Add Civic Eye UI and robust camera fallbacks

backend/__main__.py

@@ -1,12 +1,13 @@
 """
 Entry point for running the backend as a module.
-This allows running: 
+This allows running:
   - From root: python -m backend
   - From backend: python -m __main__
 
 This will start the FastAPI application with uvicorn,
 which includes the Telegram bot via the lifespan context manager.
 """
+
 import os
 import sys
 import uvicorn
@@ -15,7 +16,7 @@
     # Get the port from environment variable (Render provides PORT)
     port = int(os.environ.get("PORT", 8000))
     host = os.environ.get("HOST", "0.0.0.0")
-    
+
     # Determine the correct module path based on where we're running from
     # If we're in the backend directory, use "main:app"
     # If we're in the root directory, use "backend.main:app"
@@ -24,11 +25,6 @@
         app_module = "main:app"
     else:
         app_module = "backend.main:app"
-    
+
     # Run uvicorn
-    uvicorn.run(
-        app_module,
-        host=host,
-        port=port,
-        log_level="info"
-    )
+    uvicorn.run(app_module, host=host, port=port, log_level="info")

backend/adaptive_weights.py

@@ -6,7 +6,8 @@
 
 logger = logging.getLogger(__name__)
 
-DATA_FILE = os.path.join(os.path.dirname(__file__), 'data', 'modelWeights.json')
+DATA_FILE = os.path.join(os.path.dirname(__file__), "data", "modelWeights.json")
+
 
 class AdaptiveWeights:
     _instance = None
@@ -32,7 +33,7 @@ def _load_weights(self):
 
             mtime = os.path.getmtime(DATA_FILE)
             if self._weights is None or mtime > self._last_loaded:
-                with open(DATA_FILE, 'r') as f:
+                with open(DATA_FILE, "r") as f:
                     self._weights = json.load(f)
                 self._last_loaded = mtime
                 self._reload_count += 1
@@ -58,7 +59,7 @@ def reload_count(self) -> int:
 
     def _save_weights(self):
         try:
-            with open(DATA_FILE, 'w') as f:
+            with open(DATA_FILE, "w") as f:
                 json.dump(self._weights, f, indent=2)
             # Update last loaded to avoid immediate reload
             self._last_loaded = os.path.getmtime(DATA_FILE)
@@ -67,31 +68,31 @@ def _save_weights(self):
 
     def get_severity_keywords(self) -> Dict[str, List[str]]:
         self._check_reload()
-        return self._weights.get('severity_keywords', {})
+        return self._weights.get("severity_keywords", {})
 
     def get_urgency_patterns(self) -> List[List[Any]]:
         self._check_reload()
-        return self._weights.get('urgency_patterns', [])
+        return self._weights.get("urgency_patterns", [])
 
     def get_category_keywords(self) -> Dict[str, List[str]]:
         self._check_reload()
-        return self._weights.get('category_keywords', {})
+        return self._weights.get("category_keywords", {})
 
     def get_category_multipliers(self) -> Dict[str, float]:
         self._check_reload()
-        return self._weights.get('category_multipliers', {})
+        return self._weights.get("category_multipliers", {})
 
     def get_duplicate_search_radius(self) -> float:
         self._check_reload()
-        return self._weights.get('duplicate_search_radius', 50.0)
+        return self._weights.get("duplicate_search_radius", 50.0)
 
     def update_category_weight(self, category: str, factor: float):
         """
         Updates the multiplier for a category.
         Factor should be slightly > 1.0 to increase severity, or < 1.0 to decrease.
         """
-        self._check_reload() # Ensure we have latest
-        multipliers = self._weights.get('category_multipliers', {})
+        self._check_reload()  # Ensure we have latest
+        multipliers = self._weights.get("category_multipliers", {})
         current = multipliers.get(category, 1.0)
 
         # Apply factor
@@ -101,22 +102,27 @@ def update_category_weight(self, category: str, factor: float):
         new_weight = max(0.5, min(3.0, new_weight))
 
         multipliers[category] = new_weight
-        self._weights['category_multipliers'] = multipliers
+        self._weights["category_multipliers"] = multipliers
 
-        self._weights['last_updated'] = time.strftime('%Y-%m-%dT%H:%M:%SZ', time.gmtime())
+        self._weights["last_updated"] = time.strftime(
+            "%Y-%m-%dT%H:%M:%SZ", time.gmtime()
+        )
         self._save_weights()
         logger.info(f"Updated weight for {category} to {new_weight:.2f}")
 
     def update_duplicate_radius(self, factor: float):
         self._check_reload()
-        current = self._weights.get('duplicate_search_radius', 50.0)
+        current = self._weights.get("duplicate_search_radius", 50.0)
         new_radius = current * factor
         # Clamp (10m to 200m)
         new_radius = max(10.0, min(200.0, new_radius))
 
