app.py

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

# Load sentiment model
def load_model():
    model_name = "cardiffnlp/twitter-roberta-base-sentiment"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSequenceClassification.from_pretrained(model_name)
    sentiment_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
    return sentiment_pipeline

sentiment_pipeline = load_model()
label_map = {"LABEL_0": "negative", "LABEL_1": "neutral", "LABEL_2": "positive"}

# Simple explanation generator
def explain_sentiment(text, sentiment):
    text_lower = text.lower()
    if sentiment == "positive":
        if any(word in text_lower for word in ["love", "great", "happy", "excellent", "good", "amazing"]):
            return "Detected positive language such as 'great', 'happy', or 'love'."
        else:
            return "Overall positive tone based on context."
    elif sentiment == "negative":
        if any(word in text_lower for word in ["hate", "bad", "terrible", "awful", "worst", "sad"]):
            return "Detected negative words like 'hate', 'bad', or 'worst'."
        else:
            return "Overall negative tone based on context."
    else:
        return "Text appears to be neutral with no strong emotional cues."

# Gradio function
def analyze_sentiment(text):
    if not text.strip():
        return "Please enter some text.", ""
    
    result = sentiment_pipeline(text)[0]
    sentiment = label_map[result["label"]]
    confidence = result["score"]
    explanation = explain_sentiment(text, sentiment)
    
    sentiment_result = f"**Sentiment**: {sentiment.capitalize()}  \n**Confidence**: {confidence:.2f}"
    return sentiment_result, explanation

# Gradio interface
iface = gr.Interface(
    fn=analyze_sentiment,
    inputs=gr.Textbox(lines=4, placeholder="Type something here..."),
    outputs=[
        gr.Markdown(label="Sentiment Result"),
        gr.Textbox(label="Explanation")
    ],
    title="Fast Sentiment Analyzer",
    description="Enter some text. This app will classify the sentiment (Positive, Negative, or Neutral) and offer a basic explanation.",
)

if __name__ == "__main__":
    iface.launch()