app.py

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()

# CroSlo model/tokenizer
croslo_tokenizer = AutoTokenizer.from_pretrained("CroSlo")
croslo_model = AutoModelForSequenceClassification.from_pretrained("CroSlo")
croslo_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_croslo(text):
    inputs = croslo_tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
    with torch.no_grad():
        outputs = croslo_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),
        predict_croslo(text),
    )

def clear_all():
    return "", "", "", "", "", ""

with gr.Blocks() as demo:
    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, BERTić i CroSlo 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")
            croslo_output = gr.Textbox(label="CroSlo BERT")

    submit_btn.click(fn=predict_all, inputs=input_text, outputs=[svm_output, cnn_output, gru_output, bert_output, croslo_output])
    clear_btn.click(fn=clear_all, inputs=None, outputs=[input_text, svm_output, cnn_output, gru_output, bert_output, croslo_output])

if __name__ == "__main__":
    demo.launch(share=True)