-        self._weights['duplicate_search_radius'] = new_radius
-        self._weights['last_updated'] = time.strftime('%Y-%m-%dT%H:%M:%SZ', time.gmtime())
+        self._weights["duplicate_search_radius"] = new_radius
+        self._weights["last_updated"] = time.strftime(
+            "%Y-%m-%dT%H:%M:%SZ", time.gmtime()
+        )
         self._save_weights()
         logger.info(f"Updated duplicate search radius to {new_radius:.1f}m")
 
+
 adaptive_weights = AdaptiveWeights()

backend/ai_factory.py

@@ -6,6 +6,7 @@
 
 Fallback chain:  gemini → huggingface → mock
 """
+
 import os
 from typing import Literal
 

backend/ai_interfaces.py

@@ -4,6 +4,7 @@
 This module defines abstract interfaces for AI services to reduce tight coupling
 and enable easier testing, mocking, and service provider switching.
 """
+
 from abc import ABC, abstractmethod
 from typing import Dict, Optional, Protocol
 import asyncio
@@ -16,8 +17,8 @@ async def generate_action_plan(
         self,
         issue_description: str,
         category: str,
-        language: str = 'en',
-        image_path: Optional[str] = None
+        language: str = "en",
+        image_path: Optional[str] = None,
     ) -> Dict[str, str]:
         """
         Generate action plan with WhatsApp message and email draft.
@@ -57,7 +58,7 @@ async def generate_mla_summary(
         district: str,
         assembly_constituency: str,
         mla_name: str,
-        issue_category: Optional[str] = None
+        issue_category: Optional[str] = None,
     ) -> str:
         """
         Generate a human-readable summary about an MLA.
@@ -81,7 +82,7 @@ def __init__(
         self,
         action_plan_service: ActionPlanService,
         chat_service: ChatService,
-        mla_summary_service: MLASummaryService
+        mla_summary_service: MLASummaryService,
     ):
         self.action_plan_service = action_plan_service
         self.chat_service = chat_service
@@ -95,19 +96,21 @@ def __init__(
 def get_ai_services() -> AIServiceContainer:
     """Get the global AI services container."""
     if _ai_services is None:
-        raise RuntimeError("AI services not initialized. Call initialize_ai_services() first.")
+        raise RuntimeError(
+            "AI services not initialized. Call initialize_ai_services() first."
+        )
     return _ai_services
 
 
 def initialize_ai_services(
     action_plan_service: ActionPlanService,
     chat_service: ChatService,
-    mla_summary_service: MLASummaryService
+    mla_summary_service: MLASummaryService,
 ) -> None:
     """Initialize the global AI services container."""
     global _ai_services
     _ai_services = AIServiceContainer(
         action_plan_service=action_plan_service,
         chat_service=chat_service,
-        mla_summary_service=mla_summary_service
+        mla_summary_service=mla_summary_service,
     )

backend/ai_service.py

@@ -24,11 +24,14 @@
     # Allow dummy key for testing/building if not strictly required at startup
     api_key = "dummy"
     if os.environ.get("ENVIRONMENT") == "production":
-         logger.warning("GEMINI_API_KEY not set in production environment!")
+        logger.warning("GEMINI_API_KEY not set in production environment!")
 
 genai.configure(api_key=api_key)
 
-RESPONSIBILITY_MAP_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "data", "responsibility_map.json")
+RESPONSIBILITY_MAP_PATH = os.path.join(
+    os.path.dirname(os.path.dirname(__file__)), "data", "responsibility_map.json"
+)
+
 
 async def retry_with_exponential_backoff(
     func: Callable,
@@ -37,7 +40,7 @@ async def retry_with_exponential_backoff(
     max_delay: float = 60.0,
     backoff_factor: float = 2.0,
     *args,
-    **kwargs
+    **kwargs,
 ) -> Any:
     """
     Retry an async function with exponential backoff.
@@ -66,17 +69,21 @@ async def retry_with_exponential_backoff(
 
             if attempt == max_retries:
                 # Last attempt failed, re-raise the exception
-                logger.error(f"Function {func.__name__} failed after {max_retries + 1} attempts: {e}")
+                logger.error(
+                    f"Function {func.__name__} failed after {max_retries + 1} attempts: {e}"
+                )
                 raise AIServiceException(
                     f"AI service operation failed after {max_retries + 1} attempts",
                     service="Gemini",
-                    details={"function": func.__name__, "error": str(e)}
+                    details={"function": func.__name__, "error": str(e)},
                 ) from e
 
