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| import gradio as gr | |
| import numpy as np | |
| from transformers import pipeline | |
| from model import DepressionClassifier | |
| import hopsworks | |
| import joblib | |
| import torch | |
| from huggingface_hub import hf_hub_download | |
| import transformers | |
| from transformers import BertModel, BertTokenizer | |
| from PIL import Image | |
| import requests | |
| import io | |
| class_names = ['Not Depressed', 'Depressed'] | |
| pt_file = hf_hub_download(repo_id="liangc40/sentimental_analysis", filename="model.pt") | |
| model = DepressionClassifier(len(class_names), 'bert-base-cased') | |
| model.load_state_dict(torch.load(pt_file, map_location=torch.device('cpu'))) | |
| model.eval() | |
| #pipe = pipeline(model="liangc40/sentimental_analysis") | |
| #project = hopsworks.login(project='liangc40') | |
| #fs = project.get_feature_store() | |
| #mr = project.get_model_registry() | |
| #model = mr.get_model("sentimental_analysis_model", version=1) | |
| #model_dir = model.download() | |
| #model = joblib.load(model_dir + "/sentimental_analysis_model.pkl") | |
| def analyse(text): | |
| #text = "I'm depressed" | |
| #model = model.to('cpu') | |
| tokenizer = BertTokenizer.from_pretrained('bert-base-cased') | |
| encoding = tokenizer.encode_plus(text, max_length=32, add_special_tokens=True, # Add '[CLS]' and '[SEP]' | |
| return_token_type_ids=False, | |
| pad_to_max_length=True, | |
| return_attention_mask=True, | |
| return_tensors='pt') | |
| outputs = model(input_ids = encoding['input_ids'], attention_mask = encoding['attention_mask']) | |
| _, preds = torch.max(outputs, dim=1) | |
| face_url = "https://raw.githubusercontent.com/liangc40/ID2223_Sentimental_Analysis_Project/main/Image/"+ str(preds) + ".png" | |
| r = requests.get(face_url, stream=True) | |
| img = Image.open(io.BytesIO(r.content)) | |
| #img = Image.open(requests.get(face_url, stream=True).raw) | |
| #print(preds) | |
| return img | |
| with gr.Blocks() as demo: | |
| gr.Markdown("<h1><center>Sentiment Analysis with Fine-tuned BERT Model") | |
| inputs_text=gr.Textbox(placeholder='Type your text for which you want know the sentiment', label='Text') | |
| text_button = gr.Button('Analyse Sentiment') | |
| output_text_sentiment = gr.Textbox(placeholder='Sentiment of the text.', label='Sentiment') | |
| text_button.click(analyse, inputs = inputs_text, outputs = output_text_sentiment) | |
| if __name__ == "__main__": | |
| demo.launch() |