.gitignore

@@ -128,6 +128,7 @@ celerybeat.pid
 *.sage.py
 
 # Environments
+.env
 .venv
 env/
 venv/

Makefile

@@ -10,7 +10,7 @@ install:
 	$(VENV_DIR)/bin/pip install -r $(REQ_FILE)
 
 install-dev:
-	$(VENV_DIR)/bin/pip install -r requirements-dev.txt
+	$(VENV_DIR)/bin/pip install -r $(REQ_FILE) -r requirements-dev.txt
 
 start:
 	docker compose down -v db

README.md

@@ -4,7 +4,7 @@ Welcome to this pair programming excercies for Axiomatic AI!
 
 This repository is a very minimal application that we have put together to test new candidates. The repository resembles how our codebase is structured but in a very much "cut down" fashion. As you will see, the application takes pieces from "clean architecture" standards and slims it down. It is by no means perfect (and neither is our real code) - if you see areas to improve, please mention them in the interview!
 
-During the pair programming excercise, you will be given some features, and be expected to implement them e2e. We are there to guide and help you! - answering any questions you may need, or even helping with syntax.
+During the pair programming excercise, you will be given some features, and be expected to implement them e2e. We are there to guide and help you! We do not expect you to achieve this goal, answering any questions you may need, or even helping with syntax.
 
 Please make sure to follow the `Dev Setup` section before the day of the interview so that we can help within any problems you may have.
 
@@ -39,7 +39,7 @@ Go to `http://localhost:8080/docs` to see the Swagger UI
 
 There is a postgres database that powers the application. For simplicity, it is cleaned and re-seeded when you run `make start`. This means you do not have think about DB migrations. You can run `make clean-db` if you need to clean it manually.
 
-## VSCode Setup
+## IDE Setup
 
 If you open the project directly within vscode, the debugger has been setup for you.
 

backend/.env

@@ -1,6 +0,0 @@
-ENVIRONMENT=development
-PG_USER=developer
-PG_PASSWORD=password
-PG_DB_NAME=dev
-PG_HOST=localhost
-PG_PORT=5432

backend/app/ai_models/california.py

@@ -1,33 +0,0 @@
-from sklearn.datasets import fetch_california_housing
-from sklearn.linear_model import LinearRegression
-from sklearn.model_selection import train_test_split
-import joblib
-import pandas as pd
-
-# Load dataset
-data = fetch_california_housing()
-df = pd.DataFrame(data.data, columns=data.feature_names)
-df['target'] = data.target  # in 100k USD
-
-# Engineer features
-df['square_feet'] = df['AveRooms'] * 350  
-df['bedrooms'] = df['AveBedrms']
-df['bathrooms'] = df['AveRooms'] * 0.2 
-
-# Clean bathrooms
-df['bathrooms'] = df['bathrooms'].clip(lower=1)
-
-X = df[['square_feet', 'bedrooms', 'bathrooms']]
-y = df['target']
-
-# Train/test split
-X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
-
-# Train model
-model = LinearRegression()
-model.fit(X_train, y_train)
-
-# Need to be tested of course..: )
-
-# Save model
-joblib.dump(model, 'price_predictor.pkl')

backend/app/bots/adaptive_cards.py

@@ -1,47 +0,0 @@
-from typing import Dict, Any
-from langchain.chat_models import ChatOpenAI
-from langchain.prompts import ChatPromptTemplate
-from langchain.chains import LLMChain
-import asyncio
-
-class AdaptiveCards:
-    def __init__(self):
-        self.llm = ChatOpenAI(temperature=0)
-        self.prompt = ChatPromptTemplate.from_template("""
-            You are a Microsoft Adaptive Card generator. Given a data schema and known values,
-            generate an Adaptive Card (v1.3) that asks the user only for missing fields.
-
-            Use this schema: https://adaptivecards.io/schemas/adaptive-card.json
-            Respond only with valid Adaptive Card JSON. Do not include explanations.
-            Always include isRequired, and errorMessage in the schema.
-            Always include a submit button at the bottom of the card as defined in the schema.
-
-            ### Schema:
-            {schema}
-
-            ### Known values:
-            {known_values}
-
-            """)
-        self.chain = LLMChain(llm=self.llm, prompt=self.prompt)
-
-    async def generate_card(self, schema: Dict[str, Any], known_values: Dict[str, Any]) -> Dict[str, Any]:
-        loop = asyncio.get_running_loop()
-        return await loop.run_in_executor(None, self.chain.run, {
-            "schema": schema,
-            "known_values": known_values
-        })
-
-    def create_welcome_card(self):
-        """Create a welcome card"""
-        return {
-            "type": "AdaptiveCard",
-            "body": [
-                {
-                    "type": "TextBlock",
-                    "text": "Welcome to the Housing Bot!",
-                    "size": "large"
-                }
-            ],
-            "version": "1.0"
-        }

