src/main.py

# import os
# from fastapi import FastAPI, Depends, HTTPException
# from fastapi.responses import JSONResponse, RedirectResponse
# from fastapi.middleware.gzip import GZipMiddleware
# from pydantic import BaseModel
# from typing import List, Dict
# from src.api.models.embedding_models import (
#     CreateEmbeddingRequest,
#     ReadEmbeddingRequest,
#     UpdateEmbeddingRequest,
#     DeleteEmbeddingRequest,
# )
# from src.api.database import get_db, Database, QueryExecutionError, HealthCheckError
# from src.api.services.embedding_service import EmbeddingService
# from src.api.services.huggingface_service import HuggingFaceService
# from src.api.exceptions import DatasetNotFoundError, DatasetPushError, OpenAIError
# import pandas as pd
# import logging
# from dotenv import load_dotenv

# # Load environment variables
# load_dotenv()

# # Set up structured logging
# logging.basicConfig(
#     level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
# )
# logger = logging.getLogger(__name__)

# description = """A FastAPI application for similarity search with PostgreSQL and OpenAI embeddings.

# Direct/API URL:
# https://re-mind-similarity-search.hf.space
# """

# # Initialize FastAPI app
# app = FastAPI(
#     title="Similarity Search API",
#     description=description,
#     version="1.0.0",
# )

# app.add_middleware(GZipMiddleware, minimum_size=1000)


# # Root endpoint redirects to /docs
# @app.get("/")
# async def root():
#     return RedirectResponse(url="/docs")


# # Health check endpoint
# @app.get("/health")
# async def health_check(db: Database = Depends(get_db)):
#     try:
#         is_healthy = await db.health_check()
#         if not is_healthy:
#             raise HTTPException(status_code=500, detail="Database is unhealthy")
#         return {"status": "healthy"}
#     except HealthCheckError as e:
#         raise HTTPException(status_code=500, detail=str(e))


# # Dependency to get EmbeddingService
# def get_embedding_service() -> EmbeddingService:
#     return EmbeddingService(openai_api_key=os.getenv("OPENAI_API_KEY"))


# # Dependency to get HuggingFaceService
# def get_huggingface_service() -> HuggingFaceService:
#     return HuggingFaceService()


# # Endpoint to create embeddings
# @app.post("/create_embedding")
# async def create_embedding(
#     request: CreateEmbeddingRequest,
#     db: Database = Depends(get_db),
#     embedding_service: EmbeddingService = Depends(get_embedding_service),
#     huggingface_service: HuggingFaceService = Depends(get_huggingface_service),
# ):
#     """
#     Create embeddings for the target column in the dataset.
#     """
#     try:
#         # Step 1: Query the database
#         logger.info("Fetching data from the database...")
#         result = await db.fetch(request.query)
#         df = pd.DataFrame(result)

#         # Step 2: Generate embeddings
#         df = await embedding_service.create_embeddings(
#             df, request.target_column, request.output_column
#         )

#         # Step 3: Push to Hugging Face Hub
#         await huggingface_service.push_to_hub(df, request.dataset_name)

#         return JSONResponse(
#             content={
#                 "message": "Embeddings created and pushed to Hugging Face Hub.",
#                 "dataset_name": request.dataset_name,
#                 "num_rows": len(df),
#             }
#         )
#     except QueryExecutionError as e:
#         logger.error(f"Database query failed: {e}")
#         raise HTTPException(status_code=500, detail=f"Database query failed: {e}")
#     except OpenAIError as e:
#         logger.error(f"OpenAI API error: {e}")
#         raise HTTPException(status_code=500, detail=f"OpenAI API error: {e}")
#     except DatasetPushError as e:
#         logger.error(f"Failed to push dataset: {e}")
#         raise HTTPException(status_code=500, detail=f"Failed to push dataset: {e}")
#     except Exception as e:
#         logger.error(f"An error occurred: {e}")
#         raise HTTPException(status_code=500, detail=f"An error occurred: {e}")


# # Endpoint to read embeddings
# # @app.get("/read_embeddings/{dataset_name}")
# @app.post("/read_embeddings")
# async def read_embeddings(
#     request: ReadEmbeddingRequest,
#     huggingface_service: HuggingFaceService = Depends(get_huggingface_service),
# ):
#     """
#     Read embeddings from a Hugging Face dataset.
#     """
#     try:
#         df = await huggingface_service.read_dataset(request.dataset_name)
#         return df
#     except DatasetNotFoundError as e:
#         logger.error(f"Dataset not found: {e}")
#         raise HTTPException(status_code=404, detail=f"Dataset not found: {e}")
#     except Exception as e:
#         logger.error(f"An error occurred: {e}")
#         raise HTTPException(status_code=500, detail=f"An error occurred: {e}")


