Update flask_Character.py

flask_Character.py

@@ -1,3 +1,6 @@
+# This software is licensed under a **dual-license model**
+# For individuals and businesses earning **under $1M per year**, this software is licensed under the **MIT License**
+# Businesses or organizations with **annual revenue of $1,000,000 or more** must obtain permission to use this software commercially.
 import os
 # NUMBA_CACHE_DIR and NUMBA_DISABLE_CACHE are often set for specific environments,
 # e.g., if you're experiencing issues with Numba's caching behavior or in containerized environments.
@@ -11,43 +14,30 @@ from datetime import date, datetime, timedelta
 from typing import List, Optional, Literal, Dict, Any, Tuple
 import traceback
 import asyncio
-from uuid import uuid4, UUID 
 
 from fastapi import FastAPI, HTTPException, Response, Query, Depends, status
+from fastapi.responses import FileResponse
 from fastapi.exception_handlers import http_exception_handler
 from starlette.exceptions import HTTPException as StarletteHTTPException
-
 from langchain.prompts import PromptTemplate
 from langchain_groq import ChatGroq
 from pydantic import BaseModel, Field, BeforeValidator, model_serializer
 from typing_extensions import Annotated
-from pydantic_core import core_schema 
+from pydantic_core import core_schema # Import core_schema for direct use in __get_pydantic_json_schema__
 
 from pymongo import MongoClient
 from pymongo.errors import ConnectionFailure, OperationFailure
 from bson import ObjectId
 
-# --- Batching Configuration ---
-MAX_BATCH_SIZE = 20
-BATCH_INTERVAL_SECONDS = 1.0
-
-# --- Queues and pending request stores for batching ---
-extract_data_queue: Optional[asyncio.Queue[Tuple[Any, UUID]]] = None
-generate_reply_queue: Optional[asyncio.Queue[Tuple[Any, UUID]]] = None
-
-extract_pending_requests: Dict[UUID, asyncio.Future] = {}
-generate_pending_requests: Dict[UUID, asyncio.Future] = {}
-
-# Shutdown event for worker tasks
-shutdown_event = asyncio.Event()
-
-
 # --- MongoDB Configuration ---
-MONGO_URI = "mongodb+srv://precison9:P1LhtFknkT75yg5L@cluster0.isuwpef.mongodb.net" 
+# IMPORTANT: Use environment variables for your MONGO_URI in production for security.
+# Example: MONGO_URI = os.getenv("MONGO_URI", "mongodb://localhost:27017")
+MONGO_URI = "mongodb+srv://precison9:P1LhtFknkT75yg5L@cluster0.isuwpef.mongodb.net"
 DB_NAME = "email_assistant_db"
 EXTRACTED_EMAILS_COLLECTION = "extracted_emails"
 GENERATED_REPLIES_COLLECTION = "generated_replies"
 
+# Global variables for MongoDB client and collections
 client: Optional[MongoClient] = None
 db: Optional[Any] = None
 extracted_emails_collection: Optional[Any] = None
@@ -55,30 +45,50 @@ generated_replies_collection: Optional[Any] = None
 
 # --- Pydantic ObjectId Handling ---
 class CustomObjectId(str):
+    """
+    Custom Pydantic type for handling MongoDB ObjectIds.
+    It validates that the input is a valid ObjectId string and
+    ensures it's represented as a string in JSON Schema.
+    """
     @classmethod
     def __get_validators__(cls):
         yield cls.validate
+
     @classmethod
     def validate(cls, v):
+        # Allow None or empty string to pass through for optional fields
+        # This validator is only called if the field is not None
+        # Pydantic's Optional[PyObjectId] handles the None case before this validator
         if v is None or v == "":
             return None
+
         if not isinstance(v, (str, ObjectId)):
             raise ValueError("ObjectId must be a string or ObjectId instance")
+
+        # Convert ObjectId to string if it's already an ObjectId instance
         if isinstance(v, ObjectId):
             return str(v)
+
+        # Validate string format
         if not ObjectId.is_valid(v):
             raise ValueError("Invalid ObjectId format")
-        return cls(v)
+        return cls(v) # Return an instance of CustomObjectId (which is a str subclass)
+
+    # This method is crucial for Pydantic v2 to generate correct OpenAPI schema
     @classmethod
     def __get_pydantic_json_schema__(
         cls, _core_schema: core_schema.CoreSchema, handler
     ) -> Dict[str, Any]:
+        # We tell Pydantic that this custom type should be represented as a standard string
+        # in the generated JSON Schema (OpenAPI documentation).
         json_schema = handler(core_schema.str_schema())
-        json_schema["example"] = "60c728ef238b9c7b9e0f6c2a"
+        json_schema["example"] = "60c728ef238b9c7b9e0f6c2a" # Add an example for clarity
         return json_schema
 
+# Annotated type for convenience in models
 PyObjectId = Annotated[CustomObjectId, BeforeValidator(str)]
 
+
 # ---------------------- Models ----------------------
 class Contact(BaseModel):
     name: str
@@ -100,20 +110,28 @@ class Task(BaseModel):
     due_date: date
 
 class ExtractedData(BaseModel):
+    # Use PyObjectId for the _id field
     id: Optional[PyObjectId] = Field(alias="_id", default=None)
     contacts: List[Contact]
     appointments: List[Appointment]
     tasks: List[Task]
     original_email_text: str
     processed_at: datetime = Field(default_factory=datetime.utcnow)
+
     class Config:
-        populate_by_name = True
-        arbitrary_types_allowed = True
+        populate_by_name = True # Allow setting 'id' or '_id'
+        arbitrary_types_allowed = True # Allow CustomObjectId and ObjectId
+
+    # Custom serializer for JSON output to ensure ObjectId is converted to string
     @model_serializer(when_used='json')
     def serialize_model(self):
         data = self.model_dump(by_alias=True, exclude_none=True)
+        # Ensure _id is a string when serializing to JSON
         if "_id" in data and isinstance(data["_id"], ObjectId):
             data["_id"] = str(data["_id"])
+        # Ensure dates are correctly serialized to ISO format if they are date objects
+        # Pydantic v2 usually handles this automatically for `date` types,
+        # but explicit conversion can be useful if direct manipulation is expected or for specific formats.
         if 'appointments' in data:
             for appt in data['appointments']:
                 if isinstance(appt.get('start_date'), date):
@@ -140,6 +158,7 @@ class GenerateReplyRequest(BaseModel):
     emoji: str = Field("Auto", examples=["Auto", "None", "Occasional", "Frequent"])
 
