Update flask_Character.py

flask_Character.py

@@ -5,29 +5,666 @@
 import os
 os.environ["NUMBA_CACHE_DIR"] = "/tmp/numba_cache"
 os.environ["NUMBA_DISABLE_CACHE"] = "1"
-from flask import request, jsonify
-import numpy as np
-import torch
-import flask
+import json
+import re
+from datetime import date, datetime, timedelta
+from typing import List, Optional, Literal, Dict, Any, Tuple
+import os
+import traceback
+import asyncio
+
+from fastapi import FastAPI, HTTPException, Response, Query, Depends
+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 pymongo import MongoClient
+from pymongo.errors import ConnectionFailure, OperationFailure
+from bson import ObjectId
+
+# --- MongoDB Configuration ---
+MONGO_URI = "mongodb+srv://precison9:P1LhtFknkT75yg5L@cluster0.isuwpef.mongodb.net" # Replace with your actual URI
+DB_NAME = "email_assistant_db"
+EXTRACTED_EMAILS_COLLECTION = "extracted_emails"
+GENERATED_REPLIES_COLLECTION = "generated_replies"
+
+client: Optional[MongoClient] = None
+db: Optional[Any] = None # Changed to Optional[Any] as Database type is not directly imported for annotation here
+extracted_emails_collection: Optional[Any] = None
+generated_replies_collection: Optional[Any] = None
+
+# --- Pydantic ObjectId Handling ---
+class CustomObjectId(str):
+    @classmethod
+    def __get_validators__(cls):
+        yield cls.validate
+
+    @classmethod
+    def validate(cls, v, info):
+        if not ObjectId.is_valid(v):
+            raise ValueError("Invalid ObjectId")
+        return str(v)
+
+    @classmethod
+    def __get_pydantic_json_schema__(cls, core_schema, handler):
+        json_schema = handler(core_schema)
+        json_schema["type"] = "string"
+        json_schema["example"] = "60c728ef238b9c7b9e0f6c2a"
+        return json_schema
+
+PyObjectId = Annotated[CustomObjectId, BeforeValidator(str)]
+
+# ---------------------- Models ----------------------
+class Contact(BaseModel):
+    name: str
+    last_name: str
+    email: Optional[str] = None
+    phone_number: Optional[str] = None
+
+class Appointment(BaseModel):
+    title: str
+    description: str
+    start_date: date
+    start_time: Optional[str] = None
+    end_date: Optional[date] = None
+    end_time: Optional[str] = None
+
+class Task(BaseModel):
+    task_title: str
+    task_description: str
+    due_date: date
+
+class ExtractedData(BaseModel):
+    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
+
+    @model_serializer(when_used='json')
+    def serialize_model(self):
+        data = self.model_dump(by_alias=True, exclude_none=True)
+        if "_id" in data and isinstance(data["_id"], ObjectId):
+            data["_id"] = str(data["_id"])
+        if 'appointments' in data:
+            for appt in data['appointments']:
+                if isinstance(appt.get('start_date'), date):
+                    appt['start_date'] = appt['start_date'].isoformat()
+                if isinstance(appt.get('end_date'), date) and appt.get('end_date') is not None:
+                    appt['end_date'] = appt['end_date'].isoformat()
+        if 'tasks' in data:
+            for task_item in data['tasks']:
+                if isinstance(task_item.get('due_date'), date):
+                    task_item['due_date'] = task_item['due_date'].isoformat()
+        return data
+
+class ProcessEmailRequest(BaseModel):
+    email_text: str = Field(..., example="Oggetto: Follow-up progetto “Delta”...")
+    groq_api_key: str = Field(..., example="YOUR_GROQ_API_KEY")
+
+class GenerateReplyRequest(BaseModel):
+    email_text: str = Field(..., example="Oggetto: Follow-up progetto “Delta”...")
