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app.py

@@ -1,18 +1,34 @@
 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__)
+
 # Get token from environment (set in HF Space secrets)
 HF_TOKEN = os.environ.get("HF_TOKEN", "")
+logger.info(f"HF_TOKEN configured: {bool(HF_TOKEN)}")
+
 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 with text length: {len(text)}")
+    
     if not text.strip():
+        logger.warning("Empty text received")
         return "🤔 Enter some text!", {}
     
     try:
+        logger.info("Calling text_classification API...")
         result = client.text_classification(
             text,
             model="distilbert-base-uncased-finetuned-sst-2-english",
@@ -20,12 +36,17 @@ def analyze(text: str) -> tuple[str, dict]:
         
         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!")
 
@@ -54,4 +75,5 @@ with gr.Blocks(title="Sentiment Explorer") as demo:
     )
 
 demo.queue()
+logger.info("Starting Gradio server...")
 demo.launch()