 class GeneratedReplyData(BaseModel):
+    # Use PyObjectId for the _id field
     id: Optional[PyObjectId] = Field(alias="_id", default=None)
     original_email_text: str
     generated_reply_text: str
@@ -149,9 +168,11 @@ class GeneratedReplyData(BaseModel):
     tone: str
     emoji: str
     generated_at: datetime = Field(default_factory=datetime.utcnow)
+
     class Config:
         populate_by_name = True
         arbitrary_types_allowed = True
+
     @model_serializer(when_used='json')
     def serialize_model(self):
         data = self.model_dump(by_alias=True, exclude_none=True)
@@ -159,349 +180,527 @@ class GeneratedReplyData(BaseModel):
             data["_id"] = str(data["_id"])
         return data
 
+# NEW: Response Model for /generate-reply endpoint
 class GenerateReplyResponse(BaseModel):
     reply: str = Field(..., description="The AI-generated reply text.")
     stored_id: str = Field(..., description="The MongoDB ID of the stored reply.")
     cached: bool = Field(..., description="True if the reply was retrieved from cache, False if newly generated.")
 
+# --- Query Models for GET Endpoints ---
 class ExtractedEmailQuery(BaseModel):
-    contact_name: Optional[str] = Query(None, description="Filter by contact name.")
-    appointment_title: Optional[str] = Query(None, description="Filter by appointment title.")
-    task_title: Optional[str] = Query(None, description="Filter by task title.")
-    from_date: Optional[date] = Query(None, description="Filter by processed date (start).")
-    to_date: Optional[date] = Query(None, description="Filter by processed date (end).")
-    limit: int = Query(10, ge=1, le=100)
+    contact_name: Optional[str] = Query(None, description="Filter by contact name (case-insensitive partial match).")
+    appointment_title: Optional[str] = Query(None, description="Filter by appointment title (case-insensitive partial match).")
+    task_title: Optional[str] = Query(None, description="Filter by task title (case-insensitive partial match).")
+    from_date: Optional[date] = Query(None, description="Filter by data processed on or after this date (YYYY-MM-DD).")
+    to_date: Optional[date] = Query(None, description="Filter by data processed on or before this date (YYYY-MM-DD).")
+    limit: int = Query(10, ge=1, le=100, description="Maximum number of results to return.")
 
 class GeneratedReplyQuery(BaseModel):
-    language: Optional[Literal["Italian", "English"]] = Query(None, description="Filter by language.")
-    style: Optional[str] = Query(None, description="Filter by style.")
-    tone: Optional[str] = Query(None, description="Filter by tone.")
-    from_date: Optional[date] = Query(None, description="Filter by generation date (start).")
-    to_date: Optional[date] = Query(None, description="Filter by generation date (end).")
-    limit: int = Query(10, ge=1, le=100)
-
-# --- Utility Functions ---
+    language: Optional[Literal["Italian", "English"]] = Query(None, description="Filter by reply language.")
+    style: Optional[str] = Query(None, description="Filter by reply style (e.g., Professional, Casual).")
+    tone: Optional[str] = Query(None, description="Filter by reply tone (e.g., Friendly, Neutral).")
+    from_date: Optional[date] = Query(None, description="Filter by data generated on or after this date (YYYY-MM-DD).")
+    to_date: Optional[date] = Query(None, description="Filter by data generated on or before this date (YYYY-MM-DD).")
+    limit: int = Query(10, ge=1, le=100, description="Maximum number of results to return.")
+
+# ---------------------- Utility Functions ----------------------
 def extract_last_json_block(text: str) -> Optional[str]:
+    """
+    Extracts the last JSON block enclosed in ```json``` from a string,
+    or a standalone JSON object if no code block is found.
+    """
     pattern = r'```json\s*(.*?)\s*```'
     matches = re.findall(pattern, text, re.DOTALL)
-    if matches: return matches[-1].strip()
+    if matches:
+        return matches[-1].strip()
+    # Fallback: try to find a standalone JSON object
     match = re.search(r'\{.*\}', text, re.DOTALL)
-    if match: return match.group(0)
+    if match:
+        return match.group(0)
     return None
 
 def parse_date(date_str: Optional[str], current_date: date) -> Optional[date]:
-    if not date_str: return None
+    """
+    Parses a date string, handling 'today', 'tomorrow', and YYYY-MM-DD format.
+    Returns None if input is None or cannot be parsed into a valid date.
+    """
+    if not date_str:
+        return None
     date_str_lower = date_str.lower().strip()
-    if date_str_lower == "today": return current_date
-    if date_str_lower == "tomorrow": return current_date + timedelta(days=1)
-    try: return datetime.strptime(date_str_lower, "%Y-%m-%d").date()
-    except ValueError: return None
+    if date_str_lower == "today":
+        return current_date
+    if date_str_lower == "tomorrow":
+        return current_date + timedelta(days=1)
+    try:
+        return datetime.strptime(date_str_lower, "%Y-%m-%d").date()
+    except ValueError:
+        # If parsing fails, return None. The calling function (normalize_llm_output)
+        # will then decide the default (e.g., current_date).
+        return None
 