backend/app/bots/dayta.py

@@ -1,115 +0,0 @@
-import json
-from typing import Annotated
-from fastapi import Depends
-from langchain.chat_models import ChatOpenAI
-from botbuilder.core import ActivityHandler, TurnContext
-from botbuilder.schema import Activity, Attachment, ActivityTypes
-import asyncio
-
-from pydantic import ValidationError
-
-from backend.app.bots.adaptive_cards import AdaptiveCards
-from backend.app.bots.intent_detector import IntentDetector
-from backend.app.bots.slot_filler import SlotFiller
-from backend.app.dtos.house.house_features import HouseFeatures
-from backend.app.services.house_price_predictor import HousePricePredictor
-
-class Dayta(ActivityHandler):
-    def __init__(
-            self, 
-            intent_detector: Annotated[IntentDetector, Depends()], 
-            card_bot: Annotated[AdaptiveCards, Depends()], 
-            slot_filler: Annotated[SlotFiller, Depends()],
-            price_predictor: Annotated[HousePricePredictor, Depends()],):
-        
-        self.intent_detector = intent_detector
-        self.card_bot = card_bot
-        self.slot_filler = slot_filler
-        self.price_predictor = price_predictor
-        self.chat_llm = ChatOpenAI(temperature=0.7)
-        self.user_sessions = {}
-
-    async def on_message_activity(self, turn_context: TurnContext):
-        user_message = turn_context.activity.text
-        user_id = turn_context.activity.from_property.id
-        submitted_values = turn_context.activity.value
-
-        known_values = self.user_sessions.get(user_id, {})
-        schema = HouseFeatures.model_json_schema()
-        #required_fields = list(HouseFeatures.model_fields.keys())
-        required_fields = [
-            name for name, field in HouseFeatures.model_fields.items()
-            if field.is_required()
-        ]
-        print(f"required_fields: {required_fields}")
-        
-        # Update known values
-        if submitted_values is not None:
-            known_values.update(submitted_values)
-        else:
-            extracted = await self.slot_filler.extract_slots(schema, user_message)
-            known_values.update(extracted)
-
-        self.user_sessions[user_id] = known_values
-
-        # Detect intent only if message-based
-        if not submitted_values:
-            intent = await self.intent_detector.detect_intent(user_message)
-            if intent.strip().lower() in ("unknown", ""):
-                response = await asyncio.get_event_loop().run_in_executor(
-                    None,
-                    lambda: self.chat_llm.predict(f"The user said: '{user_message}'. Respond helpfully.")
-                )
-                await turn_context.send_activity(response)
-                return
-
-        # Delegate to common logic
-        await self._handle_collected_data(turn_context, user_id, known_values, required_fields, schema)
-
-    async def _handle_collected_data(
-        self,
-        turn_context: TurnContext,
-        user_id: str,
-        known_values: dict,
-        required_fields: list[str],
-        full_schema: dict
-    ):
-        missing_fields = [f for f in required_fields if f not in known_values]
-        print(f"Missing fields: {missing_fields}")
-
-        if not missing_fields:
-           try:
-             features = HouseFeatures(**known_values)
-             price = self.price_predictor.predict(features)
-             await turn_context.send_activity(f"The estimated price of the house is ${price:.2f}")
-             del self.user_sessions[user_id]
-             return
-           except ValidationError as e:
-            await turn_context.send_activity(f"Validation failed: {e}")
-            return
-      
-
-        # Generate adaptive card for missing fields
-        filtered_schema = {
-            **full_schema,
-            "properties": {
-                k: v for k, v in full_schema["properties"].items() if k in missing_fields
-            },
-            "required": missing_fields
-        }
-
-        card_json = await self.card_bot.generate_card(filtered_schema, known_values)
-        if isinstance(card_json, str):
-            card_json = json.loads(card_json)
-        print(f"card_json: {card_json}")
-        await turn_context.send_activity(
-            Activity(
-                type=ActivityTypes.message,
-                attachments=[
-                    Attachment(
-                        content_type="application/vnd.microsoft.card.adaptive",
-                        content=card_json
-                    )
-                ]
-            )
-        )

backend/app/bots/intent_detector.py

@@ -1,23 +0,0 @@
-from langchain.chat_models import ChatOpenAI
-from langchain.prompts import ChatPromptTemplate
-from langchain.chains import LLMChain
-import asyncio
-
-class IntentDetector:
-    def __init__(self, temperature: float = 0.0):
-        self.llm = ChatOpenAI(temperature=temperature)
-        self.prompt = ChatPromptTemplate.from_template("""
-        You are an intent detection bot. Classify the user input into one of the following intents:
-
-        - Information about house prices
-        - unknown
-
-        If you're unsure, respond with `unknown`.
-
-        User: {message}
-        Intent:""")
-        self.chain = LLMChain(llm=self.llm, prompt=self.prompt)
-
-    async def detect_intent(self, message: str) -> str:
-        loop = asyncio.get_running_loop()
-        return await loop.run_in_executor(None, self.chain.run, {"message": message})

backend/app/bots/slot_filler.py

@@ -1,31 +0,0 @@
-from langchain.chat_models import ChatOpenAI
-from langchain.prompts import ChatPromptTemplate
-from langchain.chains import LLMChain
-from typing import Dict, Any
-import asyncio
-
-class SlotFiller:
-    def __init__(self):
-        self.llm = ChatOpenAI(temperature=0)
-        self.prompt = ChatPromptTemplate.from_template("""
-            You are a helpful assistant. Given a message and a schema, extract all known values.
-
-            Only return a JSON object containing the extracted values and no extra text.
-
-            Schema: {schema}
-            Message: {message}
-            """)
-        self.chain = LLMChain(llm=self.llm, prompt=self.prompt)
-
-    async def extract_slots(self, schema: Dict[str, Any], message: str) -> Dict[str, Any]:
-        loop = asyncio.get_event_loop()
-        result = await loop.run_in_executor(None, self.chain.run, {
-            "schema": schema,
-            "message": message
-        })
-
-        import json
-        try:
-            return json.loads(result)
-        except Exception:
-            return {}

backend/app/dtos/house/house_features.py

@@ -1,8 +0,0 @@
-from pydantic import BaseModel, Field
-from typing import Optional
-
-class HouseFeatures(BaseModel):
-    square_feet: float = Field(..., description="Total square feet of the house")
-    bedrooms: int = Field(..., description="Number of bedrooms")
-    bathrooms: float = Field(..., description="Number of bathrooms")
-    number_of_floors: Optional[int] = Field(default=None, description="Number of floors")

