import os
import io
import json
import requests
from flask import Flask, request, jsonify
from flask_cors import CORS

# Optional: but good for local debugging if you run this file directly
try:
    from dotenv import load_dotenv
    load_dotenv()
except ImportError:
    pass

app = Flask(__name__)
CORS(app)

# ── Configuration (with Defaults to prevent crashes) ────────────────
TWILIO_ACCOUNT_SID      = os.getenv("TWILIO_ACCOUNT_SID", "")
TWILIO_AUTH_TOKEN       = os.getenv("TWILIO_AUTH_TOKEN", "")
TWILIO_FROM_NUMBER      = os.getenv("TWILIO_FROM_NUMBER", "+17125825991")
YOUR_PHONE_NUMBER       = os.getenv("YOUR_PHONE_NUMBER", "+919047432845")
AI_CONFIDENCE_THRESHOLD = float(os.getenv("AI_CONFIDENCE_THRESHOLD", 0.5))

# HuggingFace Inference API Config
HF_API_URL = "https://api-inference.huggingface.co/models/umm-maybe/AI-image-detector"
HF_API_TOKEN = os.getenv("HF_API_TOKEN", "")

# ── Routes ──────────────────────────────────────────────────

@app.route("/")
def home():
    return "VisionAI Backend is Live. Use /status or /analyze."

@app.route("/status")
def status():
    return jsonify({
        "status": "online",
        "deployment": "vercel",
        "alerts_configured": bool(TWILIO_ACCOUNT_SID and TWILIO_AUTH_TOKEN),
        "target_phone": YOUR_PHONE_NUMBER,
        "threshold": AI_CONFIDENCE_THRESHOLD * 100
    })

@app.route("/analyze", methods=["POST"])
def analyze():
    if "image" not in request.files:
        return jsonify({"error": "No image provided"}), 400

    file = request.files["image"]
    img_data = file.read()

    # Call HuggingFace Inference API
    headers = {}
    if HF_API_TOKEN:
        headers["Authorization"] = f"Bearer {HF_API_TOKEN}"

    try:
        response = requests.post(HF_API_URL, headers=headers, data=img_data)
        results = response.json()

        # Handle API loading state (model taking time to boot)
        if isinstance(results, dict) and "estimated_time" in results:
            return jsonify({
                "error": "Model is warming up on HuggingFace. Please try again in 20 seconds.",
                "wait_time": results["estimated_time"]
            }), 503

        if isinstance(results, dict) and "error" in results:
            return jsonify({"error": results["error"]}), 502

        # Parse results
        scores = {r["label"].lower(): r["score"] for r in results}
        art_score = scores.get("artificial", 0.0)
        real_score = scores.get("real", 1.0 - art_score)
        is_ai = art_score > AI_CONFIDENCE_THRESHOLD

    except Exception as e:
        return jsonify({"error": f"Analysis failed: {str(e)}"}), 502

    # ── Twilio Alert ────────────────────────────────────────
    call_placed = False
    call_sid = None
    call_error = None

    if is_ai and TWILIO_ACCOUNT_SID and TWILIO_AUTH_TOKEN:
        try:
            from twilio.rest import Client
            client = Client(TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN)
            call = client.calls.create(
                twiml=f"""<Response>
                    <Say voice="alice">
                        Alert! An AI generated image has been detected.
                        Confidence is {round(art_score * 100)} percent.
                    </Say>
                </Response>""",
                to=YOUR_PHONE_NUMBER,
                from_=TWILIO_FROM_NUMBER,
            )
            call_placed = True
            call_sid = call.sid
        except Exception as e:
            call_error = str(e)

    return jsonify({
        "filename": file.filename,
        "is_ai": is_ai,
        "artificial_score": round(art_score * 100, 1),
        "real_score": round(real_score * 100, 1),
        "all_scores": [{"label": r["label"], "score": round(r["score"] * 100, 1)} for r in results],
        "threshold": AI_CONFIDENCE_THRESHOLD * 100,
        "call_placed": call_placed,
        "call_sid": call_sid,
        "call_error": call_error
    })

# The 'app' object is automatically used by Vercel