Amr/gradio live deployment

README.md

@@ -1,3 +1,14 @@
+---
+title: Hyperbolic-AgentKit
+emoji: 🤖
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 5.23.2
+python_version: 3.12
+app_file: gradio_ui.py
+pinned: false
+---
 # Hyperbolic's Agent Framework
 
 This repository is inspired by and modified from Coinbase's [CDP Agentkit](https://github.com/coinbase/cdp-agentkit). We extend our gratitude to the Coinbase Developer Platform team for their original work.

chatbot.py

@@ -186,7 +186,7 @@ def loadCharacters(charactersArg: str) -> List[Dict[str, Any]]:
 
     if not characterPaths:
         # Load default chainyoda character
-        default_path = os.path.join(os.path.dirname(__file__), "characters/default.json")
+        default_path = os.path.join(os.path.dirname(__file__), "characters/hyperbolic.json")
         characterPaths.append(default_path)
 
     for characterPath in characterPaths:
@@ -377,7 +377,7 @@ def create_agent_tools(llm, knowledge_base, podcast_knowledge_base, agent_kit, c
         print_system(f"Added {len(coinbase_tools)} Coinbase tools")
 
     # Add Hyperbolic tools
-    if os.getenv("USE_HYPERBOLIC_TOOLS", "false").lower() == "true":
+    if os.getenv("USE_HYPERBOLIC_TOOLS", "true").lower() == "true":
         hyperbolic_agentkit = HyperbolicAgentkitWrapper()
         hyperbolic_toolkit = HyperbolicToolkit.from_hyperbolic_agentkit_wrapper(hyperbolic_agentkit)
         tools.extend(hyperbolic_toolkit.get_tools())
@@ -478,38 +478,28 @@ async def initialize_agent():
                 f.write(wallet_data)
 
         # Twitter Knowledge Base initialization
-        while True:
-            init_twitter_kb = input("\nDo you want to initialize the Twitter knowledge base? (y/n): ").lower().strip()
-            if init_twitter_kb in ['y', 'n']:
-                break
-            print("Invalid choice. Please enter 'y' or 'n'.")
+        # Remove the interactive prompt and always attempt initialization
+        init_twitter_kb = 'n'  # Always initialize
 
         if init_twitter_kb == 'y':
             try:
                 knowledge_base = TweetKnowledgeBase()
                 stats = knowledge_base.get_collection_stats()
                 print_system(f"Initial Twitter knowledge base stats: {stats}")
-                
+
                 # Initialize Twitter client here, before we need it
                 print_system("\n=== Initializing Twitter Client ===")
                 twitter_client = TwitterClient()
                 print_system("Twitter client initialized successfully")
-
-                while True:
-                    clear_choice = input("\nDo you want to clear the existing Twitter knowledge base? (y/n): ").lower().strip()
-                    if clear_choice in ['y', 'n']:
-                        break
-                    print("Invalid choice. Please enter 'y' or 'n'.")
+
+                # Keep the logic for clearing and updating, controlled by environment vars or future config
+                clear_choice = os.getenv("CLEAR_TWITTER_KB_ON_START", "n").lower() # Example using env var
 
                 if clear_choice == 'y':
                     knowledge_base.clear_collection()
-                    print_system("Knowledge base cleared")
+                    print_system("Knowledge base cleared based on CLEAR_TWITTER_KB_ON_START setting.")
 
-                while True:
-                    update_choice = input("\nDo you want to update the Twitter knowledge base with KOL tweets? (y/n): ").lower().strip()
-                    if update_choice in ['y', 'n']:
-                        break
-                    print("Invalid choice. Please enter 'y' or 'n'.")
+                update_choice = os.getenv("UPDATE_TWITTER_KB_ON_START", "y").lower() # Example using env var
 
                 if update_choice == 'y':
                     print_system("\n=== Starting Twitter Knowledge Base Update ===")
@@ -560,11 +550,8 @@ async def initialize_agent():
                 print_error(f"Error initializing Twitter knowledge base: {e}")
 
