import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
import torch.nn.functional as F

# 🌍 Model yükle
MODEL_NAME = "cardiffnlp/twitter-xlm-roberta-base-sentiment"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)

labels = ["negative", "neutral", "positive"]

def analyze_text(text):
    if not text.strip():
        return {"label": "empty", "emoji": "💬", "scores": {}}

    inputs = tokenizer(text, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
        probs = F.softmax(outputs.logits, dim=1)

    scores = {labels[i]: round(float(probs[0][i]), 3) for i in range(len(labels))}
    top_label = max(scores, key=scores.get)

    emoji_map = {
        "positive": "😄",
        "neutral": "😐",
        "negative": "😞",
        "empty": "💬"
    }

    return {
        "label": top_label,
        "emoji": emoji_map[top_label],
        "scores": scores
    }

# ✅ GRADIO arayüzü (3.x sürümü)
iface = gr.Interface(
    fn=analyze_text,
    inputs=gr.Textbox(label="Metin Gir"),
    outputs="json",
    title="🌍 Multilingual Sentiment Analyzer",
    description="Sentiment analysis for multiple languages."
)

# ✅ ÖNEMLİ: 3.x'te launch otomatik predict API oluşturur
iface.launch(server_name="0.0.0.0", server_port=7860)