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| # Import the required Libraries | |
| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| import pickle | |
| import transformers | |
| from transformers import AutoTokenizer, AutoConfig,AutoModelForSequenceClassification,TFAutoModelForSequenceClassification | |
| from scipy.special import softmax | |
| # Requirements | |
| model_path = "Kaludi/Reviews-Sentiment-Analysis" | |
| tokenizer = AutoTokenizer.from_pretrained(model_path) | |
| 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) | |
| # ---- Function to process the input and return prediction | |
| def sentiment_analysis(text): | |
| text = preprocess(text) | |
| encoded_input = tokenizer(text, return_tensors = "pt") # for PyTorch-based models | |
| output = model(**encoded_input) | |
| scores_ = output[0][0].detach().numpy() | |
| scores_ = softmax(scores_) | |
| # Format output dict of scores | |
| labels = ["Negative", "Positive"] | |
| scores = {l:float(s) for (l,s) in zip(labels, scores_) } | |
| return scores | |
| # ---- Gradio app interface | |
| app = gr.Interface(fn = sentiment_analysis, | |
| inputs = gr.Textbox("Write your text or review here..."), | |
| outputs = "label", | |
| title = "Sentiment Analysis of Customer Reviews", | |
| description = "A tool that analyzes the overall sentiment of customer reviews for a specific product or service, whether it's positive or negative. This analysis is performed by using natural language processing algorithms and machine learning from the model 'Reviews-Sentiment-Analysis' trained by Kaludi, allowing businesses to gain valuable insights into customer satisfaction and improve their products and services accordingly.", | |
| interpretation = "default", | |
| examples = [["I was extremely disappointed with this product. The quality was terrible and it broke after only a few days of use. Customer service was unhelpful and unresponsive. I would not recommend this product to anyone."],[ "I am so impressed with this product! The quality is outstanding and it has exceeded all of my expectations. The customer service team was also incredibly helpful and responsive to any questions I had. I highly recommend this product to anyone in need of a top-notch, reliable solution."],["I don't feel like you trust me to do my job."],["This service was honestly one of the best I've experienced, I'll definitely come back!"]] | |
| ) | |
| app.launch() | |