import sys
import os
import copy
import uvicorn
import socket
import logging
import datetime
from models.vectorizer import Vectorizer
from models.prompt_search_engine import PromptSearchEngine
from models.data_reader import load_prompts_from_jsonl
from models.Query import Query, Query_Multiple, SearchResponse, SimilarPrompt
from decouple import config
from fastapi import FastAPI, HTTPException, Depends, Body
from sentence_transformers import SentenceTransformer



prompt_path = r"C:\Users\jov2bg\Desktop\PromptSearch\models\prompts_data.jsonl"


app = FastAPI(title="Search Prompt Engine", description="API for prompt search", version="1.0")

prompts = load_prompts_from_jsonl(prompt_path)
search_engine = PromptSearchEngine()
search_engine.add_prompts_to_vector_database(prompts)

@app.get("/")
def read_root():
    return {"message": "Prompt Search Engine is running!"}

@app.post("/search/")
async def search_prompts(query: Query, k: int = 3):
    print(f'Prompt: {query.prompt}')
    similar_prompts, distances = search_engine.most_similar(query.prompt, top_k=k)
    print(f'Similar Prompts {similar_prompts}')
    print(f'Distances {distances}')
    print(40*'****')
    # Format the response
    response = [
        SimilarPrompt(prompt=prompt, distance=float(distance)) 
        for prompt, distance in zip(similar_prompts, distances)
    ]
    
    return SearchResponse(results=response)

@app.post("/all_vectors_similarities/")
async def all_vectors(query: Query):

    all_similarities = search_engine.cosine_similarity(query.prompt, search_engine.index)
    response = [
        SimilarPrompt(prompt=prompt, distance=float(distance)) 
        for prompt, distance in all_similarities.items()
    ]
    return SearchResponse(results=response)

if __name__ == "__main__":
    localhost = socket.gethostbyname("localhost") 
    uvicorn.run(app, host=localhost, port=8000)