app.py

import gradio as gr
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import numpy as np
from huggingface_hub import hf_hub_download
import pickle
import json
import nltk
nltk.download('punkt')
nltk.download('punkt_tab')
from nltk.tokenize import word_tokenize


# ========== Load Your Models ==========

# ML Models
#logreg
ml1_model = pickle.load(open(hf_hub_download("InfZnDipl/ML_LogReg", "model.pkl"), "rb"))
ml1_vectorizer = pickle.load(open(hf_hub_download("InfZnDipl/ML_LogReg", "vectorizer.pkl"), "rb"))

#Multinomial Naive Bayes
ml2_model = pickle.load(open(hf_hub_download("InfZnDipl/ML_N_Bayes", "model.pkl"), "rb"))
ml2_vectorizer = pickle.load(open(hf_hub_download("InfZnDipl/ML_N_Bayes", "vectorizer.pkl"), "rb"))

# DL Models (LSTM, GRU)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#dl1_model = torch.load(hf_hub_download("InfZnDipl/RNN_LSTM_TRAIN", "RNN-T-t3.pt"), map_location=device)
dl2_model = torch.load(hf_hub_download("InfZnDipl/CNN_TRAIN", "CNN1-T-t11.pt"), map_location=device)

class SentimentCNN(nn.Module):
    def __init__(self, embedding_matrix, output_dim=3, filter_sizes=[3,4,5], num_filters=350, dropout=0.458):
        super().__init__()
        vocab_size, embedding_dim = embedding_matrix.shape
        self.embedding = nn.Embedding.from_pretrained(torch.FloatTensor(embedding_matrix), freeze=False)

        self.convs = nn.ModuleList([
            nn.Conv2d(1, num_filters, (fs, embedding_dim)) for fs in filter_sizes
        ])

        self.fc = nn.Linear(num_filters * len(filter_sizes), output_dim)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        x = self.embedding(x).unsqueeze(1)  # [B, 1, T, D]
        x = [torch.relu(conv(x)).squeeze(3) for conv in self.convs]  # conv outputs: [B, F, T]
        x = [torch.max(i, dim=2)[0] for i in x]  # max-pooling: [B, F]
        x = torch.cat(x, dim=1)  # [B, F * len(filter_sizes)]
        x = self.dropout(x)
        return self.fc(x)  # [B, C]

with open(hf_hub_download("InfZnDipl/CNN_TRAIN", "vocab_cnn_nltk_TRAIN.json"), "r") as f:
    vocab = json.load(f)

embedding_matrix = np.load(hf_hub_download("InfZnDipl/CNN_TRAIN", "embedding_matrix.npy"))
embedding_matrix = torch.tensor(embedding_matrix, dtype=torch.float)

dl2_model = SentimentCNN(
    embedding_matrix=embedding_matrix,
    num_filters=275,  # <-- važno: iz HPO/sačuvanog modela
    output_dim=3,
    filter_sizes=[3, 4, 5],
    dropout=0.45827226824902384  # <-- tačan dropout iz HPO
)

dl2_model.load_state_dict(torch.load(hf_hub_download("InfZnDipl/CNN_TRAIN", "CNN1-T-t11.pt"), map_location=device))
dl2_model.to(device)
dl2_model.eval()

PAD_IDX = 0
UNK_IDX = vocab.get("<UNK>", 1)
MAX_LEN = 100

def preprocess_dl_text(text):
    tokens = word_tokenize(text.lower())
    ids = [vocab.get(token, UNK_IDX) for token in tokens]
    length = len(ids)

    if length < MAX_LEN:
        ids += [PAD_IDX] * (MAX_LEN - length)
    else:
        ids = ids[:MAX_LEN]
        length = MAX_LEN

    ids_tensor = torch.tensor([ids], dtype=torch.long)         # [1, max_len]
    length_tensor = torch.tensor([length], dtype=torch.long)   # [1]
    #print("Debug...", tokens, ids, "|||", ids_tensor, length_tensor)
    return ids_tensor, length_tensor


