import os
import logging
import gradio as gr
from huggingface_hub import InferenceClient

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s | %(levelname)s | %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
)
logger = logging.getLogger(__name__)

# Environment variables for configuration
HF_TOKEN = os.environ.get("HF_TOKEN", "")
MODEL_ID = os.environ.get("MODEL_ID", "distilbert/distilbert-base-uncased-finetuned-sst-2-english")

logger.info(f"HF_TOKEN configured: {bool(HF_TOKEN)}")
logger.info(f"MODEL_ID: {MODEL_ID}")

client = InferenceClient(token=HF_TOKEN) if HF_TOKEN else InferenceClient()
logger.info("InferenceClient initialized")


def analyze(text: str) -> tuple[str, dict]:
    """Return emoji + label and confidence scores."""
    logger.info(f"analyze() called | text_len={len(text)}")
    
    if not text.strip():
        logger.warning("Empty text received")
        return "🤔 Enter some text!", {}
    
    try:
        logger.info(f"Calling text_classification | model={MODEL_ID}")
        result = client.text_classification(text, model=MODEL_ID)[0]
        
        label = result.label
        score = result.score
        logger.info(f"Result: {label} ({score:.1%})")
        
        emoji = "😊" if label == "POSITIVE" else "😞"
        return f"{emoji} {label} ({score:.1%})", {label: score, "OTHER": 1 - score}
    except Exception as e:
        logger.error(f"API error: {e}")
        return f"❌ Error: {e}", {}


logger.info("Building Gradio interface...")

with gr.Blocks(title="Sentiment Explorer") as demo:
    gr.Markdown("# 🎭 Sentiment Explorer\nType anything and see if it's positive or negative!")

    inp = gr.Textbox(
        label="Your text",
        placeholder="I absolutely love learning about AI!",
        lines=3,
        autofocus=True,
    )

    with gr.Row(equal_height=True):
        result_label = gr.Textbox(label="Verdict", interactive=False)
        confidence = gr.Label(label="Confidence")

    btn = gr.Button("Analyze", variant="primary")
    btn.click(analyze, inputs=inp, outputs=[result_label, confidence])
    inp.submit(analyze, inputs=inp, outputs=[result_label, confidence])

    gr.Examples(
        examples=[
            ["This tutorial is amazing and super helpful!"],
            ["I'm frustrated, nothing works today."],
            ["The weather is okay, I guess."],
        ],
        inputs=inp,
    )

demo.queue()
logger.info("Starting Gradio server...")
demo.launch()