 def normalize_llm_output(data: dict, current_date: date, original_email_text: str) -> ExtractedData:
+    """
+    Normalizes and validates LLM extracted data into ExtractedData Pydantic model.
+    Handles defaults for dates and name splitting.
+    """
     def split_name(full_name: str) -> tuple[str, str]:
         parts = full_name.strip().split()
         name = parts[0] if parts else ""
         last_name = " ".join(parts[1:]) if len(parts) > 1 else ""
         return name, last_name
-    contacts_data = [Contact(name=split_name(c.get("name",""))[0], last_name=split_name(c.get("name",""))[1], email=c.get("email"), phone_number=c.get("phone_number")) for c in data.get("contacts", [])]
-    appointments_data = [Appointment(title=a.get("title", "Untitled"), description=a.get("description", "No description"), start_date=parse_date(a.get("start_date"), current_date) or current_date, start_time=a.get("start_time"), end_date=parse_date(a.get("end_date"), current_date), end_time=a.get("end_time")) for a in data.get("appointments", [])]
-    tasks_data = [Task(task_title=t.get("task_title", "Untitled"), task_description=t.get("task_description", "No description"), due_date=parse_date(t.get("due_date"), current_date) or current_date) for t in data.get("tasks", [])]
+
+    contacts_data = []
+    for c in data.get("contacts", []):
+        name_val, last_name_val = split_name(c.get("name", ""))
+        contacts_data.append(Contact(name=name_val, last_name=last_name_val, email=c.get("email"), phone_number=c.get("phone_number")))
+
+    appointments_data = []
+    for a in data.get("appointments", []):
+        # Default start_date to current_date if not provided or invalid
+        start_date_val = parse_date(a.get("start_date"), current_date) or current_date
+        # end_date remains optional
+        end_date_val = parse_date(a.get("end_date"), current_date)
+
+        appointments_data.append(Appointment(
+            title=a.get("title", "Untitled"), description=a.get("description", "No description"),
+            start_date=start_date_val, start_time=a.get("start_time"),
+            end_date=end_date_val, end_time=a.get("end_time")
+        ))
+
+    tasks_data = []
+    for t in data.get("tasks", []):
+        # Default due_date to current_date if not provided or invalid
+        due_date_val = parse_date(t.get("due_date"), current_date) or current_date
+        tasks_data.append(Task(
+            task_title=t.get("task_title", "Untitled"), task_description=t.get("task_description", "No description"),
+            due_date=due_date_val
+        ))
     return ExtractedData(contacts=contacts_data, appointments=appointments_data, tasks=tasks_data, original_email_text=original_email_text)
 
-# --- Core Logic (Internal Functions) ---
+# ---------------------- Core Logic (Internal Functions) ----------------------
 def _process_email_internal(email_text: str, api_key: str, current_date: date) -> ExtractedData:
-    if not email_text: raise ValueError("Email text cannot be empty for processing.")
+    """
+    Internal function to process email text using LLM and extract structured data.
+    """
+    if not email_text:
+        raise ValueError("Email text cannot be empty for processing.")
+
     llm = ChatGroq(model="meta-llama/llama-4-scout-17b-16e-instruct", temperature=0, max_tokens=2000, groq_api_key=api_key)
+
     prompt_today_str = current_date.isoformat()
     prompt_tomorrow_str = (current_date + timedelta(days=1)).isoformat()
-    prompt_template_str = f"""You are an expert email assistant tasked with extracting structured information from an Italian email.**Your response MUST be a single, complete JSON object, wrapped in a ```json``` block.****DO NOT include any conversational text, explanations, or preambles outside the JSON block.****The JSON should contain three top-level keys: "contacts", "appointments", and "tasks".**If a category has no items, its list should be empty (e.g., "contacts": []).Here is the required JSON schema for each category:- **contacts**: List of Contact objects.    Each Contact object must have:    - `name` (string, full name)    - `last_name` (string, last name) - You should infer this from the full name.    - `email` (string, optional, null if not present)    - `phone_number` (string, optional, null if not present)- **appointments**: List of Appointment objects.    Each Appointment object must have:    - `title` (string, short, meaningful title in Italian based on the meeting's purpose)    - `description` (string, summary of the meeting's goal)    - `start_date` (string, YYYY-MM-DD. If not explicitly mentioned, use "{prompt_today_str}" for "today", or "{prompt_tomorrow_str}" for "tomorrow")    - `start_time` (string, optional, e.g., "10:30 AM", null if not present)    - `end_date` (string, YYYY-MM-DD, optional, null if unknown or not applicable)    - `end_time` (string, optional, e.g., "11:00 AM", null if not present)- **tasks**: List of Task objects.    Each Task object must have:    - `task_title` (string, short summary of action item)    - `task_description` (string, more detailed explanation)    - `due_date` (string, YYYY-MM-DD. Infer from context, e.g., "entro domani" becomes "{prompt_tomorrow_str}", "today" becomes "{prompt_today_str}")---Email:{{email}}"""
-    prompt_template = PromptTemplate(input_variables=["email"], template=prompt_template_str)
+
+    prompt_template_str = f"""
+You are an expert email assistant tasked with extracting structured information from an Italian email.
+
+**Your response MUST be a single, complete JSON object, wrapped in a ```json``` block.**
+**DO NOT include any conversational text, explanations, or preambles outside the JSON block.**
+**The JSON should contain three top-level keys: "contacts", "appointments", and "tasks".**
+If a category has no items, its list should be empty (e.g., "contacts": []).
+
+Here is the required JSON schema for each category:
+
+- **contacts**: List of Contact objects.
+    Each Contact object must have:
+    - `name` (string, full name)
+    - `last_name` (string, last name) - You should infer this from the full name.
+    - `email` (string, optional, null if not present)
+    - `phone_number` (string, optional, null if not present)
+
+- **appointments**: List of Appointment objects.
+    Each Appointment object must have:
+    - `title` (string, short, meaningful title in Italian based on the meeting's purpose)
+    - `description` (string, summary of the meeting's goal)
+    - `start_date` (string, YYYY-MM-DD. If not explicitly mentioned, use "{prompt_today_str}" for "today", or "{prompt_tomorrow_str}" for "tomorrow")
+    - `start_time` (string, optional, e.g., "10:30 AM", null if not present)
+    - `end_date` (string, YYYY-MM-DD, optional, null if unknown or not applicable)
+    - `end_time` (string, optional, e.g., "11:00 AM", null if not present)
+
+- **tasks**: List of Task objects.
+    Each Task object must have:
+    - `task_title` (string, short summary of action item)
+    - `task_description` (string, more detailed explanation)
+    - `due_date` (string, YYYY-MM-DD. Infer from context, e.g., "entro domani" becomes "{prompt_tomorrow_str}", "today" becomes "{prompt_today_str}")
+
+---
+
+Email:
+{{email}}
+"""
+    prompt_template = PromptTemplate(input_variables=["email", "prompt_today_str", "prompt_tomorrow_str"], template=prompt_template_str)
     chain = prompt_template | llm
     try:
-        llm_output = chain.invoke({"email": email_text})
+        llm_output = chain.invoke({"email": email_text, "prompt_today_str": prompt_today_str, "prompt_tomorrow_str": prompt_tomorrow_str})
         llm_output_str = llm_output.content
+
         json_str = extract_last_json_block(llm_output_str)
-        if not json_str: raise ValueError(f"No JSON block found in LLM output. LLM response: {llm_output_str}")
+
+        if not json_str:
+            raise ValueError(f"No JSON block found in LLM output. LLM response: {llm_output_str}")
         json_data = json.loads(json_str)
-        return normalize_llm_output(json_data, current_date, email_text)
-    except json.JSONDecodeError as e: raise ValueError(f"Failed to parse JSON from LLM output: {e}\nLLM response was:\n{llm_output_str}")
-    except Exception as e: traceback.print_exc(); raise Exception(f"An error occurred during email processing: {e}")
 
