import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

# Türkçe duygu analizi modeli
model_name = "savasy/bert-base-turkish-sentiment-cased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
sentiment_model = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

def analyze_text(text):
    if not text.strip():
        return "Lütfen bir metin girin."
    result = sentiment_model(text)[0]
    label = result["label"]
    score = round(result["score"], 2)
    # Model etiketleri: 1 = Olumlu, 0 = Olumsuz
    if label == "LABEL_1":
        label_text = "Pozitif 😊"
    else:
        label_text = "Negatif 😞"
    return f"{label_text} ({score})"

iface = gr.Interface(
    fn=analyze_text,
    inputs=gr.Textbox(label="Mesaj Girin"),
    outputs=gr.Textbox(label="Duygu Sonucu"),
    title="Türkçe Chat Sentiment AI",
    description="Girilen Türkçe metnin pozitif veya negatif duygu analizini yapar."
)

iface.launch()