backend/app/dtos/house/house_predict_request.py

@@ -1,7 +0,0 @@
-from pydantic import BaseModel
-
-
-class HousePricePredictionRequest(BaseModel):
-    square_feet: float
-    bedrooms: int
-    bathrooms: float

backend/app/dtos/house/house_predict_response.py

@@ -1,5 +0,0 @@
-from pydantic import BaseModel
-
-
-class HousePricePredictionResponse(BaseModel):
-    predicted_price: float

backend/app/dtos/house/houses_list_response.py

@@ -1,6 +0,0 @@
-from pydantic import BaseModel
-
-from backend.app.dtos.house.house_response import HouseResponse
-
-class HousesListResponse(BaseModel):
-    houses: list[HouseResponse]

backend/app/dtos/houses_list_response.py

@@ -8,3 +8,7 @@ class HouseResponse(BaseModel):
     city: str
     country: str
     price: float
+
+
+class HousesListResponse(BaseModel):
+    houses: list[HouseResponse]

backend/app/dtos/user/user_list_response.py

@@ -1,6 +0,0 @@
-from pydantic import BaseModel
-
-from backend.app.dtos.user.user_response import UserResponse
-
-class UserListResponse(BaseModel):
-    users: list[UserResponse]

backend/app/dtos/user/user_response.py

@@ -1,6 +0,0 @@
-from pydantic import BaseModel
-
-
-class UserResponse(BaseModel):
-    id: str
-    email: str

backend/app/factories/bot_factory.py

@@ -1,33 +0,0 @@
-from typing import Dict
-from backend.app.bots.dayta import Dayta
-from backend.app.bots.intent_detector import IntentDetector
-from backend.app.bots.slot_filler import SlotFiller
-from backend.app.bots.adaptive_cards import AdaptiveCards
-from backend.app.services.house_price_predictor import HousePricePredictor
-from botbuilder.core import BotFrameworkAdapter, BotFrameworkAdapterSettings
-
-class BotFactory:
-    def __init__(self):
-        self._bots: Dict[str, object] = {}
-        self.adapter_settings = BotFrameworkAdapterSettings(app_id="", app_password="")
-        self.adapter = BotFrameworkAdapter(self.adapter_settings)
-        
-
-        # Shared services
-        self.intent_detector = IntentDetector()
-        self.slot_filler = SlotFiller()
-        self.card_bot = AdaptiveCards()
-        self.price_predictor = HousePricePredictor()
-        # Register all bots
-        self._bots["dayta"] = Dayta(
-            intent_detector=self.intent_detector,
-            card_bot=self.card_bot,
-            slot_filler=self.slot_filler,
-            price_predictor=self.price_predictor
-        )
-
-    def get_bot(self, name: str):
-        return self._bots.get(name)
-
-    def get_adapter(self):
-        return self.adapter

backend/app/main.py

@@ -7,13 +7,14 @@ from .middleware.authenticate import authenticate
 from .providers.db_provider import create_db_and_tables
 from .routers.houses import router as houses_router
 from .routers.owners import router as owners_router
-from .routers.direct_line import router as direct_line_router
-from .routers.bot import router as bot_router
+
+
 @asynccontextmanager
 async def lifespan(_app: FastAPI):
     create_db_and_tables()
     yield
 
+
 app = FastAPI(
     title="Fair Housing API",
     description="Provides access to core functionality for the fair housing platform.",
@@ -32,5 +33,3 @@ app.add_middleware(
 
 app.include_router(houses_router, prefix="/houses", tags=["houses"])
 app.include_router(owners_router, prefix="/owners", tags=["owners"])
-app.include_router(bot_router, tags=["bot"])
-app.include_router(direct_line_router, tags=["directline"])

backend/app/models/house.py

@@ -1,11 +1,6 @@
-from typing import Optional, TYPE_CHECKING  
 from uuid import UUID, uuid4
 
-from sqlmodel import Field, Relationship, SQLModel
-
-if TYPE_CHECKING:
-    from backend.app.models.owner import Owner
-
+from sqlmodel import Field, SQLModel
 
 
 class House(SQLModel, table=True):
@@ -19,4 +14,3 @@ class House(SQLModel, table=True):
     square_feet: float = Field()
     bedrooms: int = Field()
     bathrooms: float = Field()
-    owner: Optional["Owner"] = Relationship(back_populates="houses")

backend/app/models/owner.py

@@ -1,17 +1,8 @@
-from typing import Optional, TYPE_CHECKING
 from uuid import UUID, uuid4
 
-from sqlmodel import Field, Relationship, SQLModel
-
-if TYPE_CHECKING:
-    from backend.app.models.house import House
-    from backend.app.models.user import User
+from sqlmodel import Field, SQLModel
 
 
 class Owner(SQLModel, table=True):
     id: UUID = Field(default_factory=uuid4, primary_key=True)
     user_id: UUID = Field(foreign_key="user.id", unique=True)
-    
-    # Relationship
-    houses: list["House"] = Relationship(back_populates="owner")
-    user: Optional["User"] = Relationship(back_populates="owner")

backend/app/models/user.py

@@ -1,16 +1,9 @@
-from typing import Optional, TYPE_CHECKING
 from uuid import UUID, uuid4
 
-from sqlmodel import Field, Relationship, SQLModel
-
-if TYPE_CHECKING:
-    from backend.app.models.owner import Owner
+from sqlmodel import Field, SQLModel
 
 
 class User(SQLModel, table=True):
     id: UUID = Field(default_factory=lambda: uuid4(), primary_key=True)
     email: str = Field(unique=True, nullable=False)
     password_hash: str = Field(nullable=False)
-
-    # Relationships
-    owner: Optional["Owner"] = Relationship(back_populates="user")

backend/app/repositories/house_repository.py

@@ -1,30 +1,20 @@
-from typing import Annotated, Literal
+from typing import Annotated
 from uuid import UUID
 
 from fastapi import Depends
 from sqlalchemy.ext.asyncio.session import AsyncSession
-from sqlmodel import asc, desc, select
+from sqlmodel import select
 
 from ..models.house import House
 from ..providers.db_provider import get_session
 
