from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import re
import json

# ----------------------------
# 1. Configuration
# ----------------------------

MODEL_NAME = "Salesforce/codegen-350M-mono"
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

# ----------------------------
# 2. FastAPI App Initialization
# ----------------------------

app = FastAPI(
    title="AI Code Review Service",
    description="An API to get AI-powered code reviews for pull request diffs.",
    version="1.0.0",
)

# ----------------------------
# 3. AI Model Loading
# ----------------------------

model = None
tokenizer = None

def load_model():
    """Loads the model and tokenizer into memory."""
    global model, tokenizer
    if model is None:
        print(f"Loading model: {MODEL_NAME} on device: {DEVICE}...")
        tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
        model = AutoModelForCausalLM.from_pretrained(
            MODEL_NAME,
            torch_dtype=torch.float32,
            device_map="cpu",
        )
        print("Model loaded successfully.")

@app.on_event("startup")
async def startup_event():
    """
    On server startup, we trigger the model loading.
    """
    print("Server starting up...")
    load_model()

# ----------------------------
# 4. API Request/Response Models
# ----------------------------

class ReviewRequest(BaseModel):
    diff: str

class ReviewComment(BaseModel):
    file_path: str
    line_number: int
    comment_text: str

class ReviewResponse(BaseModel):
    comments: list[ReviewComment]

# ----------------------------
# 5. The AI Review Logic
# ----------------------------

def run_ai_inference(diff: str) -> str:
    """
    Runs the AI model to get the review.
    """
    if not model or not tokenizer:
        raise RuntimeError("Model is not loaded.")

    # Improved prompt for codegen-350M-mono
    prompt = (
        "Below is a Python function. Please provide a code review comment with suggestions for improvement, in natural language. "
        "Do not repeat the code.\n"
        f"{diff[:800]}\n"
        "Review comment:"
    )
    encoded = tokenizer(
        prompt,
        return_tensors="pt",
        max_length=1024,
        truncation=True,
        padding="max_length"
    )
    input_ids = encoded["input_ids"]
    attention_mask = encoded["attention_mask"]
    with torch.no_grad():
        outputs = model.generate(
            input_ids=input_ids,
            attention_mask=attention_mask,
            max_new_tokens=128,
            do_sample=True,
            temperature=0.7,
            top_p=0.95,
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id if tokenizer.eos_token_id is not None else tokenizer.pad_token_id,
            eos_token_id=tokenizer.eos_token_id if tokenizer.eos_token_id is not None else tokenizer.pad_token_id,
            use_cache=True
        )
    response_text = tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=True)
    # Post-process: filter out code-like lines and fallback if needed
    review_lines = [line.strip() for line in response_text.strip().split('\n') if line.strip()]
    # Filter out lines that look like code
    comment_lines = [l for l in review_lines if not l.startswith("def ") and not l.startswith("class ") and not l.endswith(":") and not l.startswith("#")]
    review = comment_lines[0] if comment_lines else "Consider adding a docstring and input validation."
    return review

def parse_ai_response(response_text: str) -> list[ReviewComment]:
    """
    Parses the raw text from the AI to extract the JSON array.
    """
    # For codegen-350M-mono, just wrap the review in a single comment
    return [ReviewComment(
        file_path="code_reviewed.py",
        line_number=1,
        comment_text=response_text.strip()
    )]

# ----------------------------
# 6. The API Endpoint
# ----------------------------

@app.post("/review", response_model=ReviewResponse)
async def get_code_review(request: ReviewRequest):
    if not request.diff:
        raise HTTPException(status_code=400, detail="Diff content cannot be empty.")

    import time
    start_time = time.time()
    print(f"Starting review request at {start_time}")

    try:
        print("Running AI inference...")
        ai_response_text = run_ai_inference(request.diff)
        print(f"AI inference completed in {time.time() - start_time:.2f} seconds")
        
        print("Parsing AI response...")
        parsed_comments = parse_ai_response(ai_response_text)
        print(f"Total processing time: {time.time() - start_time:.2f} seconds")
        
        return ReviewResponse(comments=parsed_comments)

    except Exception as e:
        print(f"An unexpected error occurred after {time.time() - start_time:.2f} seconds: {e}")
        raise HTTPException(status_code=500, detail="An internal error occurred while processing the review.")

# ----------------------------
# 7. Health Check Endpoint
# ----------------------------

@app.get("/health")
async def health_check():
    return {"status": "ok", "model_loaded": model is not None}