v3

app.py

@@ -3,21 +3,17 @@ import gradio as gr
 import spaces
 import json
 from modules.pmbl import PMBL
-from huggingface_hub import HfApi
 
 # Initialize the PMBL instance
 pmbl = PMBL("Qwen/QwQ-32B-GGUF")
 
-# Check if we're running on Hugging Face Spaces
-IS_HF_SPACE = os.environ.get("SPACE_ID") is not None
-DATASET_REPO = "Sergidev/PMBMemory"
-api = HfApi()
-
-# Define theme
+# Define theme with compatible settings
 theme = gr.themes.Soft(
     primary_hue="red",
     secondary_hue="purple",
-).set(
+)
+# Use update instead of set for theme configuration
+theme = theme.update(
     body_background_fill="linear-gradient(to bottom right, #222222, #333333)",
     block_background_fill="#1e1e1e",
     button_primary_background_fill="#59788E",
@@ -27,7 +23,6 @@ theme = gr.themes.Soft(
     block_border_width="1px",
     block_border_color="#ccc",
     block_radius="5px",
-    text_color="#f0f8ff",
 )
 
 # CSS for customizing the chat interface
@@ -81,7 +76,7 @@ def generate_response(message, history, memory_mode):
         response += chunk
         yield response
 
-    # Save the conversation to history
+    # Save the conversation to local history only
     pmbl.save_chat(message, response)
 
     # Process and organize chat history

modules/pmbl.py

@@ -1,73 +1,38 @@
 import json
-import asyncio
-from datetime import datetime
-from huggingface_hub import HfApi
 import os
+from datetime import datetime
 from ctransformers import AutoModelForCausalLM
 
-# Check if we're running on Hugging Face Spaces
-IS_HF_SPACE = os.environ.get("SPACE_ID") is not None
-DATASET_REPO = "Sergidev/PMBMemory"
-api = HfApi()
-
 class PMBL:
     def __init__(self, model_path):
         self.model_path = model_path
-        self.init_lock = asyncio.Lock()
         self.chat_history = []
-        # Initialize history immediately without asyncio task
+        # Load local history only
         self._load_history_sync()
 
     def _load_history_sync(self):
-        """Load chat history synchronously from Hugging Face dataset"""
-        if IS_HF_SPACE:
-            try:
-                # Check if the chat history file exists in the dataset
-                files = api.list_repo_files(DATASET_REPO)
-                if "chat_history.json" in files:
-                    # Download the file
-                    content = api.hf_hub_download(DATASET_REPO, "chat_history.json")
-                    with open(content, 'r') as f:
-                        self.chat_history = json.load(f)
-                    print(f"Loaded {len(self.chat_history)} chat records")
-                else:
-                    print("No chat history found, starting with empty history")
-                    self.chat_history = []
-            except Exception as e:
-                print(f"Error loading chat history: {e}")
-                self.chat_history = []
-        else:
-            # Local development mode
+        """Load chat history from local file"""
+        try:
+            # Local storage only
             if os.path.exists("chat_history.json"):
                 with open("chat_history.json", 'r') as f:
                     self.chat_history = json.load(f)
+                print(f"Loaded {len(self.chat_history)} chat records")
             else:
+                print("No chat history found, starting with empty history")
                 self.chat_history = []
+        except Exception as e:
+            print(f"Error loading chat history: {e}")
+            self.chat_history = []
 
     def save_history_sync(self):
-        """Save chat history synchronously to Hugging Face dataset"""
-        if IS_HF_SPACE:
-            try:
-                # Create a temporary file
-                with open("temp_history.json", 'w') as f:
-                    json.dump(self.chat_history, f)
-
-                # Upload the file to the dataset
-                api.upload_file(
-                    path_or_fileobj="temp_history.json",
-                    path_in_repo="chat_history.json",
-                    repo_id=DATASET_REPO,
-                    repo_type="dataset"
-                )
-
-                # Clean up
-                os.remove("temp_history.json")
-            except Exception as e:
-                print(f"Error saving chat history: {e}")
-        else:
-            # Local development mode
+        """Save chat history to local file"""
+        try:
+            # Local storage only
             with open("chat_history.json", 'w') as f:
                 json.dump(self.chat_history, f)
+        except Exception as e:
+            print(f"Error saving chat history: {e}")
 
     def process_history(self, history, mode, user_message):
         """Process the chat history based on the mode"""
@@ -171,7 +136,7 @@ class PMBL:
             yield chunk
 
     def save_chat(self, prompt, response):
-        """Save chat to history synchronously"""
+        """Save chat to history"""
         # Add the new chat to history
         chat_entry = {
             "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
@@ -182,11 +147,11 @@ class PMBL:
 
         self.chat_history.append(chat_entry)
 
-        # Save the updated history
+        # Save the updated history locally only
         self.save_history_sync()
 
     def sleep_mode(self):
-        """Process and organize chat history synchronously"""
+        """Process and organize chat history"""
         # Find chats without specific topics
         untitled_chats = [
             (i, chat) for i, chat in enumerate(self.chat_history)
@@ -206,7 +171,7 @@ class PMBL:
             self.save_history_sync()
 
     def _generate_topic_sync(self, prompt, response):
-        """Generate a topic for a chat using the model synchronously"""
+        """Generate a topic for a chat using the model"""
         try:
             # Use a very small context for topic generation to save resources
             model = AutoModelForCausalLM.from_pretrained(