app.py

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

MODEL_NAME = "kaixkhazaki/turkish-sentiment"
device = 0 if torch.cuda.is_available() else -1

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
sentiment = pipeline("text-classification", model=model, tokenizer=tokenizer, return_all_scores=True, device=device)

def analyze(text):
    results = sentiment(text)[0]
    results_sorted = sorted(results, key=lambda x: x["score"], reverse=True)
    formatted = "\n".join([f"{r['label']}: {r['score']:.3f}" for r in results_sorted])
    return formatted

demo = gr.Interface(
    fn=analyze,
    inputs=gr.Textbox(lines=3, placeholder="Bir metin yazın..."),
    outputs=gr.Textbox(label="Duygu ve Skorlar"),
    title="Türkçe Duygu Analizi (Skorlarla)",
    description="Her etiket için olasılık skorlarını gösterir."
)

demo.launch()