Update agent.py

agent.py

@@ -1,89 +1,118 @@
-import os
-from dotenv import load_dotenv
-from langchain.tools import Tool
-from langchain.utilities import SerpAPIWrapper
-from langgraph.graph.message import add_messages
-from langgraph.graph import START, StateGraph
-from langgraph.prebuilt import ToolNode, tools_condition
-from langchain_core.messages import AnyMessage, HumanMessage
+from langchain_core.tools import tool
+from langgraph.graph import StateGraph, START, MessagesState
+from langgraph.prebuilt import tools_condition, ToolNode
 from langchain_groq import ChatGroq
-from typing import TypedDict, Annotated
+from langchain_core.messages import HumanMessage, SystemMessage
+import math
 
-# Load environment variables
-load_dotenv()
-groq_api_key = os.getenv("GROQ_API_KEY")
-serpapi_api_key = os.getenv("SERPAPI_API_KEY")
+# -------------------------
+# Tools
+# -------------------------
+@tool
+def add(a: float, b: float) -> float:
+    return a + b
 
-# --- Tool Definitions ---
-def calculator_tool_func(query: str):
+@tool
+def subtract(a: float, b: float) -> float:
+    return a - b
+
+@tool
+def multiply(a: float, b: float) -> float:
+    return a * b
+
+@tool
+def divide(a: float, b: float) -> float:
+    if b == 0:
+        return float('inf')
+    return a / b
+
+@tool
+def modulus(a: int, b: int) -> int:
+    return a % b
+
+@tool
+def python_eval(code: str) -> str:
     try:
-        result = str(eval(query, {"__builtins__": {}}))
-        return result
-    except Exception:
-        return "I couldn't compute that expression."
-
-calculator_tool = Tool(
-    name="Calculator",
-    func=calculator_tool_func,
-    description="Performs basic arithmetic calculations."
-)
-
-def weather_tool_func(location: str):
-    return f"The weather in {location}: Sunny, 25°C."
-
-weather_tool = Tool(
-    name="Weather",
-    func=weather_tool_func,
-    description="Provides mock weather information for a given location."
-)
-
-serpapi = SerpAPIWrapper(serpapi_api_key=serpapi_api_key)
-web_search_tool = Tool(
-    name="WebSearch",
-    func=serpapi.run,
-    description="Searches the web for recent information using SerpAPI."
-)
-
-# --- Model and Tools Binding ---
-tools = [web_search_tool, calculator_tool, weather_tool]
-llm = ChatGroq(model="deepseek-r1-distill-llama-70b", groq_api_key=groq_api_key)
-llm_with_tools = llm.bind_tools(tools)
-
-# --- Agent State ---
-class AgentState(TypedDict):
-    messages: Annotated[list[AnyMessage], add_messages]
-
-def assistant(state: AgentState):
-    return {"messages": [llm_with_tools.invoke(state["messages"])]}
-
-# --- Agent Graph ---
-builder = StateGraph(AgentState)
-builder.add_node("assistant", assistant)
-builder.add_node("tools", ToolNode(tools))
-builder.add_edge(START, "assistant")
-builder.add_conditional_edges("assistant", tools_condition)
-builder.add_edge("tools", "assistant")
-
-# 👇 The compiled agent: use with .invoke({...})
-ninu = builder.compile()
-
-# --- Run Function ---
-def run_ninu(query: str):
-    conversation = []
-
-    system_prompt = """
-You are a smart, helpful, and curious assistant. Think step by step and use any available tools if needed. Explain your reasoning, be honest about what you know and don't know, and speak naturally in English.
+        result = eval(code)
+        return f"Result: {result}"
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+@tool
+def translate_to_arabic(text: str) -> str:
+    return f"Arabic translation of '{text}'"
+
+@tool
+def translate_to_english(text: str) -> str:
+    return f"English translation of '{text}'"
+
+@tool
+def summarize_text(text: str) -> str:
+    return f"Summary: {text[:100]}..."
+
+@tool
+def analyze_sentiment(text: str) -> str:
+    if any(word in text.lower() for word in ["good", "great", "excellent", "happy"]):
+        return "Sentiment: Positive"
+    elif any(word in text.lower() for word in ["bad", "terrible", "sad", "hate"]):
+        return "Sentiment: Negative"
+    return "Sentiment: Neutral"
+
+@tool
+def speech_to_text_stub(audio: str) -> str:
+    return "Converted audio to text: (This is a placeholder result)"
+
+# -------------------------
+# System Prompt
+# -------------------------
+system_prompt = """
+You are DeepSeek, a thoughtful and curious AI assistant. You analyze before answering.
+You always reflect step by step, consider using tools intelligently, and aim for precision and clarity.
+
+Behaviors:
+- Think deeply about the user's question.
+- Decide if you need tools to calculate, search, translate, or analyze.
+- If no tool is needed, answer directly with your own knowledge.
+
+Respond in a helpful, concise, and accurate way.
 """
-    conversation.append(HumanMessage(content=system_prompt.strip()))
-    conversation.append(HumanMessage(content=query))
+sys_msg = SystemMessage(content=system_prompt)
+
+# -------------------------
+# Build LangGraph Agent
+# -------------------------
+def build_deepseek_graph():
+    llm = ChatGroq(model="deepseek-llm-67b", temperature=0.3)
+
+    all_tools = [
+        add, subtract, multiply, divide, modulus,
+        translate_to_arabic, translate_to_english,
+        summarize_text, analyze_sentiment,
+        python_eval, speech_to_text_stub
+    ]
+    llm_with_tools = llm.bind_tools(all_tools)
+
+    def assistant(state: MessagesState):
+        return {"messages": [llm_with_tools.invoke(state["messages"])]}
+
+    builder = StateGraph(MessagesState)
+    builder.add_node("assistant", assistant)
+    builder.add_node("tools", ToolNode(all_tools))
+
+    builder.add_edge(START, "assistant")
+    builder.add_conditional_edges("assistant", tools_condition)
+    builder.add_edge("tools", "assistant")
 
-    response = ninu.invoke({"messages": conversation})
-    for message in reversed(response["messages"]):
-        if isinstance(message.content, str):
-            return message.content.strip()
-    return "No response received."
+    ninu = builder.compile()  
+    return ninu
 
-# --- Example test ---
+# -------------------------
+# Example Run
+# -------------------------
 if __name__ == "__main__":
-    result = run_ninu("What is the capital of Japan?")
-    print("🧠 NINU replied:", result)
+    ninu = build_deepseek_graph()  
+    user_question = "ترجم لي الجملة: Artificial intelligence is transforming education."
+    messages = [sys_msg, HumanMessage(content=user_question)]
+    result = ninu.invoke({"messages": messages})
+    for msg in result["messages"]:
+        print("\n", msg.content)