Create app.py

app.py

@@ -0,0 +1,67 @@
+import time
+import torch
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+MODEL_ID = "ruSpamModels/ruSpam-Qwen-0.5B-50k"
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen2.5-0.5B-Instruct",
+    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+    device_map=device,
+    trust_remote_code=True,
+)
+
+model = PeftModel.from_pretrained(base_model, MODEL_ID)
+model.eval()
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+def classify(message):
+    prompt = (
+        "You are a spam classifier.\n"
+        "Answer with one word: spam or ham.\n\n"
+        f"Message:\n{message}\n\n"
+        "Answer:"
+    )
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+
+    start = time.time()
+    with torch.no_grad():
+        out = model.generate(
+            **inputs,
+            max_new_tokens=1,
+            do_sample=False,
+            temperature=0.01,
+            pad_token_id=tokenizer.eos_token_id,
+            eos_token_id=tokenizer.eos_token_id,
+        )
+
+    elapsed = (time.time() - start) * 1000
+
+    new_token_id = out[0, inputs["input_ids"].shape[1]]
+    answer = tokenizer.decode(new_token_id).strip().lower()
+
+    if answer.startswith("spam"):
+        label = "SPAM"
+    elif answer.startswith("ham"):
+        label = "HAM"
+    else:
+        label = "UNKNOWN"
+
+    return f"{label} ({elapsed:.1f} ms)"
+
+iface = gr.Interface(
+    fn=classify,
+    inputs=gr.Textbox(lines=4, placeholder="Введите сообщение"),
+    outputs=gr.Textbox(label="Результат"),
+    title="ruSpam Qwen 0.5B",
+    description="Классификация сообщений: SPAM / HAM",
+)
+
+if __name__ == "__main__":
+    iface.launch()