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 = ['Negative๐Ÿ˜ข๐Ÿ˜ข', 'Neutral', 'Positive๐Ÿ˜ƒ๐Ÿ˜ƒ'] 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 ๐Ÿ˜ƒ ๐Ÿ˜ƒ ๐Ÿ˜ƒ ๐Ÿ˜ƒ " 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)