             # Calculate delay with exponential backoff
-            delay = min(base_delay * (backoff_factor ** attempt), max_delay)
+            delay = min(base_delay * (backoff_factor**attempt), max_delay)
 
-            logger.warning(f"Function {func.__name__} failed (attempt {attempt + 1}/{max_retries + 1}): {e}. Retrying in {delay:.1f}s")
+            logger.warning(
+                f"Function {func.__name__} failed (attempt {attempt + 1}/{max_retries + 1}): {e}. Retrying in {delay:.1f}s"
+            )
             await asyncio.sleep(delay)
 
     # This should never be reached, but just in case
@@ -108,7 +115,12 @@ def build_x_post(issue_description: str, category: str) -> str:
     return f"{base_message} #CivicIssue #VishwaGuru"
 
 
-async def generate_action_plan(issue_description: str, category: str, language: str = 'en', image_path: Optional[str] = None) -> dict:
+async def generate_action_plan(
+    issue_description: str,
+    category: str,
+    language: str = "en",
+    image_path: Optional[str] = None,
+) -> dict:
     """
     Generates an action plan (WhatsApp message, Email draft) using Gemini with retry logic.
     """
@@ -120,12 +132,12 @@ async def generate_action_plan(issue_description: str, category: str, language:
         "whatsapp": f"Hello, I would like to report a {category} issue: {issue_description}",
         "email_subject": f"Complaint regarding {category}",
         "email_body": f"Respected Authority,\n\nI am writing to bring to your attention a {category} issue: {issue_description}.\n\nPlease take necessary action.\n\nSincerely,\nCitizen",
-        "x_post": x_post_text
+        "x_post": x_post_text,
     }
 
     async def _generate_with_gemini() -> dict:
         """Inner function to generate action plan with Gemini"""
-        model = genai.GenerativeModel('gemini-1.5-flash')
+        model = genai.GenerativeModel("gemini-1.5-flash")
 
         prompt = f"""
         You are a civic action assistant. A user has reported a civic issue.
@@ -147,9 +159,9 @@ async def _generate_with_gemini() -> dict:
 
         # Cleanup if markdown code blocks are returned
         if "```json" in text_response:
-             text_response = text_response.split("```json")[1].split("```")[0]
+            text_response = text_response.split("```json")[1].split("```")[0]
         elif "```" in text_response:
-             text_response = text_response.split("```")[1].split("```")[0]
+            text_response = text_response.split("```")[1].split("```")[0]
 
         text_response = text_response.strip()
 
@@ -165,7 +177,9 @@ async def _generate_with_gemini() -> dict:
         return plan
 
     try:
-        return await retry_with_exponential_backoff(_generate_with_gemini, max_retries=3)
+        return await retry_with_exponential_backoff(
+            _generate_with_gemini, max_retries=3
+        )
     except AIServiceException:
         # Already properly wrapped, re-raise
         raise
@@ -174,14 +188,16 @@ async def _generate_with_gemini() -> dict:
         raise AIServiceException(
             "Failed to generate action plan",
             service="Gemini",
-            details={"error": str(e)}
+            details={"error": str(e)},
         ) from e
 
+
 # Manual cache for chat
 _chat_cache = {}
-CHAT_CACHE_TTL = 3600 # 1 hour
+CHAT_CACHE_TTL = 3600  # 1 hour
 MAX_CHAT_CACHE_SIZE = 100
 
+
 async def chat_with_civic_assistant(query: str) -> str:
     """
     Chat with the civic assistant using Gemini with retry logic.
@@ -199,7 +215,7 @@ async def chat_with_civic_assistant(query: str) -> str:
 
     async def _chat_with_gemini() -> str:
         """Inner function to chat with Gemini"""
-        model = genai.GenerativeModel('gemini-1.5-flash')
+        model = genai.GenerativeModel("gemini-1.5-flash")
 
         prompt = f"""
         You are VishwaGuru, a helpful civic assistant for Indian citizens.
@@ -219,7 +235,7 @@ async def _chat_with_gemini() -> str:
         # Update cache
         if len(_chat_cache) > MAX_CHAT_CACHE_SIZE:
             # Prune oldest 20%
-            keys_to_remove = list(_chat_cache.keys())[:int(MAX_CHAT_CACHE_SIZE * 0.2)]
+            keys_to_remove = list(_chat_cache.keys())[: int(MAX_CHAT_CACHE_SIZE * 0.2)]
             for k in keys_to_remove:
                 del _chat_cache[k]
 
@@ -234,5 +250,5 @@ async def _chat_with_gemini() -> str:
         raise AIServiceException(
             "Failed to process chat request",
             service="Gemini",
-            details={"error": str(e)}
+            details={"error": str(e)},
         ) from e