add app.py

app.py

@@ -0,0 +1,41 @@
+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()

requirements.txt

@@ -0,0 +1,3 @@
+gradio
+torch
+transformers