main.py

import asyncio
import json
import os
import random
import sys
from typing import Any, Dict, List, Optional

import httpx
import redis
from fastapi import FastAPI, HTTPException, Query
from loguru import logger

from .load_examples import custom_openapi
from .logging_utils import RequestIDMiddleware
from .pydantic_models import FeatureRequest, FeatureRequestFeature, FeatureRequestResult
from .utils import debug_logging_decorator

app = FastAPI()
app.add_middleware(RequestIDMiddleware)

logger.remove()
logger.add(
    sys.stderr,
    format="{time:YYYY-MM-DD HH:mm:ss.SSS} | {level:<8} | {name}:{function}:{line} | request_id: {extra[rec_id]} - {message}",
)


SEQRP_MODEL_SERVER_URL = os.getenv("SEQRP_MODEL_SERVER_URL", "http://localhost:3000")
REDIS_HOST = os.getenv("REDIS_HOST", "localhost")
REDIS_PORT = os.getenv("REDIS_PORT", 6379)
FEAST_ONLINE_SERVER_HOST = os.getenv("FEAST_ONLINE_SERVER_HOST", "localhost")
FEAST_ONLINE_SERVER_PORT = os.getenv("FEAST_ONLINE_SERVER_PORT", 6566)

seqrp_url = f"{SEQRP_MODEL_SERVER_URL}/predict"
redis_client = redis.Redis(
    host=REDIS_HOST, port=REDIS_PORT, db=0, decode_responses=True
)
redis_output_i2i_key_prefix = "output:i2i:"
redis_feature_recent_items_key_prefix = "feature:user:recent_items:"
redis_output_popular_key = "output:popular"
redis_item_tag_key_prefix = "dim:tag_item_map:"

# Set the custom OpenAPI schema with examples
app.openapi = lambda: custom_openapi(
    app,
    redis_client,
    redis_output_i2i_key_prefix,
    redis_feature_recent_items_key_prefix,
)


def get_recommendations_from_redis(
    redis_key: str, count: Optional[int]
) -> Dict[str, Any]:
    rec_data = redis_client.get(redis_key)
    if not rec_data:
        error_message = f"[DEBUG] No recommendations found for key: {redis_key}"
        logger.error(error_message)
        raise HTTPException(status_code=404, detail=error_message)
    rec_data_json = json.loads(rec_data)
    rec_item_ids = rec_data_json.get("rec_item_ids", [])
    rec_scores = rec_data_json.get("rec_scores", [])
    if count is not None:
        rec_item_ids = rec_item_ids[:count]
        rec_scores = rec_scores[:count]
    return {"rec_item_ids": rec_item_ids, "rec_scores": rec_scores}


def get_items_from_tag_redis(
    redis_key: str, count: Optional[int] = 100
) -> Dict[str, Any]:
    items = redis_client.smembers(redis_key)
    if not items:
        error_message = f"[DEBUG] No items found for key: {redis_key}"
        logger.error(error_message)
        raise HTTPException(status_code=404, detail=error_message)
    random.shuffle(items)
    return {"items": items[:count], "redis_key": redis_key}


@app.get("/recs/i2i")
@debug_logging_decorator
async def get_recommendations_i2i(
    item_id: str = Query(..., description="ID of the item to get recommendations for"),
    count: Optional[int] = Query(10, description="Number of recommendations to return"),
    debug: bool = Query(False, description="Enable debug logging"),
):
    redis_key = f"{redis_output_i2i_key_prefix}{item_id}"
    recommendations = get_recommendations_from_redis(redis_key, count)
    return {
        "item_id": item_id,
        "recommendations": recommendations,
    }


@app.get(
    "/recs/u2i/last_item_i2i",
    summary="Get recommendations for users based on their most recent items",
)
@debug_logging_decorator
async def get_recommendations_u2i_last_item_i2i(
    user_id: str = Query(..., description="ID of the user"),
    count: Optional[int] = Query(10, description="Number of recommendations to return"),
    debug: bool = Query(False, description="Enable debug logging"),
):
    logger.debug(f"Getting recent items for user_id: {user_id}")

    # Step 1: Get the recent items for the user
    item_sequences = await feature_store_fetch_item_sequence(user_id)
    last_item_id = item_sequences["item_sequence"][-1]

    logger.debug(f"Most recently interacted item: {last_item_id}")