-def _generate_response_internal(email_text: str, api_key: str, language: Literal["Italian", "English"], length: str, style: str, tone: str, emoji: str) -> str:
-    if not email_text: return "Cannot generate reply for empty email text."
+        extracted_data = normalize_llm_output(json_data, current_date, email_text)
+        return extracted_data
+    except json.JSONDecodeError as e:
+        raise ValueError(f"Failed to parse JSON from LLM output: {e}\nLLM response was:\n{llm_output_str}")
+    except Exception as e:
+        traceback.print_exc()
+        raise Exception(f"An error occurred during email processing: {e}")
+
+def _generate_response_internal(
+    email_text: str, api_key: str, language: Literal["Italian", "English"],
+    length: str, style: str, tone: str, emoji: str
+) -> str:
+    """
+    Internal function to generate a reply to an email using LLM.
+    """
+    print(f"[{datetime.now()}] _generate_response_internal: Starting LLM call. API Key starts with: {api_key[:5]}...") # Debug log
+    if not email_text:
+        print(f"[{datetime.now()}] _generate_response_internal: Email text is empty.")
+        return "Cannot generate reply for empty email text."
+
     try:
         llm = ChatGroq(model="meta-llama/llama-4-scout-17b-16e-instruct", temperature=0.7, max_tokens=800, groq_api_key=api_key)
-        prompt_template_str="""    You are an assistant that helps reply to emails.    Create a response to the following email with the following parameters:    - Language: {language}    - Length: {length}    - Style: {style}    - Tone: {tone}    - Emoji usage: {emoji}    Email:    {email}    Write only the reply body. Do not repeat the email or mention any instruction.    """
-        prompt = PromptTemplate(input_variables=["email", "language", "length", "style", "tone", "emoji"], template=prompt_template_str)
+        prompt_template_str="""
+    You are an assistant that helps reply to emails.
+
+    Create a response to the following email with the following parameters:
+    - Language: {language}
+    - Length: {length}
+    - Style: {style}
+    - Tone: {tone}
+    - Emoji usage: {emoji}
+
+    Email:
+    {email}
+
+    Write only the reply body. Do not repeat the email or mention any instruction.
+    """
+        prompt = PromptTemplate(
+            input_variables=["email", "language", "length", "style", "tone", "emoji"],
+            template=prompt_template_str
+        )
         chain = prompt | llm
+        print(f"[{datetime.now()}] _generate_response_internal: Invoking LLM chain...") # Debug log
         output = chain.invoke({"email": email_text, "language": language, "length": length, "style": style, "tone": tone, "emoji": emoji})
+        print(f"[{datetime.now()}] _generate_response_internal: LLM chain returned. Content length: {len(output.content)}.") # Debug log
         return output.content.strip()
-    except Exception as e: traceback.print_exc(); raise
+    except Exception as e:
+        print(f"[{datetime.now()}] _generate_response_internal: ERROR during LLM invocation: {e}") # Debug log
+        traceback.print_exc() # Print full traceback to logs
+        raise # Re-raise the exception so it can be caught by handle_single_reply_request
 
-# --- FastAPI Application ---
-# NOTE: To make this batching system work correctly with asyncio.Queue,
-# you must run the server with a SINGLE WORKER. For example:
-# uvicorn main:app --workers 1
-app = FastAPI(title="Email Assistant API", description="API with batch processing.", version="1.3.0", docs_url="/", redoc_url="/redoc")
 
-# --- Exception Handlers ---
+# --- FastAPI Application ---
+app = FastAPI(
+    title="Email Assistant API",
+    description="API for extracting structured data from emails and generating intelligent replies using Groq LLMs, with MongoDB integration, dynamic date handling, and caching.",
+    version="1.1.0",
+    docs_url="/", # Sets Swagger UI to be the root path
+    redoc_url="/redoc"
+)
+
+# --- Global Exception Handler ---
+# Catch Starlette HTTPExceptions (FastAPI uses these internally)
 @app.exception_handler(StarletteHTTPException)
 async def custom_http_exception_handler_wrapper(request, exc):
+    """Handles FastAPI's internal HTTP exceptions."""
+    print(f"[{datetime.now()}] Caught StarletteHTTPException: {exc.status_code} - {exc.detail}")
     return await http_exception_handler(request, exc)
 
