app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load models and tokenizers
sarcasm_tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v3-base")
sarcasm_model = AutoModelForSequenceClassification.from_pretrained("dnzblgn/Sarcasm-Detection-Customer-Reviews")
sentiment_tokenizer = AutoTokenizer.from_pretrained("facebook/roberta-base")
sentiment_model = AutoModelForSequenceClassification.from_pretrained("dnzblgn/Sentiment-Analysis-Customer-Reviews")

def process_text_pipeline(user_input):
    sentences = user_input.split("\n")
    results = []
    for sentence in sentences:
        # Sentiment analysis
        sentiment_inputs = sentiment_tokenizer(sentence, return_tensors="pt", truncation=True, padding=True, max_length=512)
        with torch.no_grad():
            sentiment_outputs = sentiment_model(**sentiment_inputs)
            sentiment_logits = sentiment_outputs.logits
            sentiment_class = torch.argmax(sentiment_logits, dim=-1).item()
            sentiment = "Positive" if sentiment_class == 0 else "Negative"
        
        # Sarcasm detection for positive sentences
        if sentiment == "Positive":
            sarcasm_inputs = sarcasm_tokenizer(sentence, return_tensors="pt", truncation=True, padding=True, max_length=512)
            with torch.no_grad():
                sarcasm_outputs = sarcasm_model(**sarcasm_inputs)
                sarcasm_logits = sarcasm_outputs.logits
                sarcasm_class = torch.argmax(sarcasm_logits, dim=-1).item()
                if sarcasm_class == 1:  # Sarcasm detected
                    sentiment = "Negative (Sarcasm detected)"
        
        results.append(f"{sentence}: {sentiment}")
    return "\n".join(results)

# Gradio UI
interface = gr.Interface(
    fn=process_text_pipeline,
    inputs=gr.Textbox(lines=10, placeholder="Enter one or more sentences, each on a new line."),
    outputs="text",
    title="Sarcasm Detection for Customer Reviews",
    description="This web app analyzes the sentiment of customer reviews and detects sarcasm for positive reviews.",
)

# Run interface
if __name__ == "__main__":
    interface.launch()