Fix issues, optimize data flow and AI service robustness

backend/ai_service.py

@@ -21,10 +21,11 @@
 )
 
 # Configure Gemini
-# Use provided key as fallback if env var is missing
-api_key = os.environ.get("GEMINI_API_KEY", "AIzaSyB8_i3tbDE3GmX4CsQ8G3mD3pB2WrHi5C8")
+api_key = os.environ.get("GEMINI_API_KEY")
 if api_key:
     genai.configure(api_key=api_key)
+else:
+    logger.warning("GEMINI_API_KEY not set. AI services will be disabled.")
 
 
 @lru_cache(maxsize=1)
@@ -98,10 +99,21 @@ async def generate_action_plan(issue_description: str, category: str, image_path
 
         try:
             plan = json.loads(text_response)
-        except json.JSONDecodeError:
+        except json.JSONDecodeError as e:
             # Try to fix common JSON errors if possible, or fallback
-            logger.error(f"Gemini returned invalid JSON: {text_response}")
-            raise Exception("Invalid JSON from AI")
+            logger.error(f"Gemini returned invalid JSON: {text_response} | Error: {e}")
+
+            # Last ditch effort: Try to find the first { and last }
+            try:
+                start_idx = text_response.find('{')
+                end_idx = text_response.rfind('}')
+                if start_idx != -1 and end_idx != -1:
+                    potential_json = text_response[start_idx:end_idx+1]
+                    plan = json.loads(potential_json)
+                else:
+                    raise Exception("No JSON object found in response")
+            except Exception:
+                 raise Exception("Invalid JSON from AI")
 
         if "x_post" not in plan or not plan.get("x_post"):
             plan["x_post"] = x_post

backend/gemini_summary.py

@@ -19,8 +19,9 @@
 # Configure Gemini (mandatory environment variable)
 api_key = os.environ.get("GEMINI_API_KEY")
 if not api_key:
-    raise ValueError("GEMINI_API_KEY environment variable is required but not set. Please set it in your environment variables.")
-genai.configure(api_key=api_key)
+    logger.warning("GEMINI_API_KEY environment variable is not set. AI summaries will be disabled.")
+else:
+    genai.configure(api_key=api_key)
 
 
 def _get_fallback_summary(mla_name: str, assembly_constituency: str, district: str) -> str:

backend/main.py

@@ -14,7 +14,6 @@
     load_maharashtra_pincode_data,
     load_maharashtra_mla_data
 )
-from pydantic import BaseModel
 import json
 import os
 import shutil
@@ -87,9 +86,9 @@ async def lifespan(app: FastAPI):
         # These functions use lru_cache, so calling them once loads the data into memory
         load_maharashtra_pincode_data()
         load_maharashtra_mla_data()
-        print("Maharashtra data pre-loaded successfully.")
+        logger.info("Maharashtra data pre-loaded successfully.")
     except Exception as e:
-        print(f"Error pre-loading Maharashtra data: {e}")
+        logger.error(f"Error pre-loading Maharashtra data: {e}", exc_info=True)
 
     # Startup: Start Telegram Bot in background (non-blocking)
     bot_task = None
@@ -101,7 +100,7 @@ async def start_bot_background():
         try:
             bot_app = await run_bot()
         except Exception as e:
-            print(f"Error starting bot: {e}")
+            logger.error(f"Error starting bot: {e}", exc_info=True)
 
     # Create background task for bot initialization
     bot_task = asyncio.create_task(start_bot_background())
@@ -307,32 +306,36 @@ def get_recent_issues(db: Session = Depends(get_db)):
     # Convert to Pydantic models for validation and serialization
     data = []
     for i in issues:
-        # Handle action_plan JSON string
-        action_plan_val = i.action_plan
-        if isinstance(action_plan_val, str) and action_plan_val:
-            try:
-                action_plan_val = json.loads(action_plan_val)
-            except json.JSONDecodeError:
-                pass # Keep as string if not valid JSON
-
-        data.append(IssueResponse(
-            id=i.id,
-            category=i.category,
-            description=i.description[:100] + "..." if len(i.description) > 100 else i.description,
-            created_at=i.created_at,
-            image_path=i.image_path,
-            status=i.status,
-            upvotes=i.upvotes if i.upvotes is not None else 0,
-            location=i.location,
-            latitude=i.latitude,
-            longitude=i.longitude,
-            action_plan=action_plan_val
-        ).model_dump(mode='json')) # Store as JSON-compatible dict in cache
+        data.append(_serialize_issue(i))
 
     recent_issues_cache.set(data)
 
     return data
 
+def _serialize_issue(i: Issue) -> dict:
+    """Helper to serialize an Issue object to a dictionary."""
+    # Handle action_plan JSON string
+    action_plan_val = i.action_plan
+    if isinstance(action_plan_val, str) and action_plan_val:
+        try:
+            action_plan_val = json.loads(action_plan_val)
+        except json.JSONDecodeError:
+            pass # Keep as string if not valid JSON
+
+    return IssueResponse(
+        id=i.id,
+        category=i.category,
+        description=i.description[:100] + "..." if len(i.description) > 100 else i.description,
+        created_at=i.created_at,
+        image_path=i.image_path,
+        status=i.status,
+        upvotes=i.upvotes if i.upvotes is not None else 0,
+        location=i.location,
+        latitude=i.latitude,
+        longitude=i.longitude,
+        action_plan=action_plan_val
+    ).model_dump(mode='json')
+
 @app.post("/api/detect-pothole")
 async def detect_pothole_endpoint(image: UploadFile = File(...)):
     # Convert to PIL Image directly from file object to save memory
@@ -361,7 +364,6 @@ async def detect_infrastructure_endpoint(request: Request, image: UploadFile = F
 
     # Run detection using unified service (local ML by default)
     try:
-        detections = await detect_infrastructure(pil_image)
         # Use shared HTTP client from app state
         client = request.app.state.http_client
         detections = await detect_infrastructure_clip(image_bytes, client=client)
@@ -381,7 +383,6 @@ async def detect_flooding_endpoint(request: Request, image: UploadFile = File(..
 
     # Run detection using unified service (local ML by default)
     try:
-        detections = await detect_flooding(pil_image)
         # Use shared HTTP client from app state
         client = request.app.state.http_client
         detections = await detect_flooding_clip(image_bytes, client=client)
@@ -401,7 +402,6 @@ async def detect_vandalism_endpoint(request: Request, image: UploadFile = File(.
 
     # Run detection using unified service (local ML by default)
     try:
-        detections = await detect_vandalism(pil_image)
         # Use shared HTTP client from app state
         client = request.app.state.http_client
         detections = await detect_vandalism_clip(image_bytes, client=client)

backend/schemas.py

@@ -22,7 +22,7 @@ class IssueResponse(BaseModel):
     location: Optional[str] = None
     latitude: Optional[float] = None
     longitude: Optional[float] = None
-    action_plan: Optional[Any] = None
+    action_plan: Optional[ActionPlan | dict] = None
 
     model_config = ConfigDict(from_attributes=True)