from fastapi import FastAPI
from pydantic import BaseModel
import requests
import os

app = FastAPI(title="Forex Sentiment API", version="2.0")

# ===============================
# Konfigurasi
# ===============================
HF_ROUTER_URL = "https://router.huggingface.co/hf-inference/models"
HF_API_KEY = os.getenv("HF_API_KEY")

FINBERT_MODEL = "ProsusAI/finbert"
LONGFORMER_MODEL = "Miruzen/LongFormer_Skripsi"


# ===============================
# Input Schema
# ===============================
class InputData(BaseModel):
    title: str | None = None
    content: str | None = None


# ===============================
# Helper Functions
# ===============================
def call_hf_model(model_name: str, text: str):
    """Kirim teks ke model Hugging Face menggunakan router API baru"""
    headers = {
        "Authorization": f"Bearer {HF_API_KEY}",
        "Content-Type": "application/json",
    }

    payload = {
        "inputs": text,
        "options": {"wait_for_model": True}
    }

    url = f"{HF_ROUTER_URL}/{model_name}"
    response = requests.post(url, headers=headers, json=payload)

    if response.status_code != 200:
        raise Exception(
            f"HF API error ({response.status_code}): {response.text}"
        )

    return response.json()


def extract_scores(predictions):
    """Convert HF model output into {positive, neutral, negative} dict."""
    scores = {"positive": 0.0, "neutral": 0.0, "negative": 0.0}

    # Router API sometimes returns either list of lists or single list
    data = predictions[0] if isinstance(predictions, list) else predictions
    if isinstance(data, list) and len(data) > 0 and isinstance(data[0], dict):
        for item in data:
            label = item.get("label", "").lower()
            if "pos" in label:
                scores["positive"] = item["score"]
            elif "neg" in label:
                scores["negative"] = item["score"]
            elif "neu" in label:
                scores["neutral"] = item["score"]

    dominant = max(scores, key=scores.get)
    return {"label": dominant, "scores": scores}


# ===============================
# Main Endpoint
# ===============================
@app.post("/analyze")
def analyze(data: InputData):
    print("🚀 Incoming request:", data)
    print("📡 Using router endpoint:", HF_ROUTER_URL)
    print("🔑 Using key starts with:", HF_API_KEY[:10])

    result = {}
    errors = []


    if data.title:
        try:
            finbert_out = call_hf_model(FINBERT_MODEL, data.title)
            result["title"] = extract_scores(finbert_out)
        except Exception as e:
            errors.append(f"Title analysis error: {e}")

    if data.content:
        try:
            longformer_out = call_hf_model(LONGFORMER_MODEL, data.content)
            result["content"] = extract_scores(longformer_out)
        except Exception as e:
            errors.append(f"Content analysis error: {e}")

    mood_score = (
        result.get("title", {}).get("scores", {}).get("positive", 0)
        + result.get("content", {}).get("scores", {}).get("positive", 0)
        - result.get("title", {}).get("scores", {}).get("negative", 0)
        - result.get("content", {}).get("scores", {}).get("negative", 0)
    )

    return {
        "mood_score": mood_score,
        "details": result,
        "errors": errors,
        "status": "ok" if not errors else "partial"
    }


@app.get("/")
def root():
    return {"message": "Forex Sentiment API active via router.huggingface.co!"}