Create app.py

app.py

@@ -0,0 +1,46 @@
+import gradio as gr
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+import torch
+
+model_id = "naver/ecir23-scratch-tydi-russian-splade"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForMaskedLM.from_pretrained(model_id)
+
+def compute_vector(question: str):
+    """
+    Computes a vector from logits and attention mask using ReLU, log, and max operations.
+
+    Args:
+    logits (torch.Tensor): The logits output from a model.
+    attention_mask (torch.Tensor): The attention mask corresponding to the input tokens.
+
+    Returns:
+    torch.Tensor: Computed vector.
+    """
+    # Извлекаем 'inputs' из полученного JSON
+    text = question.get('inputs', '')
+    # Проверяем, что text это строка
+    if not isinstance(text, str):
+        return {"error": "Неправильный формат входных данных. Ожидается строка."}
+
+    tokens = tokenizer(text, truncation=True, padding=True, max_length=2048, return_tensors="pt")
+    output = model(**tokens)
+    logits, attention_mask = output.logits, tokens.attention_mask
+    relu_log = torch.log(1 + torch.relu(logits))
+    weighted_log = relu_log * attention_mask.unsqueeze(-1)
+    max_val, _ = torch.max(weighted_log, dim=1)
+    vec = max_val.squeeze()
+    indices = vec.nonzero().numpy().flatten().tolist()
+    values = vec.detach().numpy()[indices].tolist()
+
+    return indices, values
+
+sparse_int = gr.Interface(
+    fn=compute_vector,
+    inputs=gr.JSON(label="Введите JSON в формате {'inputs': 'sentence1'}"),
+    outputs="json",
+    title="Sparse vector",
+    description="Compute sparse vector using the 'naver/ecir23-scratch-tydi-russian-splade' model under the hood!"
+)
+
+sparse_int.launch()