCT"
fi
# CORRECTED forecast request with proper schema
FORECAST_REQUEST="{
\"product_name\": \"$FIRST_PRODUCT\",
\"forecast_date\": \"2025-07-30\",
\"forecast_days\": 1,
\"location\": \"madrid_centro\",
\"confidence_level\": 0.85
}"
echo "Forecast Request:"
echo "$FORECAST_REQUEST" | python3 -m json.tool
# Make the API call
FORECAST_RESPONSE=$(curl -s -w "\nHTTP_CODE:%{http_code}" -X POST "$API_BASE/api/v1/tenants/$TENANT_ID/forecasts/single" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $ACCESS_TOKEN" \
-d "{
\"product_name\": [\"$FIRST_PRODUCT\"],
\"forecast_date\": "2025-11-30",
\"forecast_days\": 1,
\"date\": \"2025-09-15\",
\"location\": \"madrid_centro\",
\"confidence_level\": 0.85
}")
-d "$FORECAST_REQUEST")
if echo "$FORECAST_RESPONSE" | grep -q '"predictions"\|"forecast"'; then
log_success "Forecasting service is accessible"
# Extract HTTP code and response
HTTP_CODE=$(echo "$FORECAST_RESPONSE" | grep "HTTP_CODE:" | cut -d: -f2)
FORECAST_RESPONSE=$(echo "$FORECAST_RESPONSE" | sed '/HTTP_CODE:/d')
echo "Forecast HTTP Status: $HTTP_CODE"
echo "Forecast Response:"
echo "$FORECAST_RESPONSE" | python3 -m json.tool 2>/dev/null || echo "$FORECAST_RESPONSE"
# Validate response
if [ "$HTTP_CODE" = "200" ]; then
if echo "$FORECAST_RESPONSE" | grep -q '"predicted_demand"\|"id"'; then
log_success "Forecasting service is working correctly"
# Extract key values for validation
PREDICTED_DEMAND=$(extract_json_field "$FORECAST_RESPONSE" "predicted_demand")
CONFIDENCE_LOWER=$(extract_json_field "$FORECAST_RESPONSE" "confidence_lower")
CONFIDENCE_UPPER=$(extract_json_field "$FORECAST_RESPONSE" "confidence_upper")
if [ -n "$PREDICTED_DEMAND" ]; then
echo " Predicted Demand: $PREDICTED_DEMAND"
echo " Confidence Range: [$CONFIDENCE_LOWER, $CONFIDENCE_UPPER]"
fi
else
log_error "Forecast response missing expected fields"
echo "Response: $FORECAST_RESPONSE"
fi
elif [ "$HTTP_CODE" = "422" ]; then
log_error "Forecast request validation failed"
echo "Validation errors: $FORECAST_RESPONSE"
elif [ "$HTTP_CODE" = "404" ]; then
log_warning "Forecast endpoint not found - check API routing"
elif [ "$HTTP_CODE" = "500" ]; then
log_error "Internal server error in forecasting service"
echo "Error details: $FORECAST_RESPONSE"
else
log_warning "Forecasting may not be ready yet (model training required)"
log_warning "Forecasting may not be ready yet (HTTP $HTTP_CODE)"
echo "Response: $FORECAST_RESPONSE"
fi
echo ""