Create app_layer_agent.py

app_layer_agent.py

@@ -0,0 +1,151 @@
+import streamlit as st
+import json
+from engine import UCGEngine,TOOLS
+from groq import Groq
+from components.ucg_g1 import UCG_GRAPH_1
+
+client = Groq(api_key="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")
+
+
+
+runner=UCGEngine(UCG_GRAPH_1,TOOLS)
+
+
+
+def UCGExecutor(operation: str,address: str, ):
+    """
+    Execute a UGC graph operation for a given address.
+    
+    Args:
+        
+        operation (str): The UGC operation to run (goal node in the graph).
+        address (str): Ethereum or Solana wallet address.
+        
+    Returns:
+        dict: The final state returned by the UGC engine.
+    """
+    inputs = {"address": address}
+
+    # Assuming `runner` is an instance of UGCRunner with UGC_GRAPH_1 and TOOLS
+    result = runner.run(operation,inputs)
+    return result
+
+
+
+
+available_functions = {
+    "UCGExecutor":UCGExecutor
+  
+}
+
+
+
+
+def run_conversation(user_prompt):
+    """Run a conversation with tool calling"""
+    
+    messages = [
+
+        {
+            "role":"system",
+            "content":'''
+            
+You are an AI assistant for the UGC Engine. Your job is to convert a user's natural language query into a valid tool call for the UGCExecutor.
+
+The tool accepts only two fields:
+1. "address": The wallet address (Ethereum hex or Solana Base58) to operate on.
+2. "operation": The UGC operation/tool to run. 
+
+Valid operations/tools available:
+- InfuraRPC: Fetch ETH balance for an Ethereum address.
+- HeliusAPI: Fetch SOL balance for a Solana address.
+- EthereumSigner: Produce a cryptographic signature for a given Ethereum address.
+- SolanaSigner: Produce a cryptographic signature for a given Solana address.
+
+Analyze the user query to determine:
+- Which wallet address is mentioned.
+- Which operation/tool the query implies.
+Then call the tool with the required inputs
+'''
+        },
+        {
+            "role": "user",
+            "content": user_prompt,
+        }
+    ]
+    
+    # Define the tool schema
+    tools = [
+        
+    {
+    "type": "function",
+    "function": {
+        "name": "UCGExecutor",
+        "description": "Execute a Unified Capabilities Graph operation for a given wallet address. The AI specifies the target operation, and the engine resolves the workflow automatically.",
+        "parameters": {
+            "type": "object",
+            "properties": {
+
+                 "operation": {
+                    "type": "string",
+                    "description": "The name of the target operation/goal to run in the UGC graph (e.g., EthereumSigner, SolanaSigner, InfuraRPC, HeliusAPI)."
+                },
+                "address": {
+                    "type": "string",
+                    "description": "The wallet address to operate on (Ethereum hex or Solana Base58)."
+                },
+               
+            },
+            "required": ["address", "operation"],
+            "additionalProperties": False
+        }
+    }
+}
+
+
+    ]
+    
+    # Step 1: Make initial API call
+    response = client.chat.completions.create(
+        model="moonshotai/kimi-k2-instruct-0905",
+        messages=messages,
+        tools=tools,
+        tool_choice="required",
+    )
+    
+    response_message = response.choices[0].message
+    tool_calls = response_message.tool_calls
+    
+    # Step 2: Check if the model wants to call tools
+    if tool_calls:
+       
+        # Add the assistant's response to conversation
+        messages.append(response_message)
+        
+        # Step 3: Execute each tool call
+        for tool_call in tool_calls:
+            function_name = tool_call.function.name
+            function_to_call = available_functions[function_name]
+            function_args = json.loads(tool_call.function.arguments)
+            function_response = function_to_call(
+                operation=function_args.get("operation"),
+                address=function_args.get("address")
+            )
+            
+            # Add tool response to conversation
+            messages.append({
+                "tool_call_id": tool_call.id,
+                "role": "tool",
+                "name": function_name,
+                "content": json.dumps(function_response ,indent=2),
+            })
+        
+        # Step 4: Get final response from model
+        second_response = client.chat.completions.create(
+            model="moonshotai/kimi-k2-instruct-0905",
+            messages=messages
+        )
+        return second_response.choices[0].message.content
+    
+    # If no tool calls, return the direct response
+    return response_message.content