Merge branch 'main' of https://github.com/DevelopedBy-Siva/flow-pilot

.env

@@ -1,7 +0,0 @@
-AI_PROVIDER=vertex_ai
-ALLOW_ORIGINS=*
-GMAIL_POLL_SECONDS=30
-VERTEX_PROJECT_ID=flow-pilot-493104
-VERTEX_LOCATION=us-central1
-VERTEX_MODEL=gemini-2.5-pro
-GOOGLE_APPLICATION_CREDENTIALS=

.env.example

@@ -1,7 +0,0 @@
-AI_PROVIDER=vertex_ai
-ALLOW_ORIGINS=*
-GMAIL_POLL_SECONDS=30
-VERTEX_PROJECT_ID=your-gcp-project-id
-VERTEX_LOCATION=us-central1
-VERTEX_MODEL=gemini-2.5-pro
-GOOGLE_APPLICATION_CREDENTIALS=

.github/workflows/deploy.yml

@@ -58,4 +58,4 @@ jobs:
           image: ${{ env.REGION }}-docker.pkg.dev/${{ env.PROJECT_ID }}/${{ env.REPOSITORY }}/${{ env.IMAGE }}:${{ github.sha }}
           flags: >-
             --allow-unauthenticated
-            --set-env-vars=AI_PROVIDER=vertex_ai,VERTEX_PROJECT_ID=${{ env.PROJECT_ID }},VERTEX_LOCATION=${{ env.REGION }},VERTEX_MODEL=gemini-2.5-pro
+            --set-env-vars=AI_PROVIDER=vertex_ai,VERTEX_PROJECT_ID=${{ env.PROJECT_ID }},VERTEX_LOCATION=${{ env.REGION }},VERTEX_MODEL=gemini-2.5-flash

.gitignore

@@ -0,0 +1,23 @@
+.env
+.env.local
+.venv/
+venv/
+__pycache__/
+.pytest_cache/
+.mypy_cache/
+.ruff_cache/
+*.pyc
+*.pyo
+*.pyd
+
+dist/
+build/
+*.egg-info/
+
+.DS_Store
+.idea/
+.vscode/
+
+flowpilot-vertex-key.json
+*.pem
+*.key

README.md

@@ -74,5 +74,6 @@ The workflow builds the Docker image, pushes it to Artifact Registry, and deploy
 - Fill in your real Vertex values in `.env`, especially `VERTEX_PROJECT_ID` and `VERTEX_LOCATION`.
 - If you authenticate with `gcloud auth application-default login`, leave `GOOGLE_APPLICATION_CREDENTIALS` blank.
 - Only set `GOOGLE_APPLICATION_CREDENTIALS` when you have a real service account JSON path available.
+- The default model is `gemini-2.5-flash` for better latency and cost during iteration.
 - The backend now attempts live Vertex AI calls through the `google-genai` SDK when credentials are configured.
 - If the SDK is missing or `VERTEX_PROJECT_ID` is still a placeholder, FlowPilot falls back to local deterministic mock logic so tests and scaffolding still work.

backend/api/routes.py

@@ -1,3 +1,5 @@
+import logging
+
 from fastapi import APIRouter, HTTPException
 
 from backend.ai.analyzer import analyze_business_description, analyze_custom_task
@@ -24,11 +26,16 @@ from backend.storage.database import db
 
 router = APIRouter()
 executor = WorkflowExecutor()
+logger = logging.getLogger(__name__)
 
