import faiss
import numpy as np
from fastapi import FastAPI, Query
from datasets import load_dataset
from sentence_transformers import SentenceTransformer

app = FastAPI()

FIELDS = (
    "full_name", "description", "watchers_count", "forks_count", "license",
    "default_branch", "has_build_zig", "has_build_zig_zon", "fork",
    "open_issues", "stargazers_count", "updated_at", "created_at",
    "size"
)

model = SentenceTransformer("all-MiniLM-L6-v2")

def load_dataset_with_fields(name, include_readme=False):
    dataset = load_dataset(name)["train"]
    repo_texts = [
        " ".join(str(x.get(field, "")) for field in FIELDS) + 
        (" " + x.get("readme_content", "")) * include_readme + 
        " " + " ".join(x.get("topics", []))
        for x in dataset
    ]
    if not include_readme:
        dataset = [{k: v for k, v in item.items() if k != "readme_content"} for item in dataset]
    return dataset, repo_texts

datasets = {
    "packages": load_dataset_with_fields("zigistry/packages", include_readme=True),
    "programs": load_dataset_with_fields("zigistry/programs", include_readme=True),
}

indices = {}
for key, (dataset, repo_texts) in datasets.items():
    repo_embeddings = model.encode(repo_texts)
    index = faiss.IndexFlatL2(repo_embeddings.shape[1])
    index.add(np.array(repo_embeddings))
    indices[key] = (index, dataset)

scroll_data = {
    "infiniteScrollPackages": load_dataset_with_fields("zigistry/packages", include_readme=False)[0],
    "infiniteScrollPrograms": load_dataset_with_fields("zigistry/programs", include_readme=False)[0],
}

@app.get("/infiniteScrollPackages/")
def infinite_scroll_packages(q: int = Query(0, ge=0)):
    start = q * 10
    return scroll_data["infiniteScrollPackages"][start : start + 10]

@app.get("/infiniteScrollPrograms/")
def infinite_scroll_programs(q: int = Query(0, ge=0)):
    start = q * 10
    return scroll_data["infiniteScrollPrograms"][start : start + 10]

@app.get("/searchSomething/")
def search_something(q: str):
    key = "packages" if "package" in q.lower() else "programs"
    if key not in indices:
        return {"error": "Invalid category"}
    index, dataset = indices[key]
    query_embedding = model.encode([q])
    distances, indices_ = index.search(np.array(query_embedding), len(dataset))
    min_distance = distances[0][0]
    threshold = min_distance * 1.5
    results = [dataset[int(i)] for d, i in zip(distances[0], indices_[0]) if d <= threshold]
    return results[:280] if len(results) > 280 else results