app.py

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

# model
model_name = "fc63/gender_prediction_model_from_text"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model.eval()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# translate pipeline (multilingual → İngilizce)
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-mul-en")

def predict(text, language):
    original_text = text
    if language == "Not English":
        try:
            translated = translator(text)[0]["translation_text"]
        except Exception as e:
            return f"Translation failed: {e}"
    else:
        translated = text

    # model inference
    inputs = tokenizer(translated, return_tensors="pt", truncation=True, padding=True, max_length=128).to(device)
    with torch.no_grad():
        outputs = model(**inputs)
        probs = F.softmax(outputs.logits, dim=1)
        pred = torch.argmax(probs, dim=1).item()
        inference = "Feminine" if pred == 0 else "Masculine"
        confidence = round(probs[0][pred].item() * 100, 1)

    return f"{inference} (Confidence: {confidence}%)"

# interface / arayüz
demo = gr.Interface(
    fn=predict,
    inputs=[
        gr.Textbox(label="Enter your text here", lines=4, placeholder="Type something..."),
        gr.Radio(["English", "Not English"], label="Text Language", value="English")
    ],
    outputs="text",
    title="Masculine Feminine Text Classifier",
    description="Classifies English or translated text based on linguistic patterns associated with masculine and feminine writing styles."
)

demo.launch()