+# Catch all other unhandled exceptions
 @app.exception_handler(Exception)
 async def global_exception_handler_wrapper(request, exc):
-    traceback.print_exc()
-    return Response(content=json.dumps({"detail": f"Internal Server Error: {str(exc)}", "type": "unhandled_exception"}), status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, media_type="application/json")
-
-# --- Batch Worker Helper Functions ---
-async def _execute_single_extract_task(request_item: ProcessEmailRequest, request_id: UUID) -> Tuple[UUID, Any]:
-    current_date = date.today()
-    try:
-        result = await asyncio.to_thread(_process_email_internal, request_item.email_text, request_item.groq_api_key, current_date)
-        return request_id, result
-    except Exception as e:
-        return request_id, e
-
-async def _execute_single_generate_reply_task(request_item: GenerateReplyRequest, request_id: UUID) -> Tuple[UUID, Any]:
-    try:
-        result = await asyncio.to_thread(_generate_response_internal, request_item.email_text, request_item.groq_api_key, request_item.language, request_item.length, request_item.style, request_item.tone, request_item.emoji)
-        return request_id, result
-    except Exception as e:
-        return request_id, e
-
-# --- Batch Worker Functions ---
-async def batch_worker(
-    queue: asyncio.Queue,
-    pending_requests: Dict[UUID, asyncio.Future],
-    execution_function: callable,
-    worker_name: str
-):
-    print(f"[{datetime.now()}] {worker_name} started.")
-    while not shutdown_event.is_set():
-        try:
-            await asyncio.wait_for(shutdown_event.wait(), timeout=BATCH_INTERVAL_SECONDS)
-            if shutdown_event.is_set(): break
-        except asyncio.TimeoutError:
-            pass  # Interval elapsed, time to process
-
-        batch_to_process = []
-        while len(batch_to_process) < MAX_BATCH_SIZE and not queue.empty():
-            try:
-                batch_to_process.append(queue.get_nowait())
-            except asyncio.QueueEmpty:
-                break
-        
-        if not batch_to_process:
-            continue
-
-        print(f"[{datetime.now()}] {worker_name}: Processing batch of {len(batch_to_process)} requests.")
-        
-        tasks = [execution_function(req_obj, req_id) for req_obj, req_id in batch_to_process]
-        results = await asyncio.gather(*tasks)
-
-        for req_id, result_or_exc in results:
-            future = pending_requests.pop(req_id, None)
-            if future and not future.done():
-                if isinstance(result_or_exc, Exception):
-                    future.set_exception(result_or_exc)
-                else:
-                    future.set_result(result_or_exc)
-
-    print(f"[{datetime.now()}] {worker_name} shutting down. Cancelling pending requests...")
-    for req_id, future in list(pending_requests.items()):
-        if not future.done():
-            future.set_exception(HTTPException(status_code=503, detail="Service shutting down"))
-        pending_requests.pop(req_id, None)
-    print(f"[{datetime.now()}] {worker_name} stopped.")
-
-# --- FastAPI Event Handlers ---
-worker_tasks: List[asyncio.Task] = []
-
+    """Handles all unhandled exceptions and returns a consistent JSON error response."""
+    print(f"[{datetime.now()}] Unhandled exception caught by global handler for request: {request.url}")
+    traceback.print_exc() # Print traceback to console for debugging
+    # Return a JSON response for consistency, even for unhandled errors
+    return Response(
+        content=json.dumps({"detail": f"Internal Server Error: {str(exc)}", "type": "unhandled_exception"}),
+        status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+        media_type="application/json"
+    )
+
+
+# --- FastAPI Event Handlers for MongoDB ---
 @app.on_event("startup")
 async def startup_event():
     global client, db, extracted_emails_collection, generated_replies_collection
-    global extract_data_queue, generate_reply_queue, worker_tasks
-
     print(f"[{datetime.now()}] FastAPI app startup sequence initiated.")
     try:
+        # Connect to MongoDB
         client = MongoClient(MONGO_URI, serverSelectionTimeoutMS=5000)
-        client.admin.command('ping')
+        client.admin.command('ping') # Test connection
         db = client[DB_NAME]
         extracted_emails_collection = db[EXTRACTED_EMAILS_COLLECTION]
         generated_replies_collection = db[GENERATED_REPLIES_COLLECTION]
         print(f"[{datetime.now()}] Successfully connected to MongoDB: {DB_NAME}")
 
-        extract_data_queue = asyncio.Queue()
-        generate_reply_queue = asyncio.Queue()
-        
-        task1 = asyncio.create_task(batch_worker(extract_data_queue, extract_pending_requests, _execute_single_extract_task, "Extract Worker"))
-        task2 = asyncio.create_task(batch_worker(generate_reply_queue, generate_pending_requests, _execute_single_generate_reply_task, "Reply Worker"))
-        worker_tasks.extend([task1, task2])
-        print(f"[{datetime.now()}] Batch processing workers started.")
-
     except (ConnectionFailure, OperationFailure) as e:
         print(f"[{datetime.now()}] ERROR: MongoDB Connection/Operation Failure: {e}")
-        client = db = extracted_emails_collection = generated_replies_collection = None
+        client = None
+        db = None
+        extracted_emails_collection = None
+        generated_replies_collection = None
     except Exception as e:
-        print(f"[{datetime.now()}] ERROR: An unexpected error during startup: {e}")
+        print(f"[{datetime.now()}] ERROR: An unexpected error occurred during MongoDB connection startup: {e}")
         traceback.print_exc()
-        client = db = extracted_emails_collection = generated_replies_collection = None
+        client = None
+        db = None
+        extracted_emails_collection = None
+        generated_replies_collection = None
     finally:
-        # CORRECTED: Use 'is not None' for pymongo objects
-        if not (client is not None and db is not None and extracted_emails_collection is not None and generated_replies_collection is not None):
-            print(f"[{datetime.now()}] WARNING: MongoDB might not be fully initialized.")
-        print(f"[{datetime.now()}] FastAPI app startup sequence completed.")
+        if client is not None and db is not None:
+            try:
+                client.admin.command('ping')
+            except Exception as e:
+                print(f"[{datetime.now()}] MongoDB ping failed after initial connection attempt during finally block: {e}")
+                client = None; db = None; extracted_emails_collection = None; generated_replies_collection = None
+        else:
+            print(f"[{datetime.now()}] MongoDB client or db object is None after connection attempt in startup. Database likely not connected.")
+            if client is None or db is None:
+                client = None; db = None; extracted_emails_collection = None; generated_replies_collection = None
+        print(f"[{datetime.now()}] FastAPI app startup sequence completed for MongoDB client initialization.")
 