+    groq_api_key: str = Field(..., example="YOUR_GROQ_API_KEY")
+    language: Literal["Italian", "English"] = Field("Italian", examples=["Italian", "English"])
+    length: str = Field("Auto", examples=["Short", "Medium", "Long", "Auto"])
+    style: str = Field("Professional", examples=["Professional", "Casual", "Formal", "Informal"])
+    tone: str = Field("Friendly", examples=["Friendly", "Neutral", "Urgent", "Empathetic"])
+    emoji: str = Field("Auto", examples=["Auto", "None", "Occasional", "Frequent"])
+
+class GeneratedReplyData(BaseModel):
+    id: Optional[PyObjectId] = Field(alias="_id", default=None)
+    original_email_text: str
+    generated_reply_text: str
+    language: str
+    length: str
+    style: str
+    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)
+        if "_id" in data and isinstance(data["_id"], ObjectId):
+            data["_id"] = str(data["_id"])
+        return data
+
+# --- Query Models for GET Endpoints ---
+class ExtractedEmailQuery(BaseModel):
+    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 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]:
+    pattern = r'```json\s*(.*?)\s*```'
+    matches = re.findall(pattern, text, re.DOTALL)
+    if matches:
+        return matches[-1].strip()
+    match = re.search(r'\{.*\}', text, re.DOTALL)
+    if match:
+        return match.group(0)
+    return None
+
+def parse_date(date_str: Optional[str], current_date: date) -> Optional[date]: # Allow None to pass through
+    if not date_str: return None # If input is None (e.g. optional end_date), 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:
+        # For "appointments" "start_date", you assumed "today" if parsing failed or not present.
+        # For "end_date" it was optional. We need to be consistent.
+        # Given the original normalize_llm_output, start_date defaulted to today, end_date was optional.
+        # This parse_date is more general. The default handling should be in normalize_llm_output.
+        return current_date # Fallback, or raise error, or return None depending on strictness
+
+def normalize_llm_output(data: dict, current_date: date, original_email_text: str) -> ExtractedData:
+    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 = []
+    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", []):
+        start_date_val = parse_date(a.get("start_date", "today"), current_date) or current_date # Default to current_date if parse_date returns None
+        end_date_val = parse_date(a.get("end_date"), current_date) # parse_date can return None if end_date is not provided or invalid
+
+        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", []):
+        due_date_val = parse_date(t.get("due_date", "today"), current_date) or current_date # Default to 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) ----------------------
+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.")
+    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()
+    # Ensure your full, detailed prompt is used here
+    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:
+        # print(f"DEBUG: Invoking LLM with email_text length: {len(email_text)} and current_date: {current_date}")
+        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
+        # print(f"DEBUG: Raw LLM output:\n{llm_output_str[:500]}...")
+
+        json_str = extract_last_json_block(llm_output_str)
+        # print(f"DEBUG: Extracted JSON string:\n{json_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)
+        # print(f"DEBUG: Parsed JSON data: {json.dumps(json_data, indent=2)}")
+
+        extracted_data = normalize_llm_output(json_data, current_date, email_text)
+        # print("DEBUG: Data normalized successfully.")
+        return extracted_data
+    except json.JSONDecodeError as e:
+        # print(f"ERROR: JSON Decode Error: {e}")
+        # print(f"ERROR: LLM response that caused error:\n{llm_output_str}")
+        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."
+    llm = ChatGroq(model="meta-llama/llama-4-scout-17b-16e-instruct", temperature=0.7, max_tokens=800, groq_api_key=api_key)
+    # Ensure your full, detailed prompt is used here
+    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
+    output = chain.invoke({"email": email_text, "language": language, "length": length, "style": style, "tone": tone, "emoji": emoji})
+    return output.content.strip()
+
+# --- Batching and Caching Configuration ---
+MAX_BATCH_SIZE = 20
+BATCH_TIMEOUT = 0.5  # seconds
+
+reply_request_queue: List[Tuple[GenerateReplyRequest, asyncio.Future, float]] = []
+reply_queue_lock = asyncio.Lock()
+reply_queue_condition = asyncio.Condition(lock=reply_queue_lock)
+batch_processor_task: Optional[asyncio.Task] = None
+
+
+# --- Batch Processor and Handler ---
+async def handle_single_reply_request(request_data: GenerateReplyRequest, future: asyncio.Future):
+    """Handles a single request: checks cache, calls LLM, stores result, and sets future."""
+    if future.cancelled():
+        return
+    try:
+        if generated_replies_collection is None:
+            raise HTTPException(status_code=503, detail="Database service not available for caching/storage.")