# RNN section
class LSTM(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, n_layers,
                 bidirectional, dropout_rate, pad_index, pretrained_embeddings=None):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=pad_index)
        if pretrained_embeddings is not None:
            self.embedding.weight.data.copy_(pretrained_embeddings)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, n_layers,
                            bidirectional=bidirectional, dropout=dropout_rate if n_layers > 1 else 0,
                            batch_first=True)
        self.fc = nn.Linear(hidden_dim * 2 if bidirectional else hidden_dim, output_dim)
        self.dropout = nn.Dropout(dropout_rate)

    def forward(self, ids, length):
        embedded = self.dropout(self.embedding(ids))
        packed_embedded = pack_padded_sequence(embedded, length.cpu(), batch_first=True, enforce_sorted=False)
        _, (hidden, _) = self.lstm(packed_embedded)
        if self.lstm.bidirectional:
            hidden = self.dropout(torch.cat([hidden[-2], hidden[-1]], dim=1))
        else:
            hidden = self.dropout(hidden[-1])
        return self.fc(hidden)

rnn_vocab_path = hf_hub_download("InfZnDipl/RNN_LSTM_TRAIN", "vocab-NOVO-6.json")
rnn_embeddings_path = hf_hub_download("InfZnDipl/RNN_LSTM_TRAIN", "embeddings-NOVO-6.pt")
rnn_weights_path = hf_hub_download("InfZnDipl/RNN_LSTM_TRAIN", "RNN-NOVO-T-t2-6.pt")
rnn_params_path = hf_hub_download("InfZnDipl/RNN_LSTM_TRAIN", "RNN-params-NOVO-6.json")

with open(rnn_vocab_path, "r") as f:
    rnn_vocab = json.load(f)

rnn_embeddings = torch.load(rnn_embeddings_path, map_location=device)
embedding_dim = rnn_embeddings.shape[1]
output_dim = 3
bidirectional = True
PAD_IDX = 0
UNK_IDX = rnn_vocab.get("<UNK>", 1)

with open(rnn_params_path, "r") as f:
    rnn_params = json.load(f)

dl1_model = LSTM(
    vocab_size=rnn_embeddings.shape[0],
    embedding_dim=embedding_dim,
    hidden_dim=rnn_params["hidden_dim"],
    output_dim=output_dim,
    n_layers=rnn_params["n_layers"],
    bidirectional=bidirectional,
    dropout_rate=rnn_params["dropout_rate"],
    pad_index=PAD_IDX,
    pretrained_embeddings=rnn_embeddings
).to(device)

dl1_model.load_state_dict(torch.load(rnn_weights_path, map_location=device))
dl1_model.eval()

def preprocess_rnn_text(text):
    tokens = word_tokenize(text.lower())
    ids = [rnn_vocab.get(token, UNK_IDX) for token in tokens]
    length = len(ids)

    if length < MAX_LEN:
        ids += [PAD_IDX] * (MAX_LEN - length)
    else:
        ids = ids[:MAX_LEN]
        length = MAX_LEN

    ids_tensor = torch.tensor([ids], dtype=torch.long)
    length_tensor = torch.tensor([length], dtype=torch.long)
    return ids_tensor.to(device), length_tensor.to(device)

# Transformer Models (e.g., BERT) - Ovo radi, ne diraj
from transformers import AutoModelForSequenceClassification, AutoTokenizer

trans1_tokenizer = AutoTokenizer.from_pretrained("InfZnDipl/bertic-sentiment_TRAIN")
trans1_model = AutoModelForSequenceClassification.from_pretrained("InfZnDipl/bertic-sentiment_TRAIN")

trans2_tokenizer = AutoTokenizer.from_pretrained("InfZnDipl/croslo-sentiment_TRAIN")
trans2_model = AutoModelForSequenceClassification.from_pretrained("InfZnDipl/croslo-sentiment_TRAIN")