 
 @router.post("/analyze")
 def analyze(request: AnalyzeRequest) -> dict:
-    analysis = analyze_business_description(request.description)
+    try:
+        analysis = analyze_business_description(request.description)
+    except Exception as exc:
+        logger.exception("Analyze request failed")
+        raise HTTPException(status_code=500, detail=f"Analyze failed: {exc}") from exc
     owner = db.ensure_owner(request.owner_id, request.owner_email)
     owner["business_description"] = request.description
     owner["business_analysis"] = analysis
@@ -39,30 +46,42 @@ def analyze(request: AnalyzeRequest) -> dict:
 @router.post("/custom-task")
 def custom_task(request: CustomTaskRequest) -> dict:
     owner = db.get_owner(request.owner_id)
-    return analyze_custom_task(
-        business_description=owner.get("business_description", ""),
-        existing_workflows=db.list_workflows(request.owner_id),
-        custom_task=request.custom_task,
-    )
+    try:
+        return analyze_custom_task(
+            business_description=owner.get("business_description", ""),
+            existing_workflows=db.list_workflows(request.owner_id),
+            custom_task=request.custom_task,
+        )
+    except Exception as exc:
+        logger.exception("Custom task analysis failed")
+        raise HTTPException(status_code=500, detail=f"Custom task analysis failed: {exc}") from exc
 
 
 @router.post("/suggest-workflows")
 def suggest_workflows(request: WorkflowSuggestionRequest) -> dict:
     owner = db.get_owner(request.owner_id)
-    return suggest_workflow_options(
-        task_name=request.task_name,
-        task_description=request.task_description,
-        category=request.category,
-        spreadsheet_info=owner.get("spreadsheet_config", {"connected": False}),
-        uploaded_files=db.list_data_files(request.owner_id),
-    )
+    try:
+        return suggest_workflow_options(
+            task_name=request.task_name,
+            task_description=request.task_description,
+            category=request.category,
+            spreadsheet_info=owner.get("spreadsheet_config", {"connected": False}),
+            uploaded_files=db.list_data_files(request.owner_id),
+        )
+    except Exception as exc:
+        logger.exception("Workflow suggestion failed")
+        raise HTTPException(status_code=500, detail=f"Workflow suggestion failed: {exc}") from exc
 
 
 @router.post("/build-workflow", response_model=BuildWorkflowResponse)
 def build_workflow(request: BuildWorkflowRequest) -> BuildWorkflowResponse:
     owner = db.get_owner(request.owner_id)
-    workflow_json = build_workflow_definition(request=request, owner=owner)
-    compiled = compile_workflow(workflow_json)
+    try:
+        workflow_json = build_workflow_definition(request=request, owner=owner)
+        compiled = compile_workflow(workflow_json)
+    except Exception as exc:
+        logger.exception("Workflow build failed")
+        raise HTTPException(status_code=500, detail=f"Workflow build failed: {exc}") from exc
     return BuildWorkflowResponse(workflow=compiled)
 
 
@@ -125,3 +144,15 @@ def simulate_run(owner_id: str, workflow_id: str, trigger: dict) -> dict:
     result = executor.execute(workflow, trigger, db=db, owner_id=owner_id)
     db.save_execution_log(owner_id, workflow_id, trigger, result["steps"], result["outcome"], result.get("error"))
     return result
+
+
+@router.get("/debug/vertex")
+def debug_vertex() -> dict:
+    from backend.ai.client import vertex_client
+
+    try:
+        text = vertex_client.generate_text("Reply with exactly: Vertex debug ok")
+        return {"status": "ok", "response": text}
+    except Exception as exc:
+        logger.exception("Vertex debug call failed")
+        raise HTTPException(status_code=500, detail=f"Vertex debug failed: {exc}") from exc

backend/config.py

@@ -16,7 +16,7 @@ class Settings(BaseModel):
     gmail_poll_seconds: int = int(os.getenv("GMAIL_POLL_SECONDS", "30"))
     vertex_project_id: str = os.getenv("VERTEX_PROJECT_ID", "your-gcp-project-id")
     vertex_location: str = os.getenv("VERTEX_LOCATION", "us-central1")
-    vertex_model: str = os.getenv("VERTEX_MODEL", "gemini-2.5-pro")
+    vertex_model: str = os.getenv("VERTEX_MODEL", "gemini-2.5-flash")
     google_application_credentials: str = os.getenv(
         "GOOGLE_APPLICATION_CREDENTIALS",
         "",