app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
from scipy.special import softmax

model_name = "SoloAlphus/ConSenBert-V1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

def analyze_sentiment(text, entity):
    input_text = text + "[SEP]" + entity
    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=512, padding=True)
    
    with torch.no_grad():
        outputs = model(**inputs)
    
    scores = outputs.logits.squeeze().numpy()
    scores = softmax(scores)
    
    labels = ['Negative', 'Neutral', 'Positive']
    result = {label: float(score) for label, score in zip(labels, scores)}
    
    predicted_sentiment = max(result, key=result.get)
    
    return result, predicted_sentiment

def gradio_interface(comment, entity):
    sentiment_scores, predicted_sentiment = analyze_sentiment(comment, entity)
    return sentiment_scores, predicted_sentiment

examples = [
    ["abc product looks much better compared to xyz product!", "xyz"],
    ["I love Apple for their out standing products in the market!", "Samsung"],
    ["Rekha is a good dancer, but Sheela is extraordinary when it comes to dancing!", "Sheela"]
]

interface = gr.Interface(fn=gradio_interface, 
                         inputs=["text", "text"], 
                         outputs=["json", "text"],
                         title="ConSenBERT - Context based Sentiment Analysis using RoBERTa",
                         description="Enter a comment and an entity to analyze the sentiment. \n \n Do like my model and space if you like my work! 🤗",
                         examples=examples)

interface.launch()