Update app.py

app.py

@@ -1,41 +1,93 @@
-
-
 import gradio as gr
 import os
+import logging
+
+# Enable logging
+logging.basicConfig(level=logging.INFO)
 
-# Get token from environment variable
+# Get the token
 hf_token = os.getenv("HF_TOKEN")
+if not hf_token:
+    raise ValueError("❌ HF_TOKEN environment variable is not set")
 
-# Replace with your private Space name
-PRIVATE_SPACE = "entropy25/sentiment-analyzer-docker2"
+# Define the private Space path
+PRIVATE_SPACE = "entropy25/private"
 
-# Load the private Space as a function
-try:
-    private_fn = gr.load(f"spaces/{PRIVATE_SPACE}", hf_token=hf_token)
-    print("Successfully connected to private Space")
-except Exception as e:
-    print(f"Connection failed: {e}")
-    private_fn = None
+def load_private_space():
+    """Load private space via token"""
+    try:
+        private_fn = gr.load(f"spaces/{PRIVATE_SPACE}", hf_token=hf_token)
+        logging.info("✅ Successfully connected to private space")
+        return private_fn
+    except Exception as e:
+        logging.error(f"❌ Connection failed: {e}")
+        return None
 
-def process_input(user_input):
-    if not private_fn:
-        return "Error: Unable to connect to private Space. Please check the token and space name."
+# Load once during startup
+private_analyzer = load_private_space()
+
+def analyze_sentiment(text):
+    """Call the private space for sentiment analysis"""
+    if not private_analyzer:
+        return "❌ Error: Failed to connect to private space"
+    
+    if not text.strip():
+        return "⚠️ Please enter text to analyze"
     
     try:
-        result = private_fn(user_input)
+        result = private_analyzer(text)
+        logging.info(f"✅ Analysis result: {result}")
         return result
     except Exception as e:
-        return f"Processing error: {str(e)}"
+        error_msg = f"❌ Analysis failed: {str(e)}"
+        logging.error(error_msg)
+        return error_msg
 
-# Create Gradio interface
-demo = gr.Interface(
-    fn=process_input,
-    inputs=gr.Textbox(label="Input", placeholder="Enter your content..."),
-    outputs=gr.Textbox(label="Output"),
-    title="Call Private Space",
-    description="Call functions from a private Hugging Face Space via this public interface"
-)
+def create_interface():
+    """Build the public-facing UI"""
+    with gr.Blocks(title="Sentiment Analyzer - Public", theme=gr.themes.Soft()) as demo:
+        gr.Markdown("# 🎭 Sentiment Analysis Tool")
+        gr.Markdown("Call a private Hugging Face space for sentiment analysis")
+        
+        with gr.Row():
+            with gr.Column(scale=2):
+                text_input = gr.Textbox(
+                    label="📝 Input Text",
+                    placeholder="Type a sentence to analyze...",
+                    lines=3,
+                    max_lines=10
+                )
+                analyze_btn = gr.Button("🔍 Analyze Sentiment", variant="primary")
+            
+            with gr.Column(scale=2):
+                result_output = gr.Textbox(
+                    label="📊 Analysis Result",
+                    lines=5,
+                    max_lines=10
+                )
+        
+        analyze_btn.click(
+            fn=analyze_sentiment,
+            inputs=text_input,
+            outputs=result_output
+        )
+        
+        gr.Examples(
+            examples=[
+                ["I'm feeling great today!"],
+                ["That movie was a waste of time."],
+                ["The weather is nice, perfect for a walk."],
+                ["Service was okay, but the quality was mediocre."]
+            ],
+            inputs=text_input
+        )
+    
+    return demo
 
 if __name__ == "__main__":
-    demo.launch()
-
+    demo = create_interface()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False
+    )