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from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer, AutoConfig
import numpy as np
import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer
# Specifying the model path, which points to the Hugging Face Model Hub
model_path = f'Mbabazi/twitter-roberta-base-sentiment-latest'
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
# Function to predict sentiment of a given tweet
def predict_tweet(tweet):
# Tokenize the input tweet using the specified tokenizer
inputs = tokenizer(tweet, return_tensors="pt", padding=True, truncation=True, max_length=128)
# Passing the tokenized input through the pre-trained sentiment analysis model
outputs = model(**inputs)
# Applying softmax to obtain probabilities for each sentiment class
probs = outputs.logits.softmax(dim=-1)
# Defining sentiment classes
sentiment_classes = ['Negative', 'Neutral', 'Positive']
# Creating a dictionary with sentiment classes as keys and their corresponding probabilities as values
return {sentiment_classes[i]: float(probs.squeeze()[i]) for i in range(len(sentiment_classes))}
# Create a Gradio Interface for the tweet sentiment prediction function
iface = gr.Interface(
fn=predict_tweet, # Set the prediction function
inputs="text", # Specify input type as text
outputs="label", # Specify output type as label
title="Tweet Sentiment Classifier", # Set the title of the interface
description="Enter a tweet to determine if the sentiment is negative, neutral, or positive." # Provide a brief description
)
iface.launch() |