+
 class HouseRepository:
     def __init__(self, session: Annotated[AsyncSession, Depends(get_session)]) -> None:
         self.session = session
 
-    async def get_all(
-        self,
-        limit: int = 100,
-        offset: int = 0,
-        order_by: Literal["PRICE"] = "PRICE",
-        sort_order: Literal["ASC", "DESC"] = "DESC",
-    ) -> list[House]:
-        sorter = desc if sort_order == "DESC" else asc
-
-        statement = (
-            select(House).offset(offset).limit(limit).order_by(sorter(House.price))
-        )
-
+    async def get_all(self, limit: int = 100, offset: int = 0) -> list[House]:
+        statement = select(House).offset(offset).limit(limit)
         result = await self.session.execute(statement)
         return result.scalars().all()
 
@@ -33,11 +23,6 @@ class HouseRepository:
         result = await self.session.execute(statement)
         return result.scalar_one_or_none()
 
-    async def get_by_user_id(self, user_id: UUID):
-        statement = select(House).where(House.owner_user_id == user_id)
-        result = await self.session.execute(statement)
-        return result.scalars().all()
-    
     async def save(self, house: House) -> None:
         """
         Save a house to the database. If a house with that ID already exists, do an upsert.

backend/app/repositories/owner_repository.py

@@ -5,9 +5,6 @@ from fastapi import Depends
 from sqlalchemy.ext.asyncio.session import AsyncSession
 from sqlmodel import select
 
-from backend.app.models.house import House
-from backend.app.models.user import User
-
 from ..models.owner import Owner
 from ..providers.db_provider import get_session
 
@@ -31,25 +28,6 @@ class OwnerRepository:
         result = await self.session.execute(statement)
         return result.scalar_one_or_none()
 
-    async def get_details_by_house_id(self, house_id: UUID):
-        statement = (
-            select(Owner, User)
-            .join(User, Owner.user_id == User.id)
-            .join(House, House.owner_user_id == Owner.user_id)
-            .where(House.id == house_id)
-        )
-        result = await self.session.execute(statement)
-        row = result.first()  
-       
-        if row:
-            owner, user = row
-            return {
-                "owner": owner,
-                "user": user
-            }
-
-        return None
-    
     async def save(self, owner: Owner) -> None:
         """
         Save a owner to the database. If an owner with that ID already exists, do an upsert.

backend/app/routers/bot.py

@@ -1,23 +0,0 @@
-from fastapi import APIRouter, Request, Depends
-from botbuilder.schema import Activity
-from botbuilder.core import TurnContext
-from backend.app.factories.bot_factory import BotFactory
-
-router = APIRouter()
-
-@router.post("/api/messages", response_model=None)
-async def messages(
-    req: Request,
-    bot = Depends(lambda: BotFactory().get_bot("dayta")),
-    adapter = Depends(lambda: BotFactory().get_adapter()    )
-    ):
-    body = await req.json()
-    activity = Activity().deserialize(body)
-
-    async def call_bot_logic(turn_context: TurnContext):
-        await bot.on_turn(turn_context)
-
-    auth_header = req.headers.get("Authorization", "")
-    await adapter.process_activity(activity, auth_header, call_bot_logic)
-
-    return {}  

backend/app/routers/direct_line.py

@@ -1,86 +0,0 @@
-from fastapi import APIRouter, HTTPException
-from typing import Dict, Any
-from uuid import uuid4
-from botbuilder.core import TurnContext
-from botbuilder.schema import Activity, ActivityTypes
-from backend.app.factories.bot_factory import BotFactory
-
-router = APIRouter(prefix="/v3/directline")
-
-# In-memory conversation store
-conversations: Dict[str, Dict[str, Any]] = {}
-# Each conversation will look like:
-# { "activities": [ { id, type, text, from } ], "watermark": int }
-
-@router.post("/conversations")
-async def start_conversation():
-    conversation_id = str(uuid4())
-    conversations[conversation_id] = {
-        "activities": [],
-        "watermark": 0
-    }
-
-    return {
-        "conversationId": conversation_id,
-        "token": "mock-token",  # Optional for dev use
-        "streamUrl": f"/v3/directline/conversations/{conversation_id}/stream"
-    }
-
-@router.get("/conversations/{conversation_id}/activities")
-async def get_activities(conversation_id: str, watermark: int = 0):
-    if conversation_id not in conversations:
-        raise HTTPException(status_code=404, detail="Conversation not found")
-
-    activities = conversations[conversation_id]["activities"]
-    return {
-        "activities": activities[watermark:],
-        "watermark": len(activities)
-    }
-
-@router.post("/conversations/{conversation_id}/activities")
-async def post_activity(conversation_id: str, activity: Dict[str, Any]):
-    if conversation_id not in conversations:
-        raise HTTPException(status_code=404, detail="Conversation not found")
-
-    # Starting with deserializing the activity
-    act = Activity().deserialize(activity)
-
-    #  Store my responses in this list please
-    bot_responses = []
-
-    #Patch TurnContext.send_activity to capture output
-    async def call_bot_logic(turn_context: TurnContext):
-        async def capture_response(response):
-            
-            # If it's a string, wrap it into an Activity
-            if isinstance(response, str):
-                bot_activity = Activity(
-                    type=ActivityTypes.message,
-                    text=response,
-                    from_property={"id": "bot"}
-                )
-            else:
-                bot_activity = response
-
-            bot_responses.append(bot_activity)
-
-        turn_context.send_activity = capture_response
-        await bot.on_turn(turn_context)
-    # 4. Call the adapter with the activity
-    adapter = BotFactory().get_adapter()
-    bot = BotFactory().get_bot("dayta")
-    auth_header = ""  
-
-    await adapter.process_activity(act, auth_header, call_bot_logic)
-
-    # 5. Store bot responses into conversation memory
-    for act in bot_responses:
-        conversations[conversation_id]["activities"].append({
-            "id": str(uuid4()),
-            "type": act.type,
-            "text": act.text,
-            "from": {"id": "bot"},
-            "attachments": [a.serialize() for a in (act.attachments or [])]
-        })
-
-    return { "id": str(uuid4()) }

