File size: 5,386 Bytes
3f29e4d 5bf80cd 3f29e4d b3ab79e 3f29e4d b3ab79e 3f29e4d b3ab79e 3f29e4d b3ab79e 3f29e4d b3ab79e 3f29e4d 5bf80cd 3f29e4d 5bf80cd 3f29e4d 5bf80cd |
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 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 |
import gradio as gr
import joblib
import torch
import torch.nn as nn
import torch.nn.functional as F
import json
from transformers import AutoTokenizer, AutoModelForSequenceClassification
print("Pokrećem aplikaciju...")
# --- Učitavanje modela i riječnika ---
svm_pipeline = joblib.load("svm_pipeline.pkl")
with open("word2idx.json", "r", encoding="utf-8") as f:
word2idx = json.load(f)
class CNNModel(nn.Module):
def __init__(self, vocab_size, embed_dim=300, num_classes=3, kernel_sizes=[3,4,5], num_filters=128):
super(CNNModel, self).__init__()
self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=0)
self.convs = nn.ModuleList([
nn.Conv2d(1, num_filters, (k, embed_dim)) for k in kernel_sizes
])
self.dropout = nn.Dropout(0.5)
self.fc = nn.Linear(num_filters * len(kernel_sizes), num_classes)
def forward(self, x):
x = self.embedding(x).unsqueeze(1)
convs = [F.relu(conv(x)).squeeze(3) for conv in self.convs]
pools = [F.max_pool1d(c, c.size(2)).squeeze(2) for c in convs]
x = torch.cat(pools, 1)
x = self.dropout(x)
return self.fc(x)
class GRUModel(nn.Module):
def __init__(self, vocab_size, embed_dim=300, hidden_dim=256, num_layers=1, num_classes=3):
super(GRUModel, self).__init__()
self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=0)
self.gru = nn.GRU(embed_dim, hidden_dim, num_layers=num_layers, batch_first=True)
self.fc = nn.Linear(hidden_dim, num_classes)
def forward(self, x):
x = self.embedding(x)
_, h_n = self.gru(x)
out = self.fc(h_n[-1])
return out
vocab_size = len(word2idx) + 1
embed_dim = 300
num_classes = 3
cnn_model = CNNModel(vocab_size, embed_dim, num_classes)
cnn_model.load_state_dict(torch.load("cnn_model.pt", map_location=torch.device('cpu')))
cnn_model.eval()
gru_model = GRUModel(vocab_size, embed_dim, hidden_dim=256, num_layers=1, num_classes=num_classes)
gru_model.load_state_dict(torch.load("gru_model.pt", map_location=torch.device('cpu')))
gru_model.eval()
bert_tokenizer = AutoTokenizer.from_pretrained("my_finetuned_model")
bert_model = AutoModelForSequenceClassification.from_pretrained("my_finetuned_model")
bert_model.eval()
label_names = {0: 'pozitivno', 1: 'neutralno', 2: 'negativno'}
def text_to_indices(text, max_len=100):
tokens = text.lower().split()
indices = [word2idx.get(token, 0) for token in tokens]
if len(indices) < max_len:
indices += [0] * (max_len - len(indices))
else:
indices = indices[:max_len]
tensor = torch.tensor([indices], dtype=torch.long)
return tensor
def predict_svm(text):
proba = svm_pipeline.predict_proba([text])[0]
pred = svm_pipeline.classes_[proba.argmax()]
return f"{label_names[pred]} (p={proba.max():.2f})"
def predict_cnn(text):
with torch.no_grad():
inputs = text_to_indices(text)
outputs = cnn_model(inputs)
probs = F.softmax(outputs, dim=1)
pred = torch.argmax(probs, dim=1).item()
confidence = probs[0][pred].item()
return f"{label_names[pred]} (p={confidence:.2f})"
def predict_gru(text):
with torch.no_grad():
inputs = text_to_indices(text)
outputs = gru_model(inputs)
probs = F.softmax(outputs, dim=1)
pred = torch.argmax(probs, dim=1).item()
confidence = probs[0][pred].item()
return f"{label_names[pred]} (p={confidence:.2f})"
def predict_bert(text):
inputs = bert_tokenizer(text, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
outputs = bert_model(**inputs)
probs = F.softmax(outputs.logits, dim=1)
pred = torch.argmax(probs, dim=1).item()
confidence = probs[0][pred].item()
return f"{label_names[pred]} (p={confidence:.2f})"
def predict_all(text):
return (
predict_svm(text),
predict_cnn(text),
predict_gru(text),
predict_bert(text),
)
def clear_all():
return "", "", "", "", ""
with gr.Blocks() as demo:
# Naslov veći, centriran
gr.Markdown(
"""
<h1 style="text-align: center; font-size: 48px; margin-bottom: 5px;">Analiza sentimenta</h1>
<p style="text-align: center; font-size: 16px; margin-top: 0;">Predikcije koriste SVM, CNN, GRU i BERTić modele.</p>
""",
elem_id="naslov"
)
input_text = gr.Textbox(lines=3, label="Unesite rečenicu za analizu:")
with gr.Row():
submit_btn = gr.Button("Submit", variant="primary")
clear_btn = gr.Button("Clear", variant="secondary")
with gr.Row():
with gr.Column():
gr.Markdown("### Machine Learning")
svm_output = gr.Textbox(label="SVM (RBF)")
with gr.Column():
gr.Markdown("### Deep Learning")
cnn_output = gr.Textbox(label="CNN")
gru_output = gr.Textbox(label="GRU")
with gr.Column():
gr.Markdown("### Transformers")
bert_output = gr.Textbox(label="BERTić")
submit_btn.click(fn=predict_all, inputs=input_text, outputs=[svm_output, cnn_output, gru_output, bert_output])
clear_btn.click(fn=clear_all, inputs=None, outputs=[input_text, svm_output, cnn_output, gru_output, bert_output])
if __name__ == "__main__":
demo.launch(share=True)
|