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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) |