import gradio as gr
from transformers import BertModel, BertConfig
import torch.nn as nn
import torch.nn.functional as F
import huggingface_hub
from huggingface_hub import hf_hub_download

huggingface_hub.Repository = 'zArabi/Persian-Sentiment-Analysis'

class SentimentModel(nn.Module):
    def __init__(self, config):
        super(SentimentModel, self).__init__()
        self.bert = BertModel.from_pretrained(modelName, return_dict=False)
        self.dropout = nn.Dropout(0.3)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
    
    def forward(self, input_ids, attention_mask):
        _, pooled_output = self.bert(
            input_ids=input_ids, 
            attention_mask=attention_mask)
        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)
        return logits

modelName = 'HooshvareLab/bert-fa-base-uncased'
class_names = ['negative', 'neutral', 'positive']
label2id = {label: i for i, label in enumerate(class_names)}
id2label = {v: k for k, v in label2id.items()}

config = BertConfig.from_pretrained(
    modelName,
    num_labels=len(class_names),
    id2label=id2label,
    label2id=label2id)

downloadedModelFile = hf_hub_download(repo_id="zArabi/Persian-Sentiment-Analysis", filename="persianModel")
loaded_model = torch.load(downloadedModelFile)

max_len=512

pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")

def predict(text):
  text = cleaning(text)
  encoding = tokenizer.encode_plus(
    sample_text,
    max_length=max_len,
    truncation=True,
    padding="max_length",
    add_special_tokens=True, # Add '[CLS]' and '[SEP]'
    return_token_type_ids=True,
    return_attention_mask=True,
    return_tensors='pt',  # Return PyTorch tensors
  )
  input_ids = encoding["input_ids"].to(device)
  attention_mask = encoding["attention_mask"].to(device)
  outputs = loaded_model (input_ids, attention_mask)
  probs = F.softmax(outputs,dim=1)
  values, indices = torch.max(probs, dim=1)
  data = {
    'comments': sample_text,
    'preds': indices.cpu().numpy()[0],
    'label': class_names[indices.cpu().numpy()[0]],
    'probablities': {class_names[i] : round(probs[0][i].item(),3) for i in range(len(probs[0]))}
  }
  return data

gr.Interface(
    predict,
    inputs=gr.Textbox(label="Explore your sentence!",lines=2, placeholder="Type Here..."),
    outputs=gr.outputs.Label(num_top_classes=3),
    title="What are feeling?!",
).launch()