# ========== Prediction Functions ==========

def get_label(index):
    return ["Positive", "Neutral", "Negative"][index]

def predict_ml(text, model, vectorizer):
    X = vectorizer.transform([text])
    pred = model.predict(X)[0]
    return get_label(pred)

def predict_dl(text, model, model_type="lstm"):
    if model_type == "lstm":
        ids_tensor, length_tensor = preprocess_rnn_text(text)
    else:
        ids_tensor, length_tensor = preprocess_dl_text(text)

    ids_tensor = ids_tensor.to(device)

    with torch.no_grad():
        if model_type == "lstm":
            logits = model(ids_tensor, length_tensor)
        else:
            logits = model(ids_tensor)

        if isinstance(logits, tuple):
            logits = logits[0]

        pred = torch.argmax(logits, dim=1).item()
    return get_label(pred)


def predict_transformer(text, tokenizer, model):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        logits = model(**inputs).logits
        pred = torch.argmax(logits, dim=1).item()
    return get_label(pred)

def analyze_sentiment(text):
    results = {
        "ML Logistic Regression": predict_ml(text, ml1_model, ml1_vectorizer),
        "ML Naive Bayes": predict_ml(text, ml2_model, ml2_vectorizer),
        "DL Model: RNN-LSTM": predict_dl(text, dl1_model, model_type="lstm"),
        "DL Model: CNN": predict_dl(text, dl2_model, model_type="cnn"),
        "Transformer: BERTIC": predict_transformer(text, trans1_tokenizer, trans1_model),
        "Transformer: CROSLO": predict_transformer(text, trans2_tokenizer, trans2_model),
    }
    return results

# ========== UI with Gradio ==========
#print(predict_dl("Ovo je najbolji film koji sam ikada gledao!", dl2_model, model_type="cnn"))
#print(predict_dl("Užasno dosadno iskustvo.", dl2_model, model_type="cnn"))

def gradio_interface(text):
    results = analyze_sentiment(text)
    return [
        results["ML Logistic Regression"],
        results["ML Naive Bayes"],
        results["DL Model: RNN-LSTM"],
        results["DL Model: CNN"],
        results["Transformer: BERTIC"],
        results["Transformer: CROSLO"]
    ]

with gr.Blocks(css="""
    .gr-button-primary {
        background-color: orange !important;
        border: none !important;
        color: white !important;
    }
    #title, .section-title {
        text-align: center;
    }
    .gr-textbox {
        border: 1px solid white !important;
    }
""") as demo:
    gr.Markdown(
        """
        # Sentiment Analysis Demo / Analiza Sentimenta  
        Enter a sentence in **Croatian** to see how 6 different models classify its sentiment: **positive**, **neutral**, or **negative**.  
        Unesite rečenicu na **hrvatskom jeziku** da biste vidjeli kako je klasificiraju 6 različitih modela: **pozitivno**, **neutralno** ili **negativno**.
        """,
        elem_id="title"
    )

    with gr.Row():
        with gr.Column(scale=1):
            text_input = gr.Textbox(
                label="Enter a sentence / Unesite rečenicu",
                placeholder="Npr. Ovo je najbolji film ikad!",
                lines=2
            )

    submit_btn = gr.Button("Analyze / Analiziraj", variant="primary")

    with gr.Row():
        with gr.Column():
            gr.Markdown("### Machine Learning", elem_classes=["section-title"])
            ml1_out = gr.Textbox(label="Logistic Regression")
            ml2_out = gr.Textbox(label="Naive Bayes")

        with gr.Column():
            gr.Markdown("### Deep Learning", elem_classes=["section-title"])
            dl1_out = gr.Textbox(label="RNN-LSTM")
            dl2_out = gr.Textbox(label="CNN")

        with gr.Column():
            gr.Markdown("### Transformers", elem_classes=["section-title"])
            trans1_out = gr.Textbox(label="BERTić")
            trans2_out = gr.Textbox(label="CroSloBERTa")

    submit_btn.click(
        gradio_interface,
        inputs=text_input,
        outputs=[ml1_out, ml2_out, dl1_out, dl2_out, trans1_out, trans2_out]
    )

demo.launch()