 
 @app.on_event("shutdown")
-async def shutdown_event_handler():
-    global client, shutdown_event, worker_tasks
+async def shutdown_event():
+    global client
     print(f"[{datetime.now()}] FastAPI app shutting down.")
-    
-    if not shutdown_event.is_set():
-        shutdown_event.set()
-    
-    if worker_tasks:
-        print(f"[{datetime.now()}] Waiting for worker tasks to complete...")
-        await asyncio.gather(*worker_tasks, return_exceptions=True)
-        print(f"[{datetime.now()}] Worker tasks completed.")
-
-    if client is not None:
+    if client:
         client.close()
         print(f"[{datetime.now()}] MongoDB client closed.")
-    print(f"[{datetime.now()}] FastAPI app shutdown sequence completed.")
+
 
 # --- API Endpoints ---
 @app.get("/health", summary="Health Check")
 async def health_check():
+    """
+    Checks the health of the API, including MongoDB connection.
+    """
     db_status = "MongoDB not connected."
     db_ok = False
-    # CORRECTED: Use 'is not None' for pymongo objects
     if client is not None and db is not None:
         try:
-            await asyncio.to_thread(client.admin.command, 'ping')
+            # Use asyncio.to_thread for blocking MongoDB call
+            await asyncio.to_thread(db.list_collection_names)
             db_status = "MongoDB connection OK."
             db_ok = True
         except Exception as e:
             db_status = f"MongoDB connection error: {e}"
-    
-    queues = {}
-    if extract_data_queue is not None and generate_reply_queue is not None:
-        queues = {
-            "extract_data_queue_size": extract_data_queue.qsize(),
-            "generate_reply_queue_size": generate_reply_queue.qsize(),
-            "extract_pending_requests": len(extract_pending_requests),
-            "generate_pending_requests": len(generate_pending_requests)
-        }
-    
+            db_ok = False
+
     if db_ok:
-        return {"status": "ok", "message": "API is up.", "database": db_status, "queues": queues}
+        return {"status": "ok", "message": "Email Assistant API is up.", "database": db_status}
     else:
-        raise HTTPException(status_code=503, detail={"message": "Service unavailable.", "database": db_status})
+        raise HTTPException(
+            status_code=503,
+            detail={"message": "Service unavailable.", "database": db_status}
+        )
+
+
+@app.post("/generate-reply", response_model=GenerateReplyResponse, summary="Generate a smart reply to an email")
+async def generate_email_reply(request: GenerateReplyRequest):
+    """
+    Generates a smart reply to the provided email text using an LLM.
+    The generated reply is also stored in MongoDB for caching and historical purposes.
+    """
+    if generated_replies_collection is None:
+        raise HTTPException(status_code=503, detail="MongoDB not available for generated_replies.")
+
+    try:
+        # Check cache first
+        cache_query = {
+            "original_email_text": request.email_text,
+            "language": request.language,
+            "length": request.length,
+            "style": request.style,
+            "tone": request.tone,
+            "emoji": request.emoji,
+        }
+        print(f"[{datetime.now()}] /generate-reply: Checking cache for reply...")
+        # Use asyncio.to_thread for blocking MongoDB operations
+        cached_reply_doc = await asyncio.to_thread(generated_replies_collection.find_one, cache_query)
+
+        if cached_reply_doc:
+            print(f"[{datetime.now()}] /generate-reply: Reply found in cache. ID: {str(cached_reply_doc['_id'])}")
+            return GenerateReplyResponse(
+                reply=cached_reply_doc["generated_reply_text"],
+                stored_id=str(cached_reply_doc["_id"]),
+                cached=True
+            )
+
+        # If not in cache, directly call the internal LLM function
+        print(f"[{datetime.now()}] /generate-reply: Reply not in cache. Calling LLM for generation...")
+        reply_content = await asyncio.to_thread(
+            _generate_response_internal,
+            request.email_text,
+            request.groq_api_key,
+            request.language,
+            request.length,
+            request.style,
+            request.tone,
+            request.emoji
+        )
+        print(f"[{datetime.now()}] /generate-reply: LLM call completed. Storing newly generated reply in MongoDB.")
+
+        # Prepare data for storage
+        reply_data_to_store = GeneratedReplyData(
+            original_email_text=request.email_text,
+            generated_reply_text=reply_content,
+            language=request.language,
+            length=request.length,
+            style=request.style,
+            tone=request.tone,
+            emoji=request.emoji
+        )
+        # Use model_dump for Pydantic v2. Exclude 'id' as it's generated by MongoDB.
+        reply_data_dict = reply_data_to_store.model_dump(by_alias=True, exclude_none=True, exclude={'id'})
 
-@app.post("/extract-data", response_model=ExtractedData, summary="Extract structured data (batched)")
+        # Insert into MongoDB
+        insert_result = await asyncio.to_thread(generated_replies_collection.insert_one, reply_data_dict)
+        stored_id = str(insert_result.inserted_id) # Convert ObjectId to string for the response
+
+        print(f"[{datetime.now()}] /generate-reply: Reply stored in MongoDB. ID: {stored_id}")
+
+        # Return the response as per GenerateReplyResponse model
+        return GenerateReplyResponse(
+            reply=reply_content,
+            stored_id=stored_id,
+            cached=False # Always False since we just generated it
+        )
+    except Exception as e:
+        traceback.print_exc()
+        # Ensure consistent error response
+        raise HTTPException(status_code=500, detail=f"Error generating or storing reply: {str(e)}")
+
+@app.post("/extract-data", response_model=ExtractedData, summary="Extract structured data from an email")
 async def extract_email_data(request: ProcessEmailRequest):
-    # CORRECTED: Use 'is None' check
-    if extracted_emails_collection is None or extract_data_queue is None:
-        raise HTTPException(status_code=503, detail="Service not available (DB or batch queue).")
+    """
+    Extracts contacts, appointments, and tasks from the provided email text.
+    """
+    if extracted_emails_collection is None:
+        raise HTTPException(status_code=503, detail="MongoDB not available.")
 