    # Step 2: Call the i2i endpoint internally to get recommendations for that item
    recommendations = await get_recommendations_i2i(last_item_id, count, debug)

    # Step 3: Format and return the output
    result = {
        "user_id": user_id,
        "last_item_id": last_item_id,
        "recommendations": recommendations["recommendations"],
    }

    return result


@app.get("/recs/u2i/rerank", summary="Get recommendations for users")
@debug_logging_decorator
async def get_recommendations_u2i_rerank(
    user_id: str = Query(
        ..., description="ID of the user to provide recommendations for"
    ),
    top_k_retrieval: Optional[int] = Query(
        100, description="Number of retrieval results to use"
    ),
    count: Optional[int] = Query(10, description="Number of recommendations to return"),
    debug: bool = Query(False, description="Enable debug logging"),
):
    # Step 1: Get popular and i2i recommendations concurrently
    popular_recs, last_item_i2i_recs = await asyncio.gather(
        get_recommendations_popular(count=top_k_retrieval, debug=False),
        get_recommendations_u2i_last_item_i2i(
            user_id=user_id, count=top_k_retrieval, debug=False
        ),
    )

    # Step 2: Merge popular and i2i recommendations
    all_items = set(popular_recs["recommendations"]["rec_item_ids"]).union(
        set(last_item_i2i_recs["recommendations"]["rec_item_ids"])
    )
    all_items = list(all_items)

    logger.debug("Retrieved items: {}", all_items)

    # Step 3: Get item_sequence features
    item_sequences = await feature_store_fetch_item_sequence(user_id)
    item_sequences = item_sequences["item_sequence"]

    # Step 4: Remove rated items
    set_item_sequences = set(item_sequences)
    set_all_items = set(all_items)
    already_rated_items = list(set_item_sequences.intersection(set_all_items))
    logger.debug(
        f"Removing {len(already_rated_items)} items already rated by this user: {already_rated_items}..."
    )
    all_items = list(set_all_items - set_item_sequences)

    # Step 5: Rerank
    reranked_recs = await score_seq_rating_prediction(
        user_ids=[user_id] * len(all_items),
        item_sequences=[item_sequences] * len(all_items),
        item_ids=all_items,
    )

    # Step 6: Extract scores from the result
    scores = reranked_recs.get("scores", [])
    returned_items = reranked_recs.get("item_ids", [])
    reranked_metadata = reranked_recs.get("metadata", {})
    if not scores or len(scores) != len(all_items):
        error_message = "[DEBUG] Mismatch sizes between returned scores and all items"
        logger.debugr("{}", error_message)
        raise HTTPException(status_code=500, detail=error_message)

    # Create a list of tuples (item_id, score)
    item_scores = list(zip(returned_items, scores))

    # Sort the items based on the scores in descending order
    item_scores.sort(key=lambda x: x[1], reverse=True)

    # Unzip the sorted items and scores
    sorted_item_ids, sorted_scores = zip(*item_scores)

    # Step 7: Return the reranked recommendations
    result = {
        "user_id": user_id,
        "features": {"item_sequence": item_sequences},
        "recommendations": {
            "rec_item_ids": list(sorted_item_ids)[:count],
            "rec_scores": list(sorted_scores)[:count],
        },
        "metadata": {"rerank": reranked_metadata},
    }

    return result


@app.get(
    "/recs/u2i/rerank_v2",
    summary="End-to-end retrieve-rerank flow from user to item recommendations",
)
@debug_logging_decorator
async def get_recommendations_u2i_rerank(
    user_id: str = Query(
        ..., description="ID of the user to provide recommendations for"
    ),
    top_k_retrieval: Optional[int] = Query(
        100, description="Number of retrieval results to use"
    ),
    count: Optional[int] = Query(10, description="Number of recommendations to return"),
    debug: bool = Query(False, description="Enable debug logging"),
):
    rec_title = "Recommended For You"
    retrievers = []

    # Get retrieves concurrently
    popular_recs, last_item_i2i_recs, user_tag_pref = await asyncio.gather(
        get_recommendations_popular(count=top_k_retrieval, debug=False),
        get_recommendations_u2i_last_item_i2i(
            user_id=user_id, count=top_k_retrieval, debug=False
        ),
        retrieve_user_tag_pref(user_id=user_id, count=10, debug=False),
    )

