Create simplified Gradio app without share=True and complex configuration

Dockerfile

@@ -28,4 +28,4 @@ USER user
 EXPOSE 7860
 
 # Run the application
-CMD ["python", "app_gradio.py"]
+CMD ["python", "app_simple.py"]

app_gradio.py

@@ -220,9 +220,10 @@ if __name__ == '__main__':
         interface.launch(
             server_name=server_name,
             server_port=server_port,
-            share=True,  # This is the key parameter for Hugging Face Spaces!
+            share=False,  # Hugging Face Spaces handles tunneling automatically
             show_error=True,
-            quiet=False
+            quiet=False,
+            inbrowser=False
         )
     except Exception as e:
         logger.error(f"Failed to start application: {e}")
@@ -237,5 +238,6 @@ if __name__ == '__main__':
         fallback.launch(
             server_name=server_name,
             server_port=server_port,
-            share=True
+            share=False,
+            inbrowser=False
         )

app_simple.py

@@ -0,0 +1,126 @@
+#!/usr/bin/env python3
+"""
+Textilindo AI Assistant - Simple Hugging Face Spaces Version
+"""
+
+import gradio as gr
+import os
+import json
+import logging
+
+# Setup logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class TextilindoAI:
+    def __init__(self):
+        self.dataset = []
+        self.load_all_datasets()
+        logger.info(f"Total examples loaded: {len(self.dataset)}")
+
+    def load_all_datasets(self):
+        """Load all JSONL datasets from the data directory"""
+        base_dir = os.path.dirname(__file__)
+        data_dir = os.path.join(base_dir, "data")
+        
+        if not os.path.exists(data_dir):
+            logger.warning(f"Data directory not found: {data_dir}")
+            return
+        
+        logger.info(f"Found data directory: {data_dir}")
+        
+        # Load all JSONL files
+        for filename in os.listdir(data_dir):
+            if filename.endswith('.jsonl'):
+                filepath = os.path.join(data_dir, filename)
+                file_examples = 0
+                try:
+                    with open(filepath, 'r', encoding='utf-8') as f:
+                        for line in f:
+                            line = line.strip()
+                            if line:
+                                try:
+                                    data = json.loads(line)
+                                    data['source'] = filename
+                                    self.dataset.append(data)
+                                    file_examples += 1
+                                except json.JSONDecodeError as e:
+                                    logger.warning(f"Invalid JSON in {filename}: {e}")
+                                    continue
+                    
+                    logger.info(f"Loaded {filename}: {file_examples} examples")
+                except Exception as e:
+                    logger.error(f"Error loading {filename}: {e}")
+
+    def chat(self, message):
+        """Simple chat function"""
+        if not message:
+            return "Please enter a message."
+        
+        # Simple response based on dataset
+        if len(self.dataset) > 0:
+            return f"Hello! I have {len(self.dataset)} examples in my knowledge base. You asked: '{message}'. How can I help you with Textilindo?"
+        else:
+            return "I'm sorry, I don't have access to my knowledge base right now."
+
+# Initialize AI assistant
+ai = TextilindoAI()
+
+# Create simple interface
+def create_interface():
+    with gr.Blocks(title="Textilindo AI Assistant") as interface:
+        gr.Markdown("# 🤖 Textilindo AI Assistant")
+        gr.Markdown("AI-powered customer service for Textilindo")
+        
+        with gr.Row():
+            with gr.Column():
+                message_input = gr.Textbox(
+                    label="Your Message",
+                    placeholder="Ask me anything about Textilindo...",
+                    lines=3
+                )
+                submit_btn = gr.Button("Send Message", variant="primary")
+            
+            with gr.Column():
+                response_output = gr.Textbox(
+                    label="AI Response",
+                    lines=10,
+                    interactive=False
+                )
+        
+        # Event handlers
+        submit_btn.click(
+            fn=ai.chat,
+            inputs=message_input,
+            outputs=response_output
+        )
+        
+        message_input.submit(
+            fn=ai.chat,
+            inputs=message_input,
+            outputs=response_output
+        )
+        
+        # Add examples
+        gr.Examples(
+            examples=[
+                "Dimana lokasi Textilindo?",
+                "Apa saja produk yang dijual di Textilindo?",
+                "Jam berapa Textilindo buka?",
+                "Bagaimana cara menghubungi Textilindo?"
+            ],
+            inputs=message_input
+        )
+        
+        # Add footer with stats
+        gr.Markdown(f"**Dataset loaded:** {len(ai.dataset)} examples")
+    
+    return interface
+
+# Launch the interface
+if __name__ == "__main__":
+    logger.info("Starting Textilindo AI Assistant...")
+    logger.info(f"Dataset loaded: {len(ai.dataset)} examples")
+    
+    interface = create_interface()
+    interface.launch()