+
+        cache_query = {
+            "original_email_text": request_data.email_text,
+            "language": request_data.language,
+            "length": request_data.length,
+            "style": request_data.style,
+            "tone": request_data.tone,
+            "emoji": request_data.emoji,
+        }
+        cached_reply_doc = await asyncio.to_thread(generated_replies_collection.find_one, cache_query)
+
+        if cached_reply_doc:
+            response = {
+                "reply": cached_reply_doc["generated_reply_text"],
+                "stored_id": str(cached_reply_doc["_id"]),
+                "cached": True
+            }
+            if not future.done(): future.set_result(response)
+            return
+
+        reply_content = await asyncio.to_thread(
+            _generate_response_internal,
+            request_data.email_text,
+            request_data.groq_api_key,
+            request_data.language,
+            request_data.length,
+            request_data.style,
+            request_data.tone,
+            request_data.emoji
+        )
+
+        reply_data_to_store = GeneratedReplyData(
+            original_email_text=request_data.email_text,
+            generated_reply_text=reply_content,
+            language=request_data.language,
+            length=request_data.length,
+            style=request_data.style,
+            tone=request_data.tone,
+            emoji=request_data.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)
+
+        final_response = {
+            "reply": reply_content,
+            "stored_id": stored_id,
+            "cached": False
+        }
+        if not future.done(): future.set_result(final_response)
+
+    except Exception as e:
+        traceback.print_exc()
+        if not future.done():
+            future.set_exception(e)
+
+async def process_reply_batches():
+    """Continuously processes requests from the reply_request_queue in batches."""
+    global reply_request_queue
+    while True:
+        batch_to_fire: List[Tuple[GenerateReplyRequest, asyncio.Future]] = []
+        async with reply_queue_condition:
+            if not reply_request_queue:
+                await reply_queue_condition.wait()
+                if not reply_request_queue:
+                    continue
+
+            now = asyncio.get_event_loop().time()
+            oldest_item_timestamp = reply_request_queue[0][2]
+
+            if len(reply_request_queue) >= MAX_BATCH_SIZE or \
+               (now - oldest_item_timestamp >= BATCH_TIMEOUT):
+                num_to_take = min(len(reply_request_queue), MAX_BATCH_SIZE)
+                for _ in range(num_to_take):
+                    req, fut, _ = reply_request_queue.pop(0)
+                    batch_to_fire.append((req, fut))
+            else:
+                time_to_wait = BATCH_TIMEOUT - (now - oldest_item_timestamp)
+                try:
+                    await asyncio.wait_for(reply_queue_condition.wait(), timeout=time_to_wait)
+                except asyncio.TimeoutError:
+                    pass # Loop will re-evaluate
+
+        if batch_to_fire:
+            tasks = [handle_single_reply_request(req_data, fut) for req_data, fut in batch_to_fire]
+            await asyncio.gather(*tasks)
+        else:
+             await asyncio.sleep(0.001)
+
+
+# ---------------------- 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, batching, and caching.",
+    version="1.1.0", # Incremented version
+    docs_url="/",
+    redoc_url="/redoc"
+)
+
+# --- Global Exception Handler ---
+@app.exception_handler(StarletteHTTPException)
+async def custom_http_exception_handler_wrapper(request, exc):
+    return await http_exception_handler(request, exc)
+
+@app.exception_handler(Exception)
+async def global_exception_handler_wrapper(request, exc):
+    print(f"Unhandled exception caught by global handler for request: {request.url}")
+    traceback.print_exc()
+    # Ensure it returns a valid FastAPI response
+    return Response(content=json.dumps({"detail": f"Internal Server Error: {str(exc)}"}), status_code=500, media_type="application/json")
+
+
+# --- FastAPI Event Handlers for MongoDB & Batch Processor ---
+@app.on_event("startup")
+async def startup_event():
+    global client, db, extracted_emails_collection, generated_replies_collection, batch_processor_task
+    try:
+        client = MongoClient(MONGO_URI, serverSelectionTimeoutMS=5000)
+        client.admin.command('ping')
+        db = client[DB_NAME]
+        extracted_emails_collection = db[EXTRACTED_EMAILS_COLLECTION]
+        generated_replies_collection = db[GENERATED_REPLIES_COLLECTION]
+        print(f"Successfully connected to MongoDB: {DB_NAME}")
+
+        if batch_processor_task is None:
+            loop = asyncio.get_event_loop()
+            batch_processor_task = loop.create_task(process_reply_batches())
+            print("Batch processor task for replies started.")
+
+    except (ConnectionFailure, OperationFailure) as e:
+        print(f"ERROR: MongoDB Connection/Operation Failure: {e}")
+        client = None
+        db = None
+        extracted_emails_collection = None
+        generated_replies_collection = None
+    except Exception as e:
+        print(f"ERROR: An unexpected error occurred during MongoDB connection or batch startup: {e}")
+        traceback.print_exc()
+        client = None
+        db = None
+        extracted_emails_collection = None
+        generated_replies_collection = None
+    finally:
+        # Corrected condition for checking client and db
+        if client is not None and db is not None:
+            try:
+                client.admin.command('ping')
+            except Exception:
+                print("MongoDB ping failed after initial connection attempt during finally block.")
