import os
import pickle
import pandas as pd
from fastapi import FastAPI, Request, HTTPException
from fastapi.responses import HTMLResponse, JSONResponse
from fastapi.templating import Jinja2Templates
from fastapi.middleware.cors import CORSMiddleware

app = FastAPI(title="Student Score Predictor Chatbot")
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"], allow_credentials=True,
    allow_methods=["*"],  allow_headers=["*"],
)
templates = Jinja2Templates(directory="templates")

# Load model once at startup
MODEL_PATH = os.getenv('MODEL_PATH', 'student_performance_model.pkl')
try:
    with open(MODEL_PATH, 'rb') as f:
        model = pickle.load(f)
except Exception as e:
    raise RuntimeError(f"Could not load model: {e}")

# Chat‑fields configuration
FIELDS = [
    {'name': 'Age', 'type': 'number',
     'question': 'What is your age?',
     'validation': {'min': 5, 'max': 100}},
    {'name': 'Gender', 'type': 'select',
     'question': 'What is your gender?',
     'options': ['Male', 'Female', 'Other']},
    {'name': 'HoursOfStudyPerDay', 'type': 'number',
     'question': 'How many hours do you study per day?',
     'validation': {'min': 0, 'max': 24}},
    {'name': 'SchoolAttendanceRate', 'type': 'number',
     'question': 'What is your school attendance rate (%)?',
     'validation': {'min': 0, 'max': 100}},
    {'name': 'TuitionAccess', 'type': 'select',
     'question': 'Do you have access to extra tuition?',
     'options': ['Yes', 'No']},
    {'name': 'AveragePreviousScores', 'type': 'number',
     'question': 'What was your average previous score?',
     'validation': {'min': 0, 'max': 100}},
    {'name': 'HoursOfSleep', 'type': 'number',
     'question': 'How many hours of sleep do you get per night?',
     'validation': {'min': 0, 'max': 24}},
    {'name': 'BreakfastDaily', 'type': 'select',
     'question': 'Do you eat breakfast every day?',
     'options': ['Yes', 'No']},
    {'name': 'ScreenTimeHours', 'type': 'number',
     'question': 'How many hours of screen time per day?',
     'validation': {'min': 0, 'max': 24}},
    {'name': 'PhysicalActivityHours', 'type': 'number',
     'question': 'How many hours of physical activity per day?',
     'validation': {'min': 0, 'max': 24}},
    {'name': 'PlaysSport', 'type': 'select',
     'question': 'Do you play any sports?',
     'options': ['Yes', 'No']},
    {'name': 'MentalHealthScore', 'type': 'number',
     'question': 'Rate your mental health on a scale of 1–10.',
     'validation': {'min': 1, 'max': 10}},
    {'name': 'ParentalEducationLevel', 'type': 'select',
     'question': 'What is your parental education level?',
     'options': ['High school', 'Graduate', 'Postgrad']},
    {'name': 'HouseholdIncomeLevel', 'type': 'select',
     'question': 'What is your household income level?',
     'options': ['Low', 'Medium', 'High']},
    {'name': 'StudyEnvironmentRating', 'type': 'number',
     'question': 'Rate your study environment (1–5).',
     'validation': {'min': 1, 'max': 5}},
    {'name': 'FriendSupportScore', 'type': 'number',
     'question': 'Rate the emotional support from friends (1–10).',
     'validation': {'min': 1, 'max': 10}},
    {'name': 'ParticipatesInClubs', 'type': 'select',
     'question': 'Do you participate in any clubs?',
     'options': ['Yes', 'No']},
    {'name': 'PartTimeWork', 'type': 'select',
     'question': 'Do you do any part‑time work?',
     'options': ['Yes', 'No']},
]

@app.get("/", response_class=HTMLResponse)
async def chat_ui(request: Request):
    return templates.TemplateResponse("chat.html", {
        "request": request,
        "fields": FIELDS
    })

@app.post("/predict_json")
async def predict_json(payload: dict):
    # collect + cast
    data = {}
    for f in FIELDS:
        key = f["name"]
        if key not in payload:
            raise HTTPException(400, f"Missing field: {key}")
        val = payload[key]
        if f["type"] == "number":
            try:
                val = float(val)
            except:
                raise HTTPException(400, f"{key} must be numeric")
        data[key] = val

    # validate ranges
    for f in FIELDS:
        if f["type"] == "number" and "validation" in f:
            v = f["validation"]
            if not (v["min"] <= data[f["name"]] <= v["max"]):
                raise HTTPException(400,
                    f"{f['name']} must be between {v['min']} and {v['max']}")

    df = pd.DataFrame([data])
    score = model.predict(df)[0]
    return JSONResponse({"predicted": round(float(score), 2)})