# app.py
from fastapi import FastAPI, UploadFile, Form, BackgroundTasks
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from pathlib import Path
import subprocess
import shutil
import time
import traceback
import cloudinary
import cloudinary.uploader
from io import BytesIO
from huggingface_hub import hf_hub_download

app = FastAPI(
    title="🚦 VRU Detection & Forecasting API",
    description="Backend API for VRU Detection, Tracking & Forecasting pipeline (Hugging Face Spaces Edition)",
    version="1.0.0"
)

# ==============================
# CONFIG
# ==============================
# Configure Cloudinary (you can put these in .env instead)
cloudinary.config(
    cloud_name="YOUR_CLOUD_NAME",
    api_key="YOUR_API_KEY",
    api_secret="YOUR_API_SECRET"
)

SCRIPTS = [
    ("Video Creation", "video_creation.py"),
    ("YOLO + DeepSORT Tracking", "yolo_deepsort_tracker.py"),
    ("Excel Generation", "excel_generation.py"),
    ("Feature Engineering", "feature_engineering_forecasting.py"),
    ("Trajectory Forecasting (Transformer)", "vru_forecasting_transformer.py"),
    ("Trajectory Visualization", "animated_visualization.py")
]

OUTPUT_GIF = Path("trajectory_comparison.gif")

# ==============================
# CORS
# ==============================
app.add_middleware(
    CORSMiddleware,
    allow_origins=["https://vru-detection.vercel.app/"],   # later replace with your frontend URL
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# ==============================
# UTILS
# ==============================
def run_pipeline(input_path: str):
    """Sequentially run all VRU pipeline scripts."""
    total = len(SCRIPTS)
    progress = []
    for i, (label, script) in enumerate(SCRIPTS):
        stage_info = {
            "stage": i + 1,
            "label": label,
            "status": "running",
            "progress": round((i + 1) / total, 2)
        }
        progress.append(stage_info)
        try:
            result = subprocess.run(
                ["python", script, input_path],
                capture_output=True, text=True
            )
            if result.returncode == 0:
                stage_info["status"] = "completed"
                stage_info["output"] = result.stdout
            else:
                stage_info["status"] = "failed"
                stage_info["error"] = result.stderr
                return {"status": "failed", "progress": progress}
        except Exception as e:
            stage_info["status"] = "error"
            stage_info["error"] = str(e)
            return {"status": "failed", "progress": progress}
        time.sleep(0.5)
    return {"status": "completed", "progress": progress}


# ==============================
# ROUTES
# ==============================
@app.get("/")
def home():
    return {"message": "🚦 VRU Detection Backend Running on Hugging Face Spaces!"}


@app.post("/run_pipeline/")
async def run_vru_pipeline(
    background_tasks: BackgroundTasks,
    dataset_name: str = Form(None),
    file: UploadFile = None
):
    """
    Run full VRU pipeline.
    Accepts either uploaded file or Hugging Face dataset name.
    """

    try:
        # Handle file upload
        if file:
            temp_path = Path("uploaded_" + file.filename)
            with open(temp_path, "wb") as buffer:
                shutil.copyfileobj(file.file, buffer)
            input_path = str(temp_path)

        # Handle Hugging Face dataset file
        elif dataset_name:
            input_path = hf_hub_download(
                repo_id=dataset_name,
                filename="ring_side_left/video.mp4"  # Adjust if structure differs
            )

        else:
            return JSONResponse({"error": "Please provide a dataset_name or upload a file."}, status_code=400)

        # Run pipeline
        result = run_pipeline(input_path)
        response = {"input_path": input_path, "result": result}

        # Upload visualization if available
        if OUTPUT_GIF.exists():
            upload_result = cloudinary.uploader.upload(
                str(OUTPUT_GIF),
                resource_type="image",
                folder="vru_results/"
            )
            response["visualization_url"] = upload_result["secure_url"]

        return JSONResponse(response)

    except Exception as e:
        return JSONResponse(
            {"error": str(e), "trace": traceback.format_exc()},
            status_code=500
        )