app.py

import gradio as gr
from transformers import pipeline

# Load domain-specific sentiment pipelines
models={
    "General":pipeline("sentiment-analysis",model="distilbert-base-uncased-finetuned-sst-2-english"),
    "Product Reviews":pipeline("sentiment-analysis",model="nlptown/bert-base-multilingual-uncased-sentiment"),
    "Movie Reviews":pipeline("sentiment-analysis",model="textattack/distilbert-base-uncased-imdb"),
    "Food/Restaurent":pipeline("sentiment-analysis",model="siebert/sentiment-roberta-large-english"),  
}

# Add label mapping  for IMDB model (Movie Reviews)
imdb_label_map={
    "LABEL_0":"Negative",
    "LABEL_1":"Positive"
}

# Define sentiment analysis function
def analyze_sentiment(domain,review):
    if not review.strip():
        return "Please enter a review."
    clf=models[domain]
    result=clf(review)[0]
    
    # Apply mapping only for Movie Reviews domain as it labesl as LABEL_0 and LABEL_1
    if domain=="Movie Reviews":
        result['label']=imdb_label_map[result['label']]
    
    return f"Model: {domain}\n\nLabel: {result['label']}\n\nConfidence: {result['score']:.2f}"

# Create Gradio interface
iface=gr.Interface(
    fn=analyze_sentiment,
    inputs=[
        gr.Dropdown(list(models.keys()),label="Choose Domain",value="General"),
        gr.Textbox(lines=4,placeholder="Type a review....",label="Review"),  
    ],
    outputs=gr.Textbox(label="Prediction"),
    title="Multi-Domain Sentiment Analysis",
    description="Pic a domian and analyze sentiment.",
    allow_flagging="never",
    
)

# Launch the interface
iface.launch()