Spaces:
Runtime error
Runtime error
| import gradio as gr | |
| from transformers import AutoModelForSequenceClassification | |
| from transformers import TFAutoModelForSequenceClassification | |
| from transformers import AutoTokenizer, AutoConfig | |
| import numpy as np | |
| from scipy.special import softmax | |
| # setting up the requiremnts | |
| model_path = f"mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis" | |
| tokenizer = AutoTokenizer.from_pretrained('mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis') | |
| config = AutoConfig.from_pretrained(model_path) | |
| model = AutoModelForSequenceClassification.from_pretrained(model_path) | |
| # Preprocess text (username and link placeholders) | |
| def preprocess(text): | |
| new_text = [] | |
| for t in text.split(" "): | |
| t = '@user' if t.startswith('@') and len(t) > 1 else t | |
| t = 'http' if t.startswith('http') else t | |
| new_text.append(t) | |
| return " ".join(new_text) | |
| # Defining the main function | |
| def sentiment_analysis(text): | |
| text = preprocess(text) | |
| # PyTorch-based models | |
| encoded_input = tokenizer(text, return_tensors='pt') | |
| output = model(**encoded_input) | |
| scores_ = output[0][0].detach().numpy() | |
| scores_ = softmax(scores_) | |
| # Format output dict of scores | |
| labels = ['Negative😢😢', 'Neutral', 'Positive😃😃'] | |
| scores = {l:float(s) for (l,s) in zip(labels, scores_) } | |
| return scores | |
| welcome_message = "Welcome to Team Paris tweets first shot Sentimental Analysis App 😃 😃 😃 😃 " | |
| demo = gr.Interface( | |
| fn=sentiment_analysis, | |
| inputs=gr.Textbox(placeholder="Write your tweet here..."), | |
| outputs="label", | |
| interpretation="default", | |
| examples=[["This is wonderful!"]], | |
| title=welcome_message, | |
| description=("This is a sentimental analysis app built by fine tuning a model trained on financial news sentiment, we leverage what the model has learnt, /n, and fine tune it on twitter comments . The eval_loss of our model is 0.785") | |
| ) | |
| demo.launch() | |
| # def greet(name): | |
| # return "Hello " + name + "!!" | |
| # iface = gr.Interface(fn=greet, inputs="text", outputs="text") | |
| # iface.launch(inline = False) |