# # Endpoint to update embeddings
# @app.post("/update_embeddings")
# async def update_embeddings(
#     request: UpdateEmbeddingRequest,
#     huggingface_service: HuggingFaceService = Depends(get_huggingface_service),
# ):
#     """
#     Update embeddings in a Hugging Face dataset.
#     """
#     try:
#         df = await huggingface_service.update_dataset(
#             request.dataset_name, request.updates
#         )
#         return {
#             "message": "Embeddings updated successfully.",
#             "dataset_name": request.dataset_name,
#         }
#     except DatasetPushError as e:
#         logger.error(f"Failed to update dataset: {e}")
#         raise HTTPException(status_code=500, detail=f"Failed to update dataset: {e}")
#     except Exception as e:
#         logger.error(f"An error occurred: {e}")
#         raise HTTPException(status_code=500, detail=f"An error occurred: {e}")


# # Endpoint to delete embeddings
# @app.post("/delete_embeddings")
# async def delete_embeddings(
#     request: DeleteEmbeddingRequest,
#     huggingface_service: HuggingFaceService = Depends(get_huggingface_service),
# ):
#     """
#     Delete embeddings from a Hugging Face dataset.
#     """
#     try:
#         await huggingface_service.delete_dataset(request.dataset_name)
#         return {
#             "message": "Embeddings deleted successfully.",
#             "dataset_name": request.dataset_name,
#         }
#     except DatasetPushError as e:
#         logger.error(f"Failed to delete columns: {e}")
#         raise HTTPException(status_code=500, detail=f"Failed to delete columns: {e}")
#     except Exception as e:
#         logger.error(f"An error occurred: {e}")
#         raise HTTPException(status_code=500, detail=f"An error occurred: {e}")


import os
from fastapi import FastAPI, Depends, HTTPException
from fastapi.responses import JSONResponse, RedirectResponse
from fastapi.middleware.gzip import GZipMiddleware
from pydantic import BaseModel
from typing import List, Dict
from src.api.models.embedding_models import (
    CreateEmbeddingRequest,
    ReadEmbeddingRequest,
    UpdateEmbeddingRequest,
    DeleteEmbeddingRequest,
    EmbedRequest,
)
from src.api.database import get_db, Database, QueryExecutionError, HealthCheckError
from src.api.services.embedding_service import EmbeddingService
from src.api.services.huggingface_service import HuggingFaceService
from src.api.exceptions import DatasetNotFoundError, DatasetPushError, OpenAIError

# from src.api.dependency import get_embedding_service, get_huggingface_service
import pandas as pd
import logging
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Set up structured logging
logging.basicConfig(
    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)

description = """A FastAPI application for similarity search with PostgreSQL and OpenAI embeddings.

Direct/API URL:
https://re-mind-similarity-search.hf.space
"""

# Initialize FastAPI app
app = FastAPI(
    title="Similarity Search API",
    description=description,
    version="1.0.0",
)

app.add_middleware(GZipMiddleware, minimum_size=1000)


# Dependency to get EmbeddingService
def get_embedding_service() -> EmbeddingService:
    return EmbeddingService(openai_api_key=os.getenv("OPENAI_API_KEY"))


# Dependency to get HuggingFaceService
def get_huggingface_service() -> HuggingFaceService:
    return HuggingFaceService()


# Root endpoint redirects to /docs
@app.get("/")
async def root():
    return RedirectResponse(url="/docs")


# Health check endpoint
@app.get("/health")
async def health_check(db: Database = Depends(get_db)):
    try:
        is_healthy = await db.health_check()
        if not is_healthy:
            raise HTTPException(status_code=500, detail="Database is unhealthy")
        return {"status": "healthy"}
    except HealthCheckError as e:
        raise HTTPException(status_code=500, detail=str(e))


# Endpoint to generate embeddings for a list of strings
@app.post("/embed")
async def embed(
    request: EmbedRequest,
    embedding_service: EmbeddingService = Depends(get_embedding_service),
):
    """
    Generate embeddings for a list of strings and return them in the response.
    """
    try:
        # Step 1: Generate embeddings
        logger.info("Generating embeddings for list of texts...")
        embeddings = await embedding_service.create_embeddings(request.texts)

        return JSONResponse(
            content={
                "message": "Embeddings generated successfully.",
                "embeddings": embeddings,
                "num_texts": len(request.texts),
            }
        )
    except OpenAIError as e:
        logger.error(f"OpenAI API error: {e}")
        raise HTTPException(status_code=500, detail=f"OpenAI API error: {e}")
    except Exception as e:
        logger.error(f"An error occurred: {e}")
        raise HTTPException(status_code=500, detail=f"An error occurred: {e}")