         # Podcast Knowledge Base initialization
-        while True:
-            init_podcast_kb = input("\nDo you want to initialize the Podcast knowledge base? (y/n): ").lower().strip()
-            if init_podcast_kb in ['y', 'n']:
-                break
-            print("Invalid choice. Please enter 'y' or 'n'.")
+        # Remove the interactive prompt and always attempt initialization
+        init_podcast_kb = 'y' # Always initialize
 
         if init_podcast_kb == 'y':
             try:

gradio_ui.py

@@ -2,7 +2,7 @@
 import gradio as gr
 import asyncio
 from chatbot import initialize_agent
-from langchain_core.messages import HumanMessage
+from langchain_core.messages import HumanMessage, AIMessage
 from langchain_core.runnables import RunnableConfig
 from base_utils.utils import format_ai_message_content
 from datetime import datetime
@@ -14,57 +14,65 @@
 async def chat_with_agent(message, history):
     global agent, agent_config
 
-    # Convert history into messages format that the agent expects
     messages = []
     if history:
-        print("History:", history)  # Debug print
-        for msg in history:
-            if isinstance(msg, dict):
-                if msg.get("role") == "user":
-                    messages.append(HumanMessage(content=msg["content"]))
-                elif msg.get("role") == "assistant":
-                    messages.append({"role": "assistant", "content": msg["content"]})
-
-    # Add the current message
+        print("History:", history)
+        # Iterate through the flat list of message dictionaries
+        for msg_dict in history: 
+            if isinstance(msg_dict, dict) and 'role' in msg_dict and 'content' in msg_dict:
+                role = msg_dict['role']
+                content = msg_dict['content']
+                if role == "user":
+                    messages.append(HumanMessage(content=content))
+                elif role == "assistant":
+                    # Pass the content as is; Langchain can handle AIMessage content
+                    messages.append(AIMessage(content=content)) 
+            elif isinstance(msg_dict, (list, tuple)) and len(msg_dict) == 2:
+                 # Fallback for older Gradio history format (just in case)
+                 user_msg, ai_msg = msg_dict
+                 if user_msg:
+                     messages.append(HumanMessage(content=user_msg))
+                 if ai_msg:
+                     messages.append(AIMessage(content=ai_msg))
+            else:
+                print(f"Skipping unexpected history item: {msg_dict}")
+
+
+    # Add the current user message
     messages.append(HumanMessage(content=message))
 
-    print("Final messages:", messages)  # Debug print
+    print("Final messages being sent to agent:", messages) 
 
     runnable_config = RunnableConfig(
         recursion_limit=agent_config["configurable"]["recursion_limit"],
         configurable={
             "thread_id": agent_config["configurable"]["thread_id"],
             "checkpoint_ns": "chat_mode",
-            "checkpoint_id": str(datetime.now().timestamp())
+            "checkpoint_id": str(datetime.now().timestamp()) # Keep checkpointing per interaction
         }
     )
 
-    response_messages = []
-    yield response_messages
-    # Process message with agent
+    current_turn_messages = [] 
+
     async for chunk in agent.astream(
-        {"messages": messages},  # Pass the full message history
+        {"messages": messages}, 
         runnable_config
     ):
         if "agent" in chunk:
             print("agent in chunk")
-            response = chunk["agent"]["messages"][0].content
-            response_messages.append(dict(
-                role="assistant",
-                content=format_ai_message_content(response, format_mode="markdown")
-            ))
-            print(response_messages)
-            yield response_messages
+            response_content = chunk["agent"]["messages"][0].content
+            formatted_content = format_ai_message_content(response_content, format_mode="markdown")
+            current_turn_messages.append(formatted_content) 
+            print("Yielding agent response:", current_turn_messages)
+            yield "\n\n".join(current_turn_messages) # Join with double newline for better separation
+
         elif "tools" in chunk:
             print("tools in chunk")
             tool_message = str(chunk["tools"]["messages"][0].content)
-            response_messages.append(dict(
-                role="assistant",
-                content=tool_message,
-                metadata={"title": "🛠️ Tool Call"}
-            ))
-            print(response_messages)
-            yield response_messages
+            formatted_content = f"**🛠️ Tool Call:**\n```\n{tool_message}\n```" 
+            current_turn_messages.append(formatted_content)
+            print("Yielding tool response:", current_turn_messages)
+            yield "\n\n".join(current_turn_messages) # Join with double newline
 
 def create_ui():
     # Create the Gradio interface

pyproject.toml

@@ -47,4 +47,4 @@ pytest-playwright = "^0.6.2"
 
 [build-system]
 requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
+build-backend = "poetry.core.masonry.api"