import gradio as gr
from transformers import pipeline

print("🚀 Loading models...")

# Load models with automatic device selection
sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
translator = pipeline("translation_en_to_es", model="Helsinki-NLP/opus-mt-en-es")

print("✅ Models loaded successfully!")

def analyze_sentiment(text):
    if not text.strip():
        return "⚠️ Please enter some text!"
    try:
        result = sentiment_analyzer(text[:500])[0]
        emoji = "😊" if result['label'] == "POSITIVE" else "😞"
        return f"{emoji} **{result['label']}**\nConfidence: {result['score']:.1%}"
    except Exception as e:
        return f"❌ Error: {str(e)}"

def translate_text(text):
    if not text.strip():
        return "⚠️ Please enter some text!"
    try:
        result = translator(text[:400])[0]
        return result['translation_text']
    except Exception as e:
        return f"❌ Error: {str(e)}"

# Simple UI
with gr.Blocks() as demo:
    gr.Markdown("# 🤖 AI Text Tools")
    
    with gr.Tab("😊 Sentiment"):
        sent_in = gr.Textbox(label="Text", lines=3)
        sent_btn = gr.Button("Analyze")
        sent_out = gr.Textbox(label="Result")
        sent_btn.click(analyze_sentiment, sent_in, sent_out)
    
    with gr.Tab("🌎 Translate"):
        trans_in = gr.Textbox(label="English", lines=3)
        trans_btn = gr.Button("Translate")
        trans_out = gr.Textbox(label="Spanish")
        trans_btn.click(translate_text, trans_in, trans_out)

demo.launch()