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import gradio as gr |
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import pandas as pd |
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import numpy as np |
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import pickle |
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from scipy.special import softmax |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig |
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model_path = "QuophyDzifa/Sentiment-Analysis-Model" |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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config = AutoConfig.from_pretrained(model_path) |
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model = AutoModelForSequenceClassification.from_pretrained(model_path) |
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def preprocess(text): |
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new_text = [] |
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for t in text.split(" "): |
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t = '@user' if t.startswith('@') and len(t) > 1 else t |
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t = 'http' if t.startswith('http') else t |
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new_text.append(t) |
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return " ".join(new_text) |
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def sent_analysis(text): |
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text = preprocess(text) |
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encoded_input = tokenizer(text, return_tensors='pt') |
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output = model(**encoded_input) |
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scores_ = output[0][0].detach().numpy() |
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scores_ = softmax(scores_) |
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labels = {0: 'NEGATIVE', 1: 'NEUTRAL', 2: 'POSITIVE'} |
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scores = {labels[i]: float(s) for i, s in enumerate(scores_)} |
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return scores |
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demo = gr.Interface( |
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fn=sent_analysis, |
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inputs=gr.Textbox(placeholder="Share your thoughts on COVID vaccines..."), |
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outputs="label", |
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interpretation="default", |
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examples=[ |
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["I feel confident about covid vaccines"], |
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["I do not like the covid vaccine"], |
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["I like the covid vaccines"], |
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["The covid vaccines are effective"] |
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], |
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title="COVID Vaccine Sentiment Analysis", |
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description="An AI model that predicts sentiment about COVID vaccines, providing labels and probabilities for 'NEGATIVE', 'NEUTRAL', and 'POSITIVE' sentiments.", |
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theme="default", |
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live=True |
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) |
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if __name__ == "__main__": |
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demo.launch("0.0.0.0:7860") |
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