"""FastAPI server for SENG inference.

Loads the merged GPT-2 Large model (LoRA already folded in) and exposes
endpoints that replicate the exact inference logic from run_pipeline.py.
"""

import os

import torch
from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_DIR = os.environ.get("MODEL_DIR", "/model")
INSTRUCTION = "Analyze these repository events and extract development beliefs."
CHUNK_SIZE = 12

app = FastAPI(title="SENG API")

# Globals populated at startup
model = None
tokenizer = None
device = None


class PredictRequest(BaseModel):
    text: str


class BatchRequest(BaseModel):
    narrative: str


@app.on_event("startup")
def load_model():
    global model, tokenizer, device

    device = "cuda" if torch.cuda.is_available() else "cpu"
    dtype = torch.float16 if device == "cuda" else torch.float32

    print(f"Loading model from {MODEL_DIR} on {device} ({dtype})")
    tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
    model = AutoModelForCausalLM.from_pretrained(
        MODEL_DIR, torch_dtype=dtype
    ).to(device)
    model.eval()
    print("Model loaded.")


@app.get("/health")
def health():
    return {
        "status": "healthy" if model is not None else "loading",
        "model_loaded": model is not None,
        "gpu_available": torch.cuda.is_available(),
    }


def run_inference(text: str) -> str:
    """Run inference on a single text chunk.

    Replicates run_pipeline.py:120-146 exactly.
    """
    prompt = (
        f"### Instruction:\n{INSTRUCTION}\n\n"
        f"### Input:\n{text}\n\n"
        f"### Response:\n"
    )

    inputs = tokenizer(
        prompt,
        return_tensors="pt",
        truncation=True,
        max_length=768,
    ).to(device)

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=256,
            temperature=0.7,
            do_sample=True,
            top_p=0.9,
            pad_token_id=tokenizer.eos_token_id,
            repetition_penalty=1.2,
        )

    generated = tokenizer.decode(
        outputs[0][inputs["input_ids"].shape[1]:],
        skip_special_tokens=True,
    ).strip()

    return generated


def chunk_narrative(narrative: str, chunk_size: int = CHUNK_SIZE) -> list[str]:
    """Split narrative into non-overlapping chunks.

    Replicates run_pipeline.py:89-99.
    """
    lines = [line.rstrip("\n") for line in narrative.splitlines() if line.strip()]
    chunks = []
    for i in range(0, len(lines), chunk_size):
        chunk_lines = lines[i : i + chunk_size]
        if chunk_lines:
            chunks.append("\n".join(chunk_lines))
    return chunks


@app.post("/predict")
def predict(req: PredictRequest):
    generated = run_inference(req.text)
    return {"generated_text": generated}


@app.post("/batch")
def batch(req: BatchRequest):
    chunks = chunk_narrative(req.narrative)
    results = []
    for i, chunk in enumerate(chunks):
        generated = run_inference(chunk)
        results.append({"chunk_index": i, "generated_text": generated})
    return {"total_chunks": len(chunks), "results": results}