    # Prioritize user_tag_pref retrieve
    if user_tags := user_tag_pref.get("data"):
        logger.debug(f"Creating retrieve based on user tag preferences {user_tags}...")
        # Get top 5 tags. The list user tags is sorted by score already.
        user_tags = user_tags[:5]
        # Select random one tag as retrieve key
        chosen = random.choice(user_tags)
        tag = chosen["tag"]
        redis_key = redis_item_tag_key_prefix + tag
        logger.debug(f"Calling redis with key {redis_key}...")
        all_items = get_items_from_tag_redis(redis_key, count=top_k_retrieval).get(
            "items", []
        )
        rec_title = f"Based on Your Interest in {tag} Titles"
        retrievers.append("user_tag_pref")
    else:
        logger.debug(f"Merging popular retrieve and last item i2i retrieve...")
        # Merge popular and i2i recommendations
        all_items = set(popular_recs["recommendations"]["rec_item_ids"]).union(
            set(last_item_i2i_recs["recommendations"]["rec_item_ids"])
        )
        all_items = list(all_items)
        retrievers.extend(["popular", "last_item_i2i"])

    logger.debug("Retrieved items: {}", all_items)

    # Get item_sequence features
    item_sequences = await feature_store_fetch_item_sequence(user_id)
    item_sequences = item_sequences["item_sequence"]

    # Remove rated items
    set_item_sequences = set(item_sequences)
    set_all_items = set(all_items)
    already_rated_items = list(set_item_sequences.intersection(set_all_items))
    logger.debug(
        f"Removing {len(already_rated_items)} items already rated by this user: {already_rated_items}..."
    )
    all_items = list(set_all_items - set_item_sequences)

    # Rerank
    reranked_recs = await score_seq_rating_prediction(
        user_ids=[user_id] * len(all_items),
        item_sequences=[item_sequences] * len(all_items),
        item_ids=all_items,
    )

    # Extract scores from the result
    scores = reranked_recs.get("scores", [])
    returned_items = reranked_recs.get("item_ids", [])
    reranked_metadata = reranked_recs.get("metadata", {})
    if not scores or len(scores) != len(all_items):
        error_message = "[DEBUG] Mismatch sizes between returned scores and all items"
        logger.debugr("{}", error_message)
        raise HTTPException(status_code=500, detail=error_message)

    # Create a list of tuples (item_id, score)
    item_scores = list(zip(returned_items, scores))

    # Sort the items based on the scores in descending order
    item_scores.sort(key=lambda x: x[1], reverse=True)

    # Unzip the sorted items and scores
    sorted_item_ids, sorted_scores = zip(*item_scores)

    # Return the reranked recommendations
    result = {
        "user_id": user_id,
        "features": {"item_sequence": item_sequences},
        "recommendations": {
            "rec_item_ids": list(sorted_item_ids)[:count],
            "rec_scores": list(sorted_scores)[:count],
        },
        "rec_title": rec_title,
        "metadata": {"retrieve": retrievers, "rerank": reranked_metadata},
    }

    return result


@app.get("/recs/popular")
@debug_logging_decorator
async def get_recommendations_popular(
    count: Optional[int] = Query(10, description="Number of popular items to return"),
    debug: bool = Query(False, description="Enable debug logging"),
):
    recommendations = get_recommendations_from_redis(redis_output_popular_key, count)
    return {"recommendations": recommendations}


@app.get("/recs/retrieve/user_tag_pref")
@debug_logging_decorator
async def retrieve_user_tag_pref(
    user_id: str = Query(
        ..., description="ID of the user to provide recommendations for"
    ),
    count: Optional[int] = Query(10, description="Number of items to return"),
    debug: bool = Query(False, description="Enable debug logging"),
):
    feature_view = "user_tag_pref"
    user_tag_pref_feature = FeatureRequestFeature(
        feature_view=feature_view, feature_name="user_tag_pref_score_full_list"
    )

    fr = FeatureRequest(
        entities={"user_id": [user_id]},
        features=[user_tag_pref_feature.get_full_name(fresh=False, is_request=True)],
    )
    response = await fetch_features(fr)

    result = FeatureRequestResult(
        metadata=response["metadata"], results=response["results"]
    )
    feature_value = result.get_feature_value_no_fresh(user_tag_pref_feature)

