from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import Optional
import time
import uuid
from datetime import datetime

from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse
import os

from services.inference import run_single_strategy, run_all_strategies
from services.attention import extract_attention
from services.embeddings import compute_similarity

# ── Creating the FastAPI app ─────────────────────────────────────────────────

app = FastAPI(
    title='Inference Observatory API',
    description="Local inference engine for all 10 decoding strategies",
    version="1.0.0",
)


app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


class RunAllRequest(BaseModel):

    prompt: str
    max_tokens: Optional[int] = 150

    beam_size: Optional[int] = 5

    temperature: Optional[float] = 0.7

    top_k: Optional[int] = 50
    top_p: Optional[float] = 0.9

    tktp_k: Optional[int] = 50        
    tktp_p: Optional[float] = 0.9     
    ttk_temp: Optional[float] = 0.7   
    ttk_k: Optional[int] = 50         
    ttp_temp: Optional[float] = 0.7   
    ttp_p: Optional[float] = 0.9      
    ttkp_temp: Optional[float] = 0.7  
    ttkp_k: Optional[int] = 50        
    ttkp_p: Optional[float] = 0.9     

class RunStrategyRequest(BaseModel):
    
    strategy: str
    params: RunAllRequest

class AttentionRequest(BaseModel):
    
    word: str     
    prompt: str

class SimilarityRequest(BaseModel):
   
    texts: dict

# ── ENDPOINTS ──────────────────────────────────────────────────────────────

frontend_path = os.path.abspath(
    os.path.join(os.path.dirname(__file__), '..', 'frontend')
)

@app.get("/")
async def serve_frontend():
    return FileResponse(os.path.join(frontend_path, 'index.html'))

@app.get("/styles.css")
async def serve_css():
    return FileResponse(os.path.join(frontend_path, 'styles.css'))

@app.get("/app.js")
async def serve_js():
    return FileResponse(os.path.join(frontend_path, 'app.js'))


@app.get("/health")
async def health():
    
    return {
        "status": "healthy",
        "timestamp": time.time()
    }

@app.post("/api/run-strategy")
async def run_strategy_endpoint(request: RunStrategyRequest):
    
    try:
        start_time = time.time()

        output = await run_single_strategy(
            strategy=request.strategy,
            params=request.params
        )

        output["total_time_ms"] = round((time.time() - start_time) * 1000)
        return output

    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/api/run-all")
async def run_all(request: RunAllRequest):
   
    try:
        start_time = time.time()

        outputs = await run_all_strategies(
            prompt=request.prompt,
            max_tokens=request.max_tokens,
            beam_size=request.beam_size,
            top_k=request.top_k,
            top_p=request.top_p,
            temperature=request.temperature,
            tktp_k=request.tktp_k,
            tktp_p=request.tktp_p,
            ttk_temp=request.ttk_temp,
            ttk_k=request.ttk_k,
            ttp_temp=request.ttp_temp,
            ttp_p=request.ttp_p,
            ttkp_temp=request.ttkp_temp,
            ttkp_k=request.ttkp_k,
            ttkp_p=request.ttkp_p,
        )

        return {
            "outputs": outputs,
            "run_id": str(uuid.uuid4()),
            "timestamp": datetime.utcnow().isoformat(),
            "total_time_ms": round((time.time() - start_time) * 1000)
        }

    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/api/attention")
async def attention(request: AttentionRequest):
  
    try:
        result = await extract_attention(
            prompt=request.prompt,
            word=request.word
        )
        return result

    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/api/similarity")
async def similarity(request: SimilarityRequest):
    
    try:
        scores = await compute_similarity(request.texts)
        return {"scores": scores}

    except Exception as e:
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=str(e))

# ── Run the server ─────────────────────────────────────────────────────────

if __name__ == "__main__":
    import uvicorn
    print("Starting Inference Observatory backend...")
    print("API docs available at: http://localhost:8000/docs")
    print("Model: Qwen2.5-0.5B (loads on first request)")
    uvicorn.run(
        "main:app",
        host="0.0.0.0",
        port=7860,
        reload=True
    )