backend/app/routers/houses.py

@@ -1,20 +1,16 @@
-from typing import Annotated, Literal
+from typing import Annotated
 
 from fastapi import APIRouter, Depends
 
-from backend.app.dtos.house.house_create_request import HouseCreateRequest
-from backend.app.dtos.house.house_create_response import HouseCreateResponse
-from backend.app.dtos.house.house_features import HouseFeatures
-from backend.app.dtos.house.house_predict_request import HousePricePredictionRequest
-from backend.app.dtos.house.house_predict_response import HousePricePredictionResponse
-from backend.app.dtos.house.house_response import HouseResponse
-from backend.app.dtos.house.houses_list_response import HousesListResponse
+from ..dtos.house_create_request import HouseCreateRequest
+from ..dtos.house_create_response import HouseCreateResponse
+from ..dtos.houses_list_response import HouseResponse, HousesListResponse
 from ..models.house import House
 from ..models.owner import Owner
 from ..providers.auth_provider import AuthContext
 from ..repositories.house_repository import HouseRepository
 from ..repositories.owner_repository import OwnerRepository
-from ..services.house_price_predictor import HousePricePredictor
+
 router = APIRouter()
 
 
@@ -52,14 +48,10 @@ async def create_house(
 @router.get("")
 async def get_all_houses(
     house_repository: Annotated[HouseRepository, Depends()],
-    order_by: Literal["PRICE"] = "PRICE",
-    sort_order: Literal["ASC", "DESC"] = "DESC",
     limit: int = 100,
     offset: int = 0,
 ) -> HousesListResponse:
-    all_houses = await house_repository.get_all(
-        offset=offset, limit=limit, order_by=order_by, sort_order=sort_order
-    )
+    all_houses = await house_repository.get_all(offset=offset, limit=limit)
 
     house_responses = [
         HouseResponse(
@@ -72,23 +64,6 @@ async def get_all_houses(
         )
         for house in all_houses
     ]
-
+    print(house_responses)
 
     return HousesListResponse(houses=house_responses)
-
-@router.post("/predict-price")
-async def predict_house_price(
-        body: HouseFeatures,
-        price_predictor: Annotated[HousePricePredictor, Depends()],
-    ) -> HousePricePredictionResponse:
-        """
-        Predict the price of a house based on its features.
-        """
-        predicted_price = await price_predictor.predict_california(
-            square_feet=body.square_feet,
-            bedrooms=body.bedrooms,
-            bathrooms=body.bathrooms
-        )
-        
-        return HousePricePredictionResponse(predicted_price=predicted_price)  
-

backend/app/routers/owners.py

@@ -1,14 +1,15 @@
 from typing import Annotated
 
-from fastapi import APIRouter, Depends, HTTPException
+from fastapi import APIRouter, Depends
 
-from ..dtos.owner.owner_detail_response import OwnerDetailResponse
-from ..dtos.owner.owner_list_response import OwnerListResponse, OwnerResponse
+from ..dtos.owner_detail_response import OwnerDetailResponse
+from ..dtos.owner_list_response import OwnerListResponse, OwnerResponse
 from ..repositories.owner_repository import OwnerRepository
 from ..repositories.user_repository import UserRepository
 
 router = APIRouter()
 
+
 @router.get("")
 async def get_owners(
     owner_repository: Annotated[OwnerRepository, Depends()],
@@ -21,6 +22,7 @@ async def get_owners(
 
     return OwnerListResponse(owners=owners_response)
 
+
 @router.get("/{id}")
 async def get_owner(
     id: str,
@@ -33,22 +35,3 @@ async def get_owner(
     return OwnerDetailResponse(
         id=str(owner.id), user_id=str(owner.user_id), email=user.email
     )
-
-@router.get("/byhouse/{house_id}")
-async def get_owner_by_house_id(
-    house_id: str,
-    owner_repository: Annotated[OwnerRepository, Depends()],
-) -> OwnerDetailResponse:
-    result = await owner_repository.get_details_by_house_id(house_id)
-    
-    if result is None:
-        raise HTTPException(status_code=404, detail="House or owner not found")
-        
-    owner = result["owner"]
-    user = result["user"]
-    
-    return OwnerDetailResponse(
-        id=str(owner.id),
-        user_id=str(owner.user_id),
-        email=str(user.email)
-    )

backend/app/services/house_price_predictor.py

@@ -1,17 +1,16 @@
-import os
-import joblib
-import numpy as np
-
-from backend.app.dtos.house.house_features import HouseFeatures
-
 class HousePricePredictor:
     """
     Mock ML model that predicts house prices.
     In a real scenario, this would load a trained model.
     """
-    def __init__(self):
-        self.model = joblib.load("backend/app/ai_models/price_predictor.pkl")
 
