app.py

import torch
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
import gradio as gr

# モデルとトークナイザーのロード
model_name = "kix-intl/elon-musk-detector"  # あなたのモデル名に置き換えてください
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

def classify_tweet(tweet):
    inputs = tokenizer(tweet, return_tensors="pt", truncation=True, padding=True, max_length=128).to(device)
    with torch.no_grad():
        outputs = model(**inputs)
    probabilities = torch.softmax(outputs.logits, dim=1)
    prediction = torch.argmax(probabilities, dim=1).item()
    confidence = probabilities[0][prediction].item()
    
    if prediction == 1:
        return f"Elon Musk (Confidence: {confidence:.2f})"
    else:
        return f"Not Elon Musk (Confidence: {confidence:.2f})"

# Gradioインターフェースの作成
iface = gr.Interface(
    fn=classify_tweet,
    inputs=gr.Textbox(lines=2, placeholder="Enter a tweet here..."),
    outputs=gr.Textbox(),
    title="Elon Musk Tweet Classifier",
    description="This model classifies whether a given tweet is likely to be written by Elon Musk or not.",
    examples=[
        ["Tesla's new model is amazing!"],
        ["Just had a great coffee at my favorite local café."],
        ["Sending astronauts to Mars is the next big challenge for humanity."]
    ]
)

# Hugging Faceにデプロイする場合は、以下の行のコメントを解除してください
iface.launch()