# Endpoint to create embeddings from a database query
@app.post("/create_embedding")
async def create_embedding(
    request: CreateEmbeddingRequest,
    db: Database = Depends(get_db),
    embedding_service: EmbeddingService = Depends(get_embedding_service),
    huggingface_service: HuggingFaceService = Depends(get_huggingface_service),
):
    """
    Create embeddings for the target column in the dataset.
    """
    try:
        # Step 1: Query the database
        logger.info("Fetching data from the database...")
        result = await db.fetch(request.query)
        df = pd.DataFrame(result)

        # Step 2: Generate embeddings
        df = await embedding_service.create_embeddings(
            df, request.target_column, request.output_column
        )

        # Step 3: Push to Hugging Face Hub
        await huggingface_service.push_to_hub(df, request.dataset_name)

        return JSONResponse(
            content={
                "message": "Embeddings created and pushed to Hugging Face Hub.",
                "dataset_name": request.dataset_name,
                "num_rows": len(df),
            }
        )
    except QueryExecutionError as e:
        logger.error(f"Database query failed: {e}")
        raise HTTPException(status_code=500, detail=f"Database query failed: {e}")
    except OpenAIError as e:
        logger.error(f"OpenAI API error: {e}")
        raise HTTPException(status_code=500, detail=f"OpenAI API error: {e}")
    except DatasetPushError as e:
        logger.error(f"Failed to push dataset: {e}")
        raise HTTPException(status_code=500, detail=f"Failed to push dataset: {e}")
    except Exception as e:
        logger.error(f"An error occurred: {e}")
        raise HTTPException(status_code=500, detail=f"An error occurred: {e}")


# Endpoint to read embeddings
@app.post("/read_embeddings")
async def read_embeddings(
    request: ReadEmbeddingRequest,
    huggingface_service: HuggingFaceService = Depends(get_huggingface_service),
):
    """
    Read embeddings from a Hugging Face dataset.
    """
    try:
        df = await huggingface_service.read_dataset(request.dataset_name)
        return df
    except DatasetNotFoundError as e:
        logger.error(f"Dataset not found: {e}")
        raise HTTPException(status_code=404, detail=f"Dataset not found: {e}")
    except Exception as e:
        logger.error(f"An error occurred: {e}")
        raise HTTPException(status_code=500, detail=f"An error occurred: {e}")


# Endpoint to update embeddings
# @app.post("/update_embeddings")
# async def update_embeddings(
#     request: UpdateEmbeddingRequest,
#     huggingface_service: HuggingFaceService = Depends(get_huggingface_service),
# ):
#     """
#     Update embeddings in a Hugging Face dataset.
#     """
#     try:
#         df = await huggingface_service.update_dataset(
#             request.dataset_name, request.updates
#         )
#         return {
#             "message": "Embeddings updated successfully.",
#             "dataset_name": request.dataset_name,
#         }
#     except DatasetPushError as e:
#         logger.error(f"Failed to update dataset: {e}")
#         raise HTTPException(status_code=500, detail=f"Failed to update dataset: {e}")
#     except Exception as e:
#         logger.error(f"An error occurred: {e}")
#         raise HTTPException(status_code=500, detail=f"An error occurred: {e}")


@app.post("/update_embeddings")
async def update_embeddings(
    request: UpdateEmbeddingRequest,
    huggingface_service: HuggingFaceService = Depends(get_huggingface_service),
):
    """
    Update embeddings in a Hugging Face dataset by generating embeddings for new data and concatenating it with the existing dataset.
    """
    try:
        # Call the update_dataset method to generate embeddings, concatenate, and push the updated dataset
        updated_df = await huggingface_service.update_dataset(
            request.dataset_name,
            request.updates,
            request.target_column,
            request.output_column,
        )

        return {
            "message": "Embeddings updated successfully.",
            "dataset_name": request.dataset_name,
            "num_rows": len(updated_df),
        }
    except DatasetPushError as e:
        logger.error(f"Failed to update dataset: {e}")
        raise HTTPException(status_code=500, detail=f"Failed to update dataset: {e}")
    except Exception as e:
        logger.error(f"An error occurred: {e}")
        raise HTTPException(status_code=500, detail=f"An error occurred: {e}")


# Endpoint to delete embeddings
@app.post("/delete_embeddings")
async def delete_embeddings(
    request: DeleteEmbeddingRequest,
    huggingface_service: HuggingFaceService = Depends(get_huggingface_service),
):
    """
    Delete embeddings from a Hugging Face dataset.
    """
    try:
        await huggingface_service.delete_dataset(request.dataset_name)
        return {
            "message": "Embeddings deleted successfully.",
            "dataset_name": request.dataset_name,
        }
    except DatasetPushError as e:
        logger.error(f"Failed to delete columns: {e}")
        raise HTTPException(status_code=500, detail=f"Failed to delete columns: {e}")
    except Exception as e:
        logger.error(f"An error occurred: {e}")
        raise HTTPException(status_code=500, detail=f"An error occurred: {e}")