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| import gradio as gr | |
| import torch | |
| from datasets import load_dataset | |
| dataset = load_dataset("zeroshot/twitter-financial-news-sentiment", ) | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased") | |
| tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") | |
| def sentiment_score(review): | |
| tokens = tokenizer.encode(review, return_tensors='pt') | |
| result = model(tokens) | |
| return int(torch.argmax(result.logits)) | |
| dataset['sentiment'] = dataset['text'].apply(lambda x: sentiment_score(x[:512])) | |
| print(dataset[:10]) | |
| """ | |
| categories = ('Car in good condition','Damaged Car') | |
| def is_car(x) : return x[0].isupper() | |
| def image_classifier(img): | |
| pred,index,probs = learn.predict(img) | |
| return dict(zip(categories, map(float,probs))) | |
| # image = gr.inputs.Image(shape=(192,192)) | |
| image = gr.components.Image(shape=(192,192)) | |
| label = gr.components.Label() | |
| examples = ['./car.jpg','./crash.jpg','./carf.jpg'] | |
| intf = gr.Interface(fn= image_classifier,inputs=image,outputs=label,examples=examples) | |
| intf.launch()""" |