    if not feature_value:
        return {"data": []}

    # Example feature_value: Classic__4.0,Multiplayer__4.0
    output = []
    for tag_score in feature_value.split(","):
        tag, score = tag_score.split("__")
        output.append({"tag": tag, "score": score})

    output = sorted(output, key=lambda x: x["score"], reverse=True)[:count]

    return {"data": output}


# New endpoint to connect to external service
@app.post("/score/seq_rating_prediction")
@debug_logging_decorator
async def score_seq_rating_prediction(
    user_ids: List[str],
    item_sequences: List[List[str]],
    item_ids: List[str],
    debug: bool = Query(False, description="Enable debug logging"),
):
    logger.debug(
        f"Calling seq_rating_predicting with user_ids: {user_ids}, item_sequences: {item_sequences} and item_ids: {item_ids}"
    )

    # Step 1: Prepare the payload for the external service
    payload = {
        "input_data": {
            "user_ids": user_ids,
            "item_sequences": item_sequences,
            "item_ids": item_ids,
        }
    }

    # Using json.dumps to format payload as json string so that later can extract from logs and rebuild the data easily
    logger.debug(
        f"[COLLECT] Payload prepared: <features>{json.dumps(payload)}</features>"
    )

    # Step 2: Make the POST request to the external service
    try:
        async with httpx.AsyncClient() as client:
            response = await client.post(
                seqrp_url,
                json=payload,
                headers={
                    "accept": "application/json",
                    "Content-Type": "application/json",
                },
            )

        # Step 3: Handle response
        if response.status_code == 200:
            logger.debug(
                f"[COLLECT] Response from external service: <result>{json.dumps(response.json())}</result>"
            )
            result = response.json()

            return result
        else:
            error_message = (
                f"[DEBUG] External service returned an error: {response.text}"
            )
            logger.error(error_message)
            raise HTTPException(
                status_code=response.status_code,
                detail=error_message,
            )

    except httpx.HTTPError as e:
        error_message = f"[DEBUG] Error connecting to external service: {str(e)}"
        logger.error(error_message)
        raise HTTPException(status_code=500, detail=error_message)


@app.post("/feature_store/fetch")
async def fetch_features(request: FeatureRequest):
    # Define the URL for the feature store's endpoint
    feature_store_url = f"http://{FEAST_ONLINE_SERVER_HOST}:{FEAST_ONLINE_SERVER_PORT}/get-online-features"
    logger.info(f"Sending request to {feature_store_url}...")

    # Create the payload to send to the feature store
    payload_fresh = {
        "entities": request.entities,
        "features": request.features,
        "full_feature_names": True,
    }

    # Make the POST request to the feature store
    async with httpx.AsyncClient() as client:
        response = await client.post(feature_store_url, json=payload_fresh)

    # Check if the request was successful
    if response.status_code == 200:
        return response.json()
    else:
        raise HTTPException(
            status_code=response.status_code,
            detail=f"Error fetching features: {response.text}",
        )


@app.get("/feature_store/fetch/item_sequence")
@debug_logging_decorator
async def feature_store_fetch_item_sequence(user_id: str):
    """
    Quick work around to get feature sequences from both streaming sources and common online sources
    """
    feature_view = "user_rating_stats"
    item_sequence_feature = FeatureRequestFeature(
        feature_view=feature_view, feature_name="user_rating_list_10_recent_asin"
    )
    item_sequence_ts_feature = FeatureRequestFeature(
        feature_view=feature_view,
        feature_name="user_rating_list_10_recent_asin_timestamp",
    )

    fr = FeatureRequest(
        entities={"user_id": [user_id]},
        features=[
            item_sequence_feature.get_full_name(fresh=True, is_request=True),
            item_sequence_feature.get_full_name(fresh=False, is_request=True),
            item_sequence_ts_feature.get_full_name(fresh=True, is_request=True),
            item_sequence_ts_feature.get_full_name(fresh=False, is_request=True),
        ],
    )
    response = await fetch_features(fr)

    result = FeatureRequestResult(
        metadata=response["metadata"], results=response["results"]
    )
    item_sequence = result.get_feature_value(item_sequence_feature)
    item_sequence_ts = result.get_feature_value(item_sequence_ts_feature)

    return {
        "user_id": user_id,
        "item_sequence": item_sequence,
        "item_sequence_ts": item_sequence_ts,
    }