-    request_id = uuid4()
-    future: asyncio.Future[ExtractedData] = asyncio.get_event_loop().create_future()
-    extract_pending_requests[request_id] = future
+    current_date = date.today() # Get current date for context
 
+    print(f"[{datetime.now()}] /extract-data: Received request.")
     try:
-        await extract_data_queue.put((request, request_id))
-        extracted_data_obj = await asyncio.wait_for(future, timeout=60.0)
-        
-        data_to_insert = extracted_data_obj.model_dump(by_alias=True, exclude_none=True, exclude={'id'})
+        print(f"[{datetime.now()}] /extract-data: Calling internal processing function.")
+        # Run blocking LLM call in a thread pool
+        extracted_data = await asyncio.to_thread(_process_email_internal, request.email_text, request.groq_api_key, current_date)
+
+        print(f"[{datetime.now()}] /extract-data: Internal processing complete. Preparing for DB insert.")
+        # Convert Pydantic model to dictionary for MongoDB insert, handling _id alias
+        # Use model_dump for Pydantic v2
+        data_to_insert = extracted_data.model_dump(by_alias=True, exclude_none=True, exclude={'id'})
+
+        # --- NEW CONVERSION FOR MONGODB ---
+        # MongoDB's BSON doesn't natively support Python's datetime.date type.
+        # It expects datetime.datetime. Convert all date fields to datetime.datetime.
         if 'appointments' in data_to_insert:
             for appt in data_to_insert['appointments']:
-                if isinstance(appt.get('start_date'), date): appt['start_date'] = datetime.combine(appt['start_date'], datetime.min.time())
-                if isinstance(appt.get('end_date'), date) and appt.get('end_date') is not None: appt['end_date'] = datetime.combine(appt['end_date'], datetime.min.time())
+                if isinstance(appt.get('start_date'), date):
+                    appt['start_date'] = datetime.combine(appt['start_date'], datetime.min.time())
+                if isinstance(appt.get('end_date'), date) and appt.get('end_date') is not None:
+                    appt['end_date'] = datetime.combine(appt['end_date'], datetime.min.time())
         if 'tasks' in data_to_insert:
             for task_item in data_to_insert['tasks']:
-                if isinstance(task_item.get('due_date'), date): task_item['due_date'] = datetime.combine(task_item['due_date'], datetime.min.time())
-        
-        insert_result = await asyncio.to_thread(extracted_emails_collection.insert_one, data_to_insert)
-        extracted_data_obj.id = str(insert_result.inserted_id)
-        
-        return extracted_data_obj
-    except asyncio.TimeoutError:
-        raise HTTPException(status_code=504, detail="Request timed out while awaiting processing in batch.")
-    except Exception as e:
-        # Re-raise exceptions set by the worker (like HTTPException or others)
-        raise e
-    finally:
-        extract_pending_requests.pop(request_id, None)
-
-@app.post("/generate-reply", response_model=GenerateReplyResponse, summary="Generate smart reply (batched)")
-async def generate_email_reply(request: GenerateReplyRequest):
-    # CORRECTED: Use 'is None' check
-    if generated_replies_collection is None or generate_reply_queue is None:
-        raise HTTPException(status_code=503, detail="Service not available (DB or batch queue).")
-
-    cache_query = {"original_email_text": request.email_text, "language": request.language, "length": request.length, "style": request.style, "tone": request.tone, "emoji": request.emoji}
-    cached_reply_doc = await asyncio.to_thread(generated_replies_collection.find_one, cache_query)
-    if cached_reply_doc:
-        return GenerateReplyResponse(reply=cached_reply_doc["generated_reply_text"], stored_id=str(cached_reply_doc["_id"]), cached=True)
+                if isinstance(task_item.get('due_date'), date):
+                    task_item['due_date'] = datetime.combine(task_item['due_date'], datetime.min.time())
+        # --- END NEW CONVERSION ---
 
-    request_id = uuid4()
-    future: asyncio.Future[str] = asyncio.get_event_loop().create_future()
-    generate_pending_requests[request_id] = future
+        print(f"[{datetime.now()}] /extract-data: Inserting into MongoDB... Data: {data_to_insert}") # Add data logging
+        # Use asyncio.to_thread for blocking MongoDB insert operation
+        insert_result = await asyncio.to_thread(extracted_emails_collection.insert_one, data_to_insert)
 
-    try:
-        await generate_reply_queue.put((request, request_id))
-        reply_content_str = await asyncio.wait_for(future, timeout=60.0)
+        # Update the extracted_data object with the MongoDB-generated ID
+        extracted_data.id = str(insert_result.inserted_id)
+        print(f"[{datetime.now()}] /extract-data: Data inserted into MongoDB. ID: {extracted_data.id}")
 