-    def predict(self, features: HouseFeatures) -> float:
-        X = np.array([[features.square_feet, features.bedrooms, features.bathrooms]])
-        return self.model.predict(X)[0] * 100000 
+    async def predict(
+        self, square_feet: float, bedrooms: int, bathrooms: float
+    ) -> float:
+        base_price = square_feet * 200
+        bedroom_value = bedrooms * 25000
+        bathroom_value = bathrooms * 15000
+
+        predicted_price = base_price + bedroom_value + bathroom_value
+
+        return predicted_price

backend/app/services/investor_predictor.py

@@ -1,7 +1,5 @@
 import random
 
-from ..models import User
-
 
 class InvestorPredictor:
     def is_investor(user: User) -> bool:

backend/requirements.txt

@@ -8,5 +8,3 @@ python-dotenv
 pg8000
 asyncpg
 greenlet
-botbuilder-core 
-openai

frontend/.gitignore

@@ -1,24 +0,0 @@
-# Logs
-logs
-*.log
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-pnpm-debug.log*
-lerna-debug.log*
-
-node_modules
-dist
-dist-ssr
-*.local
-
-# Editor directories and files
-.vscode/*
-!.vscode/extensions.json
-.idea
-.DS_Store
-*.suo
-*.ntvs*
-*.njsproj
-*.sln
-*.sw?

frontend/README.md

@@ -1,54 +0,0 @@
-# React + TypeScript + Vite
-
-This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
-
-Currently, two official plugins are available:
-
-- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react/README.md) uses [Babel](https://babeljs.io/) for Fast Refresh
-- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh
-
-## Expanding the ESLint configuration
-
-If you are developing a production application, we recommend updating the configuration to enable type-aware lint rules:
-
-```js
-export default tseslint.config({
-  extends: [
-    // Remove ...tseslint.configs.recommended and replace with this
-    ...tseslint.configs.recommendedTypeChecked,
-    // Alternatively, use this for stricter rules
-    ...tseslint.configs.strictTypeChecked,
-    // Optionally, add this for stylistic rules
-    ...tseslint.configs.stylisticTypeChecked,
-  ],
-  languageOptions: {
-    // other options...
-    parserOptions: {
-      project: ['./tsconfig.node.json', './tsconfig.app.json'],
-      tsconfigRootDir: import.meta.dirname,
-    },
-  },
-})
-```
-
-You can also install [eslint-plugin-react-x](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-x) and [eslint-plugin-react-dom](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-dom) for React-specific lint rules:
-
-```js
-// eslint.config.js
-import reactX from 'eslint-plugin-react-x'
-import reactDom from 'eslint-plugin-react-dom'
-
-export default tseslint.config({
-  plugins: {
-    // Add the react-x and react-dom plugins
-    'react-x': reactX,
-    'react-dom': reactDom,
-  },
-  rules: {
-    // other rules...
-    // Enable its recommended typescript rules
-    ...reactX.configs['recommended-typescript'].rules,
-    ...reactDom.configs.recommended.rules,
-  },
-})
-```

frontend/eslint.config.js

@@ -1,28 +0,0 @@
-import js from '@eslint/js'
-import globals from 'globals'
-import reactHooks from 'eslint-plugin-react-hooks'
-import reactRefresh from 'eslint-plugin-react-refresh'
-import tseslint from 'typescript-eslint'
-
-export default tseslint.config(
-  { ignores: ['dist'] },
-  {
-    extends: [js.configs.recommended, ...tseslint.configs.recommended],
-    files: ['**/*.{ts,tsx}'],
-    languageOptions: {
-      ecmaVersion: 2020,
-      globals: globals.browser,
-    },
-    plugins: {
-      'react-hooks': reactHooks,
-      'react-refresh': reactRefresh,
-    },
-    rules: {
-      ...reactHooks.configs.recommended.rules,
-      'react-refresh/only-export-components': [
-        'warn',
-        { allowConstantExport: true },
-      ],
-    },
-  },
-)

frontend/index.html

@@ -1,13 +0,0 @@
-<!doctype html>
-<html lang="en">
-  <head>
-    <meta charset="UTF-8" />
-    <link rel="icon" type="image/svg+xml" href="/vite.svg" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>Dayta is reality now</title>
-  </head>
-  <body>
-    <div id="root"></div>
-    <script type="module" src="/src/main.tsx"></script>
-  </body>
-</html>

frontend/package.json

@@ -1,32 +0,0 @@
-{
-  "name": "dayta",
-  "private": true,
-  "version": "0.0.0",
-  "type": "module",
-  "scripts": {
-    "dev": "vite",
-    "build": "tsc -b && vite build",
-    "lint": "eslint .",
-    "preview": "vite preview"
-  },
-  "dependencies": {
-    "adaptivecards": "^3.0.5",
-    "axios": "^1.8.4",
-    "botframework-webchat": "^4.18.0",
-    "react": "^19.0.0",
-    "react-dom": "^19.0.0"
-  },
-  "devDependencies": {
-    "@eslint/js": "^9.21.0",
-    "@types/react": "^19.0.10",
-    "@types/react-dom": "^19.0.4",
-    "@vitejs/plugin-react": "^4.3.4",
-    "eslint": "^9.21.0",
-    "eslint-plugin-react-hooks": "^5.1.0",
-    "eslint-plugin-react-refresh": "^0.4.19",
-    "globals": "^15.15.0",
-    "typescript": "~5.7.2",
-    "typescript-eslint": "^8.24.1",
-    "vite": "^6.2.0"
-  }
-}

