fix

main.py

@@ -13,6 +13,7 @@ from datetime import datetime
 import time
 import random
 from collections import defaultdict, deque
+import threading
 
 from content_analyzer.document_parser import DocumentProcessor
 from search_engine.indexer import RetrieverBuilder
@@ -20,20 +21,28 @@ from intelligence.orchestrator import AgentWorkflow
 from configuration import definitions, parameters
                                  
 
-# Rate limiting configuration - 3 requests per 60 seconds per IP
+# Rate limiting configuration - 3 requests per hour per IP
 WINDOW_S = 3600
 MAX_CALLS = 3
 _calls = defaultdict(deque)  # ip -> timestamps
+_calls_lock = threading.Lock()  # Thread-safe access to rate limit state
 
 def rate_limit(request):
+    """Thread-safe rate limiting per IP address."""
     ip = getattr(request.client, "host", "unknown")
     now = time.time()
-    q = _calls[ip]
-    while q and (now - q[0]) > WINDOW_S:
-        q.popleft()
-    if len(q) >= MAX_CALLS:
-        raise gr.Error(f"Rate limit: {MAX_CALLS} requests per {WINDOW_S}s. Please wait.")
-    q.append(now)
+    
+    with _calls_lock:
+        q = _calls[ip]
+        # Remove expired entries
+        while q and (now - q[0]) > WINDOW_S:
+            q.popleft()
+        
+        if len(q) >= MAX_CALLS:
+            import gradio as gr
+            raise gr.Error(f"Rate limit: {MAX_CALLS} requests per {WINDOW_S//60} minutes. Please wait.")
+        
+        q.append(now)
 
 
 # Example data for demo
@@ -895,13 +904,18 @@ setInterval(tick, 500);
                     from huggingface_hub import hf_hub_download
                     copied_files = []
                     file_info_text = f"✅ Loaded: {example_key}\n\n"
+                    
+                    # Get HF token for private dataset access (optional)
+                    hf_token = os.environ.get("HF_TOKEN", None)
+                    
                     for file_path in file_names:
                         filename = os.path.basename(file_path)
                         try:
                             local_path = hf_hub_download(
-                                repo_id="TilanTAB/smartdoc-samples",
+                                repo_id="TilanB/smartdoc-samples",  # Correct dataset repo
                                 repo_type="dataset",
                                 filename=filename,
+                                token=hf_token,  # Pass token for private repos
                             )
                             copied_files.append(local_path)
                             file_size_mb = os.path.getsize(local_path) / (1024 * 1024)
@@ -910,7 +924,7 @@ setInterval(tick, 500);
                             logger.error(f"Failed to download {filename}: {e}")
                             file_info_text += f"❌ {filename} - Download failed\n"
                     if not copied_files:
-                        return [], "", "❌ Could not load example files from dataset"
+                        return [], "", "❌ Could not load example files from dataset. Make sure the dataset is public or HF_TOKEN is set."
                     return copied_files, question_text, file_info_text
                 except ImportError:
                     return [], "", "❌ huggingface_hub not installed"
@@ -940,6 +954,16 @@ setInterval(tick, 500);
             inputs=[example_dropdown],
             outputs=[files, question, loaded_file_info]
         )
+        
+        # Show loaded_file_info when example is selected
+        def show_info(example_key):
+            return gr.update(visible=bool(example_key))
+        
+        example_dropdown.change(
+            fn=show_info,
+            inputs=[example_dropdown],
+            outputs=[loaded_file_info]
+        )
     # Launch server - Compatible with both local and Hugging Face Spaces
     # HF Spaces sets SPACE_ID environment variable
     is_hf_space = os.environ.get("SPACE_ID") is not None