-        reply_data_to_store = GeneratedReplyData(
-            original_email_text=request.email_text, generated_reply_text=reply_content_str,
-            language=request.language, length=request.length, style=request.style,
-            tone=request.tone, emoji=request.emoji
-        )
-        reply_data_dict = reply_data_to_store.model_dump(by_alias=True, exclude_none=True, exclude={'id'})
-        
-        insert_result = await asyncio.to_thread(generated_replies_collection.insert_one, reply_data_dict)
-        stored_id = str(insert_result.inserted_id)
-        
-        return GenerateReplyResponse(reply=reply_content_str, stored_id=stored_id, cached=False)
-    except asyncio.TimeoutError:
-        raise HTTPException(status_code=504, detail="Request timed out while awaiting reply generation in batch.")
+        return extracted_data
+    except ValueError as ve:
+        raise HTTPException(status_code=400, detail=str(ve))
     except Exception as e:
-        raise e
-    finally:
-        generate_pending_requests.pop(request_id, None)
+        traceback.print_exc() # Print full traceback for debugging
+        raise HTTPException(status_code=500, detail=f"An internal server error occurred: {e}")
+
 
 @app.get("/query-extracted-emails", response_model=List[ExtractedData], summary="Query stored extracted email data")
 async def query_extracted_emails(query_params: ExtractedEmailQuery = Depends()):
-    if extracted_emails_collection is None: raise HTTPException(status_code=503, detail="MongoDB not available.")
+    """
+    Queries extracted email data from MongoDB based on various filters.
+    """
+    if extracted_emails_collection is None:
+        raise HTTPException(status_code=503, detail="MongoDB not available.")
+
     mongo_query = {}
-    if query_params.contact_name: mongo_query["$or"] = [{"contacts.name": {"$regex": query_params.contact_name, "$options": "i"}}, {"contacts.last_name": {"$regex": query_params.contact_name, "$options": "i"}}]
-    if query_params.appointment_title: mongo_query["appointments.title"] = {"$regex": query_params.appointment_title, "$options": "i"}
-    if query_params.task_title: mongo_query["tasks.task_title"] = {"$regex": query_params.task_title, "$options": "i"}
+    if query_params.contact_name:
+        # Case-insensitive partial match on contact name or last name
+        mongo_query["$or"] = [
+            {"contacts.name": {"$regex": query_params.contact_name, "$options": "i"}},
+            {"contacts.last_name": {"$regex": query_params.contact_name, "$options": "i"}}
+        ]
+    if query_params.appointment_title:
+        mongo_query["appointments.title"] = {"$regex": query_params.appointment_title, "$options": "i"}
+    if query_params.task_title:
+        mongo_query["tasks.task_title"] = {"$regex": query_params.task_title, "$options": "i"}
+
+    # Date range filtering for processed_at
     date_query = {}
-    if query_params.from_date: date_query["$gte"] = datetime.combine(query_params.from_date, datetime.min.time())
-    if query_params.to_date: date_query["$lte"] = datetime.combine(query_params.to_date, datetime.max.time())
-    if date_query: mongo_query["processed_at"] = date_query
+    if query_params.from_date:
+        date_query["$gte"] = datetime.combine(query_params.from_date, datetime.min.time())
+    if query_params.to_date:
+        date_query["$lte"] = datetime.combine(query_params.to_date, datetime.max.time())
+    if date_query:
+        mongo_query["processed_at"] = date_query
+
     try:
+        # Use asyncio.to_thread for blocking MongoDB find operation
         cursor = await asyncio.to_thread(extracted_emails_collection.find, mongo_query)
+        # Use to_list to limit results and convert to list
         results = await asyncio.to_thread(lambda: list(cursor.limit(query_params.limit)))
+
+        # Convert MongoDB documents to ExtractedData Pydantic models
         return [ExtractedData(**doc) for doc in results]
-    except Exception as e: traceback.print_exc(); raise HTTPException(status_code=500, detail=f"Error querying extracted emails: {e}")
+    except Exception as e:
+        traceback.print_exc()
+        raise HTTPException(status_code=500, detail=f"Error querying extracted emails: {e}")
+
 
 @app.get("/query-generated-replies", response_model=List[GeneratedReplyData], summary="Query stored generated replies")
 async def query_generated_replies(query_params: GeneratedReplyQuery = Depends()):
-    if generated_replies_collection is None: raise HTTPException(status_code=503, detail="MongoDB not available.")
+    """
+    Queries generated email replies from MongoDB based on various filters.
+    """
+    if generated_replies_collection is None:
+        raise HTTPException(status_code=503, detail="MongoDB not available.")
+
     mongo_query = {}
-    if query_params.language: mongo_query["language"] = query_params.language
-    if query_params.style: mongo_query["style"] = query_params.style
-    if query_params.tone: mongo_query["tone"] = query_params.tone
+    if query_params.language:
+        mongo_query["language"] = query_params.language
+    if query_params.style:
+        mongo_query["style"] = query_params.style
+    if query_params.tone:
+        mongo_query["tone"] = query_params.tone
+
+    # Date range filtering for generated_at
     date_query = {}
-    if query_params.from_date: date_query["$gte"] = datetime.combine(query_params.from_date, datetime.min.time())
-    if query_params.to_date: date_query["$lte"] = datetime.combine(query_params.to_date, datetime.max.time())
-    if date_query: mongo_query["generated_at"] = date_query
+    if query_params.from_date:
+        date_query["$gte"] = datetime.combine(query_params.from_date, datetime.min.time())
+    if query_params.to_date:
+        date_query["$lte"] = datetime.combine(query_params.to_date, datetime.max.time())
+    if date_query:
+        mongo_query["generated_at"] = date_query
+
     try:
+        # Use asyncio.to_thread for blocking MongoDB find operation
         cursor = await asyncio.to_thread(generated_replies_collection.find, mongo_query)
+        # Use to_list to limit results and convert to list
         results = await asyncio.to_thread(lambda: list(cursor.limit(query_params.limit)))
+
+        # Convert MongoDB documents to GeneratedReplyData Pydantic models
         return [GeneratedReplyData(**doc) for doc in results]
-    except Exception as e: traceback.print_exc(); raise HTTPException(status_code=500, detail=f"Error querying generated replies: {e}")
+    except Exception as e:
+        traceback.print_exc()
+        raise HTTPException(status_code=500, detail=f"Error querying generated replies: {e}")