frontend/public/vite.svg

@@ -1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="31.88" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 257"><defs><linearGradient id="IconifyId1813088fe1fbc01fb466" x1="-.828%" x2="57.636%" y1="7.652%" y2="78.411%"><stop offset="0%" stop-color="#41D1FF"></stop><stop offset="100%" stop-color="#BD34FE"></stop></linearGradient><linearGradient id="IconifyId1813088fe1fbc01fb467" x1="43.376%" x2="50.316%" y1="2.242%" y2="89.03%"><stop offset="0%" stop-color="#FFEA83"></stop><stop offset="8.333%" stop-color="#FFDD35"></stop><stop offset="100%" stop-color="#FFA800"></stop></linearGradient></defs><path fill="url(#IconifyId1813088fe1fbc01fb466)" d="M255.153 37.938L134.897 252.976c-2.483 4.44-8.862 4.466-11.382.048L.875 37.958c-2.746-4.814 1.371-10.646 6.827-9.67l120.385 21.517a6.537 6.537 0 0 0 2.322-.004l117.867-21.483c5.438-.991 9.574 4.796 6.877 9.62Z"></path><path fill="url(#IconifyId1813088fe1fbc01fb467)" d="M185.432.063L96.44 17.501a3.268 3.268 0 0 0-2.634 3.014l-5.474 92.456a3.268 3.268 0 0 0 3.997 3.378l24.777-5.718c2.318-.535 4.413 1.507 3.936 3.838l-7.361 36.047c-.495 2.426 1.782 4.5 4.151 3.78l15.304-4.649c2.372-.72 4.652 1.36 4.15 3.788l-11.698 56.621c-.732 3.542 3.979 5.473 5.943 2.437l1.313-2.028l72.516-144.72c1.215-2.423-.88-5.186-3.54-4.672l-25.505 4.922c-2.396.462-4.435-1.77-3.759-4.114l16.646-57.705c.677-2.35-1.37-4.583-3.769-4.113Z"></path></svg>

frontend/src/App.tsx

@@ -1,13 +0,0 @@
-
-import './App.css'
-import ChatUI from './components/ChatUI'
-
-function App() {
-
-
-  return (
-    <ChatUI />
-  )
-}
-
-export default App

frontend/src/components/ChatUI.tsx

@@ -1,179 +0,0 @@
-import React, { useEffect, useRef, useState } from 'react';
-import * as AdaptiveCards from 'adaptivecards';
-import axios from 'axios';
-
-interface Message {
-  from: string;
-  text?: string;
-  card?: any;
-}
-
-const DIRECT_LINE_BASE = 'https://ax.thedigitalbridge.io/v3/directline'; 
-
-const Chat: React.FC = () => {
-  const [conversationId, setConversationId] = useState<string | null>(null);
-  const [watermark, setWatermark] = useState<number>(0);
-  const [messages, setMessages] = useState<Message[]>([]);
-  const inputRef = useRef<HTMLInputElement>(null);
-  const chatRef = useRef<HTMLDivElement>(null);
-
-  // Start a new conversation
-  useEffect(() => {
-    const startConversation = async () => {
-      try {
-        const res = await axios.post(`${DIRECT_LINE_BASE}/conversations`);
-        setConversationId(res.data.conversationId);
-      } catch (err) {
-        console.error('Failed to start conversation:', err);
-      }
-    };
-    startConversation();
-  }, []);
-  
-
-  // Poll bot messages
-  useEffect(() => {
-    if (!conversationId) return;
-  
-    const interval = setInterval(async () => {
-      try {
-        const res = await axios.get(`${DIRECT_LINE_BASE}/conversations/${conversationId}/activities`, {
-          params: { watermark }
-        });
-  
-        const newActivities = res.data.activities || [];
-        if (newActivities.length) {
-          const newMsgs = newActivities.map((a: any) => ({
-            from: a.from.id,
-            text: a.text,
-            card: a.attachments?.[0]?.content
-          }));
-          setMessages((prev) => [...prev, ...newMsgs]);
-          setWatermark(res.data.watermark);
-        }
-      } catch (err) {
-        console.error('Failed to fetch activities:', err);
-      }
-    }, 1500);
-  
-    return () => clearInterval(interval);
-  }, [conversationId, watermark]);
-  
-
-  // Send message to bot
-  const sendMessage = async (message: string | Record<string, any>) => {
-    if (!conversationId) return;
-  
-    try {
-      const payload =
-        typeof message === 'string'
-          ? { type: 'message', from: { id: 'user' }, text: message }
-          : { type: 'message', from: { id: 'user' }, value: message };
-  
-      await axios.post(`${DIRECT_LINE_BASE}/conversations/${conversationId}/activities`, payload);
-  
-      // Show submitted content in chat
-      if (typeof message === 'string') {
-        setMessages((prev) => [...prev, { from: 'user', text: message }]);
-        if (inputRef.current) inputRef.current.value = '';
-      } 
-      
-    } catch (error) {
-      console.error('Failed to send message:', error);
-  
-      setMessages((prev) => [
-        ...prev,
-        {
-          from: 'bot',
-          text: 'Something went wrong sending your message.',
-        },
-      ]);
-    }
-  };
-  
-
-  // Render Adaptive Card
-  const renderCard = (card: any) => {
-    const container = document.createElement('div');
-    const adaptiveCard = new AdaptiveCards.AdaptiveCard();
-
-    adaptiveCard.onExecuteAction = async (action: AdaptiveCards.Action) => {
-      if (action instanceof AdaptiveCards.SubmitAction) {
-        const inputs = adaptiveCard.getAllInputs();
-        const formData: Record<string, any> = {};
-    
-        inputs.forEach((input) => {
-          if ('id' in input && input.id) {
-            formData[input.id] = input.value;
-          }
-        });
-    
-        // Send the form data as object
-        await sendMessage(JSON.stringify(formData));
-      }
-    };
-
-    adaptiveCard.parse(card);
-    const rendered = adaptiveCard.render();
-    if (rendered) {
-      container.appendChild(rendered);
-    }
-    return container;
-  };
-
-  useEffect(() => {
-    if (chatRef.current) {
-      chatRef.current.scrollTop = chatRef.current.scrollHeight;
-    }
-  }, [messages]);
-
-  return (
-    <div style={{ maxWidth: 600, margin: '0 auto', padding: 16 }}>
-      <h2>Dayta Assistant</h2>
-      <div
-        ref={chatRef}
-        style={{
-          border: '1px solid #ccc',
-          height: 400,
-          overflowY: 'auto',
-          padding: 12,
-          background: '#fff'
-        }}
-      >
-        {messages.map((msg, idx) => (
-          <div key={idx} style={{ marginBottom: 12 }}>
-            {msg.from === 'user' ? (
-              <div><strong>You:</strong> {msg.text}</div>
-            ) : msg.card ? (
-              <div ref={(el) => {
-                if (el && !el.hasChildNodes()) {
-                  const card = renderCard(msg.card);
-                  el.appendChild(card);
-                }
-              }} />
-            ) : (
-              <div><strong>Bot:</strong> {msg.text}</div>
-            )}
-          </div>
-        ))}
-      </div>
-
-      <div style={{ display: 'flex', marginTop: 10 }}>
-        <input
-          type="text"
-          ref={inputRef}
-          placeholder="Type your message..."
-          style={{ flexGrow: 1, padding: 8 }}
-          onKeyDown={(e) => {
-            if (e.key === 'Enter') {
-              sendMessage(inputRef.current!.value);
-            }
-          }}
-        />
-        <button onClick={() => sendMessage(inputRef.current!.value)}>Send</button>
-      </div>
-    </div>
-  );
-};
-
-export default Chat;

