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b3b4b75 5d5427a b3b4b75 25d95d8 b3b4b75 5d5427a b3b4b75 5d5427a b3b4b75 5d5427a b3b4b75 5d5427a b3b4b75 5d5427a b3b4b75 bd4ca57 b3b4b75 bd4ca57 b3b4b75 5d5427a 276b006 bd4ca57 276b006 f601439 276b006 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | import torch
import torch.nn as nn
import torch.nn.functional as F
import gradio as gr
from huggingface_hub import hf_hub_download
REPO_ID = "SentilyticsOPJ/lstm_model"
FILENAME = "lstm_model.pt"
MAX_LEN = 80
EMBEDDING_DIM = 300
LSTM_UNITS = 96
SPATIAL_DROPOUT = 0.3
DROPOUT = 0.5
class BiLSTM(nn.Module):
def __init__(self, vocab_size, num_classes, embedding_matrix=None,
embedding_dim=EMBEDDING_DIM, lstm_units=LSTM_UNITS,
spatial_dropout=SPATIAL_DROPOUT, dropout=DROPOUT,
padding_idx=0):
super().__init__()
self.padding_idx = padding_idx
self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=padding_idx)
if embedding_matrix is not None:
self.embedding.weight.data.copy_(torch.from_numpy(embedding_matrix))
self.spatial_dropout = nn.Dropout1d(spatial_dropout)
self.lstm = nn.LSTM(
input_size=embedding_dim,
hidden_size=lstm_units,
num_layers=1,
batch_first=True,
bidirectional=True,
)
self.attn = nn.Linear(lstm_units * 2, 1)
self.dropout = nn.Dropout(dropout)
self.fc = nn.Linear(lstm_units * 2, num_classes)
def forward(self, x):
emb = self.embedding(x)
emb = emb.transpose(1, 2)
emb = self.spatial_dropout(emb)
emb = emb.transpose(1, 2)
out, _ = self.lstm(emb)
scores = self.attn(out).squeeze(-1)
mask = (x == self.padding_idx)
mask[:, 0] = False
scores = scores.masked_fill(mask, float("-inf"))
weights = F.softmax(scores, dim=1).unsqueeze(-1)
context = (out * weights).sum(dim=1)
return self.fc(self.dropout(context))
path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
obj = torch.load(path, map_location="cpu", weights_only=False)
sd = obj["state_dict"]
word2id = obj["word2id"]
labels = obj["label_classes"]
max_len = obj["max_len"]
model = BiLSTM(len(word2id), len(labels))
model.load_state_dict(sd)
model.eval()
def predict(text):
if not text.strip():
return ""
ids = [word2id.get(t, 1) for t in text.lower().split()][:max_len]
ids += [0] * (max_len - len(ids))
with torch.no_grad():
probs = torch.softmax(model(torch.tensor(ids).unsqueeze(0)), dim=1)[0]
return labels[int(probs.argmax())]
demo = gr.Interface(
fn=predict,
inputs=gr.Textbox(lines=3, placeholder="Unesi tekst...", label="Text"),
outputs=gr.Label(num_top_classes=1, label="Sentiment"),
title="Sentiment Analysis (BiLSTM, fastText hr)",
description="Five-class sentiment: mixed, negative, neutral, positive, sarcastic.",
examples=["Volim kavu", "Ovaj doktor je loš", "Dan je bio ok"],
)
demo.launch() |