+                client = None; db = None; extracted_emails_collection = None; generated_replies_collection = None
+        else:
+            print("MongoDB client or db object is None after connection attempt in startup.")
+            if client is None or db is None: # Ensure all are None if one is
+                client = None; db = None; extracted_emails_collection = None; generated_replies_collection = None
+        print("FastAPI app starting up. MongoDB client & Batch Processor initialization attempted.")
+
+
+@app.on_event("shutdown")
+async def shutdown_event():
+    global client, batch_processor_task
+    if batch_processor_task:
+        batch_processor_task.cancel()
+        try:
+            await batch_processor_task
+        except asyncio.CancelledError:
+            print("Batch processor task for replies cancelled.")
+        except Exception as e:
+            print(f"Error during batch processor task shutdown: {e}")
+            traceback.print_exc()
+        batch_processor_task = None
+
+    if client:
+        client.close()
+        print("FastAPI app shutting down. MongoDB client closed.")
+
+
+@app.get("/health", summary="Health Check")
+async def health_check():
+    db_status = "MongoDB not connected. Check server startup logs."
+    db_ok = False
+    if client is not None and db is not None: # Corrected check
+        try:
+            db.list_collection_names()
+            db_status = "MongoDB connection OK."
+            db_ok = True
+        except Exception as e:
+            db_status = f"MongoDB connection error: {e}"
+
+    batch_processor_status = "Batch processor not running or state unknown."
+    if batch_processor_task is not None :
+        if not batch_processor_task.done():
+             batch_processor_status = "Batch processor is running."
+        else:
+             batch_processor_status = "Batch processor task is done (may have completed or errored)."
+    
+    if db_ok:
+         return {"status": "ok", "message": "Email Assistant API is up.", "database": db_status, "batch_processor": batch_processor_status}
+    else:
+        # Return a JSON response for HTTPException as well for consistency
+        raise HTTPException(
+            status_code=503,
+            detail={"message": "Service unavailable.", "database": db_status, "batch_processor": batch_processor_status}
+        )
+
+
+@app.post("/extract-data", response_model=ExtractedData, summary="Extract structured data from an email and store in MongoDB")
+async def extract_email_data(request: ProcessEmailRequest):
+    if extracted_emails_collection is None:
+        raise HTTPException(status_code=503, detail="MongoDB not available for extracted_emails.")
+    try:
+        current_date_val = date.today()
+        extracted_data = await asyncio.to_thread(
+            _process_email_internal, request.email_text, request.groq_api_key, current_date_val
+        )
+        extracted_data_dict = extracted_data.model_dump(by_alias=True, exclude_none=True)
+        for appt in extracted_data_dict.get('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())
+        for task_item in extracted_data_dict.get('tasks', []):
+            if isinstance(task_item.get('due_date'), date): task_item['due_date'] = datetime.combine(task_item['due_date'], datetime.min.time())
+
+        result = await asyncio.to_thread(extracted_emails_collection.insert_one, extracted_data_dict)
+        # Pydantic model expects string ID, convert from ObjectId before assigning if needed
+        # However, PyObjectId should handle this if result.inserted_id is ObjectId
+        extracted_data.id = str(result.inserted_id) if isinstance(result.inserted_id, ObjectId) else result.inserted_id
+        return extracted_data
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        traceback.print_exc()
+        raise HTTPException(status_code=500, detail=f"Internal server error during data extraction: {e}")
+
+
+@app.post("/extract-data-excel", summary="Extract structured data and download as Excel (also stores in MongoDB)")
+async def extract_email_data_excel(request: ProcessEmailRequest):
+    raise HTTPException(status_code=501, detail="Excel functionality is currently disabled.")
+
+
+@app.post("/generate-reply", summary="Generate a smart reply to an email (batched & cached)")
+async def generate_email_reply(request: GenerateReplyRequest):
+    if generated_replies_collection is None or batch_processor_task is None or reply_queue_condition is None:
+        raise HTTPException(status_code=503, detail="Reply generation service not fully initialized. Check server logs.")
+
+    future = asyncio.Future()
+    current_time = asyncio.get_event_loop().time()
+
+    async with reply_queue_condition:
+        reply_request_queue.append((request, future, current_time))
+        reply_queue_condition.notify()
+
+    try:
+        client_timeout = BATCH_TIMEOUT + 10.0
+        result = await asyncio.wait_for(future, timeout=client_timeout)
+        return result
+    except asyncio.TimeoutError:
+        if not future.done():
+            future.cancel()
+        raise HTTPException(status_code=504, detail=f"Request timed out after {client_timeout}s waiting for batch processing.")