frontend/src/main.tsx

@@ -1,10 +0,0 @@
-import { StrictMode } from 'react'
-import { createRoot } from 'react-dom/client'
-import './index.css'
-import App from './App.tsx'
-
-createRoot(document.getElementById('root')!).render(
-  <StrictMode>
-    <App />
-  </StrictMode>,
-)

frontend/src/vite-env.d.ts

@@ -1 +0,0 @@
-/// <reference types="vite/client" />

frontend/tsconfig.app.json

@@ -1,26 +0,0 @@
-{
-  "compilerOptions": {
-    "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo",
-    "target": "ES2020",
-    "useDefineForClassFields": true,
-    "lib": ["ES2020", "DOM", "DOM.Iterable"],
-    "module": "ESNext",
-    "skipLibCheck": true,
-
-    /* Bundler mode */
-    "moduleResolution": "bundler",
-    "allowImportingTsExtensions": true,
-    "isolatedModules": true,
-    "moduleDetection": "force",
-    "noEmit": true,
-    "jsx": "react-jsx",
-
-    /* Linting */
-    "strict": true,
-    "noUnusedLocals": true,
-    "noUnusedParameters": true,
-    "noFallthroughCasesInSwitch": true,
-    "noUncheckedSideEffectImports": true
-  },
-  "include": ["src"]
-}

frontend/tsconfig.json

@@ -1,7 +0,0 @@
-{
-  "files": [],
-  "references": [
-    { "path": "./tsconfig.app.json" },
-    { "path": "./tsconfig.node.json" }
-  ]
-}

frontend/tsconfig.node.json

@@ -1,24 +0,0 @@
-{
-  "compilerOptions": {
-    "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo",
-    "target": "ES2022",
-    "lib": ["ES2023"],
-    "module": "ESNext",
-    "skipLibCheck": true,
-
-    /* Bundler mode */
-    "moduleResolution": "bundler",
-    "allowImportingTsExtensions": true,
-    "isolatedModules": true,
-    "moduleDetection": "force",
-    "noEmit": true,
-
-    /* Linting */
-    "strict": true,
-    "noUnusedLocals": true,
-    "noUnusedParameters": true,
-    "noFallthroughCasesInSwitch": true,
-    "noUncheckedSideEffectImports": true
-  },
-  "include": ["vite.config.ts"]
-}

frontend/vite.config.ts

@@ -1,7 +0,0 @@
-import { defineConfig } from 'vite'
-import react from '@vitejs/plugin-react'
-
-// https://vite.dev/config/
-export default defineConfig({
-  plugins: [react()],
-})

frontend/vite.svg

@@ -1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="31.88" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 257"><defs><linearGradient id="IconifyId1813088fe1fbc01fb466" x1="-.828%" x2="57.636%" y1="7.652%" y2="78.411%"><stop offset="0%" stop-color="#41D1FF"></stop><stop offset="100%" stop-color="#BD34FE"></stop></linearGradient><linearGradient id="IconifyId1813088fe1fbc01fb467" x1="43.376%" x2="50.316%" y1="2.242%" y2="89.03%"><stop offset="0%" stop-color="#FFEA83"></stop><stop offset="8.333%" stop-color="#FFDD35"></stop><stop offset="100%" stop-color="#FFA800"></stop></linearGradient></defs><path fill="url(#IconifyId1813088fe1fbc01fb466)" d="M255.153 37.938L134.897 252.976c-2.483 4.44-8.862 4.466-11.382.048L.875 37.958c-2.746-4.814 1.371-10.646 6.827-9.67l120.385 21.517a6.537 6.537 0 0 0 2.322-.004l117.867-21.483c5.438-.991 9.574 4.796 6.877 9.62Z"></path><path fill="url(#IconifyId1813088fe1fbc01fb467)" d="M185.432.063L96.44 17.501a3.268 3.268 0 0 0-2.634 3.014l-5.474 92.456a3.268 3.268 0 0 0 3.997 3.378l24.777-5.718c2.318-.535 4.413 1.507 3.936 3.838l-7.361 36.047c-.495 2.426 1.782 4.5 4.151 3.78l15.304-4.649c2.372-.72 4.652 1.36 4.15 3.788l-11.698 56.621c-.732 3.542 3.979 5.473 5.943 2.437l1.313-2.028l72.516-144.72c1.215-2.423-.88-5.186-3.54-4.672l-25.505 4.922c-2.396.462-4.435-1.77-3.759-4.114l16.646-57.705c.677-2.35-1.37-4.583-3.769-4.113Z"></path></svg>