import json, os, re, requests, uvicorn, logging
from datetime import datetime, timedelta
from typing import Optional, List
from dotenv import load_dotenv
from fastapi import FastAPI, HTTPException, Body
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from google import genai

load_dotenv()
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(name)s: %(message)s")
logger = logging.getLogger("PestAPI")

app = FastAPI(title="Pest Prediction API")
app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])

GEMINI_ID = os.getenv("GEMINI_API_KEY")
NV_KEY = os.getenv("NVIDIA_API_KEY")
OWM_KEY = os.getenv("OPENWEATHERMAP_API_KEY")

class PredictRequest(BaseModel):
    latitude: float
    longitude: float
    crop_type: str
    season: str
    soil_type: Optional[str] = "Unknown"
    language: Optional[str] = "English"

def fetch_weather(lat, lon):
    try:
        r = requests.get("https://api.open-meteo.com/v1/forecast", params={"latitude": lat, "longitude": lon, "current_weather": True, "hourly": "relative_humidity_2m", "timezone": "auto"}).json()
        res = {"temp": r['current_weather']['temperature'], "humidity": sum(r['hourly']['relative_humidity_2m'][:24])/24}
        if OWM_KEY:
            ow = requests.get(f"https://api.openweathermap.org/data/2.5/weather?lat={lat}&lon={lon}&appid={OWM_KEY}&units=metric").json()
            res.update({"temp": ow['main']['temp'], "humidity": ow['main']['humidity']})
        return res
    except: return {"temp": 25, "humidity": 60}

@app.post("/api/predict")
def predict(payload: PredictRequest):
    w = fetch_weather(payload.latitude, payload.longitude)
    prompt = f"Expert Entomologist: Crop {payload.crop_type}, Season {payload.season}, Weather {w}. Output JSON: {{'report_title': '...', 'pest_prediction_table': [{{'pest_name': '...', 'severity': '...'}}]}}"
    
    report = None
    if GEMINI_ID:
        try:
            client = genai.Client(api_key=GEMINI_ID)
            resp = client.models.generate_content(model="gemini-2.0-flash", contents=prompt)
            data = re.search(r"\{.*\}", resp.text, re.S)
            if data: report = json.loads(data.group())
        except: pass

    if not report and NV_KEY:
        try:
            from openai import OpenAI
            nv = OpenAI(base_url="https://integrate.api.nvidia.com/v1", api_key=NV_KEY)
            resp = nv.chat.completions.create(model="meta/llama-3.1-70b-instruct", messages=[{"role": "user", "content": prompt}], max_tokens=1024)
            data = re.search(r"\{.*\}", resp.choices[0].message.content, re.S)
            if data: report = json.loads(data.group())
        except: pass

    if not report: raise HTTPException(500, "AI models failed")
    return {**report, "weather_profile": []}

@app.get("/health")
def health(): return {"status": "ok"}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)