+    except Exception as e:
+        if isinstance(e, HTTPException):
+            raise e
+        traceback.print_exc()
+        raise HTTPException(status_code=500, detail=f"Error processing your reply request: {str(e)}")
 
-from utils.generate_face_shapes import generate_facial_data_from_bytes
-from utils.model.model import load_model
-from utils.config import config
 
-app = flask.Flask(__name__)
+@app.get("/query-extracted-emails", response_model=List[ExtractedData], summary="Query extracted emails from MongoDB")
+async def query_extracted_emails_endpoint(query_params: ExtractedEmailQuery = Depends()):
+    if extracted_emails_collection is None:
+        raise HTTPException(status_code=503, detail="MongoDB not available for extracted_emails.")
+    mongo_query: Dict[str, Any] = {}
+    if query_params.contact_name: mongo_query["contacts.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"}
 
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-print("Activated device:", device)
+    if query_params.from_date or query_params.to_date:
+        date_query: Dict[str, datetime] = {}
+        if query_params.from_date: date_query["$gte"] = datetime.combine(query_params.from_date, datetime.min.time())
+        if query_params.to_date: date_query["$lt"] = datetime.combine(query_params.to_date + timedelta(days=1), datetime.min.time())
+        if date_query : mongo_query["processed_at"] = date_query
 
-model_path = 'utils/model/model.pth'
-blendshape_model = load_model(model_path, config, device)
+    try:
+        cursor = extracted_emails_collection.find(mongo_query).sort("processed_at", -1).limit(query_params.limit)
+        extracted_docs_raw = await asyncio.to_thread(list, cursor)
+        
+        results = []
+        for doc_raw in extracted_docs_raw:
+            # Convert _id to string for Pydantic model if it's an ObjectId
+            if isinstance(doc_raw.get("_id"), ObjectId):
+                doc_raw["_id"] = str(doc_raw["_id"])
+            
+            # Convert datetime objects back to date objects for Pydantic model fields that are `date`
+            if 'appointments' in doc_raw:
+                for appt in doc_raw['appointments']:
+                    if isinstance(appt.get('start_date'), datetime): appt['start_date'] = appt['start_date'].date()
+                    if isinstance(appt.get('end_date'), datetime): appt['end_date'] = appt['end_date'].date()
+            if 'tasks' in doc_raw:
+                for task_item in doc_raw['tasks']:
+                    if isinstance(task_item.get('due_date'), datetime): task_item['due_date'] = task_item['due_date'].date()
+            results.append(ExtractedData(**doc_raw))
+        return results
+    except Exception as e:
+        traceback.print_exc()
+        raise HTTPException(status_code=500, detail=f"Error querying extracted emails: {e}")
 
-@app.route('/audio_to_blendshapes', methods=['POST'])
-def audio_to_blendshapes_route():
-    audio_bytes = request.data
-    generated_facial_data = generate_facial_data_from_bytes(audio_bytes, blendshape_model, device, config)
-    generated_facial_data_list = generated_facial_data.tolist() if isinstance(generated_facial_data, np.ndarray) else generated_facial_data
 
-    return jsonify({'blendshapes': generated_facial_data_list})
+@app.get("/query-generated-replies", response_model=List[GeneratedReplyData], summary="Query generated replies from MongoDB")
+async def query_generated_replies_endpoint(query_params: GeneratedReplyQuery = Depends()):
+    if generated_replies_collection is None:
+        raise HTTPException(status_code=503, detail="MongoDB not available for generated_replies.")
+    mongo_query: Dict[str, Any] = {}
+    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.from_date or query_params.to_date:
+        date_query: Dict[str, datetime] = {}
+        if query_params.from_date: date_query["$gte"] = datetime.combine(query_params.from_date, datetime.min.time())
+        if query_params.to_date: date_query["$lt"] = datetime.combine(query_params.to_date + timedelta(days=1), datetime.min.time())
+        if date_query: mongo_query["generated_at"] = date_query
+        
+    try:
+        cursor = generated_replies_collection.find(mongo_query).sort("generated_at", -1).limit(query_params.limit)
+        generated_docs_raw = await asyncio.to_thread(list, cursor)
+        results = []
+        for doc_raw in generated_docs_raw:
+            if isinstance(doc_raw.get("_id"), ObjectId):
+                doc_raw["_id"] = str(doc_raw["_id"])
+            results.append(GeneratedReplyData(**doc_raw))
+        return results
+    except Exception as e:
+        traceback.print_exc()
+        raise HTTPException(status_code=500, detail=f"Error querying generated replies: {e}")