import io

import pandas as pd
from api import companies, investors
from db.db import db_dependency, init_database
from fastapi import FastAPI, File, UploadFile
from py_schemas import InvestorList
from pydantic import BaseModel
from services.openrouter import InvestorProcessor
from services.querying import QueryProcessor

app = FastAPI()
init_database()


# Request models
class QueryRequest(BaseModel):
    question: str

    class Config:
        json_schema_extra = {
            "example": {
                "question": "Show me growth stage fintech investors in the US with check sizes over $1 million"
            }
        }


@app.get("/")
def health():
    return {"Hello": "World"}


@app.post("/parse-csv", tags=["CSV Upload"], response_model=list[dict])
async def parse_csv(db: db_dependency, file: UploadFile = File(...)):
    # Read uploaded CSV with pandas
    content = await file.read()
    df = pd.read_csv(io.StringIO(content.decode("utf-8")))

    # Process the dataframe
    processor = InvestorProcessor(sql_session=db)
    results = await processor.process_csv(df)

    # Convert Pydantic objects to dictionaries
    return [r.model_dump() for r in results]


@app.post("/query", response_model=InvestorList, tags=["Querying"])
async def query_investors(db: db_dependency, request: QueryRequest):
    """
    Query investors using natural language.

    Supports queries like:
    - "Show me seed stage investors"
    - "Find fintech investors in Silicon Valley"
    - "Growth stage investors with $5M+ check sizes"
    - "Healthcare investors in Europe"
    """
    processor = QueryProcessor(sql_session=db)
    results = processor.process_query(request.question)
    return results


app.include_router(investors.router)
app.include_router(companies.router)
if __name__ == "__main__":
    import uvicorn

    uvicorn.run(app="main:app", host="localhost", port=8000, reload=True)