Update agent.py

agent.py

@@ -1,375 +1,374 @@
-"""langgraph ReAct LLAMA instruct agent"""
-from dotenv import load_dotenv
-import os
-from typing import TypedDict, List, Dict, Any, Optional
-from langchain_tavily import TavilySearch
-from langchain_core.tools import tool
-import requests
-from urllib.parse import urlparse
-from langgraph.graph import START, StateGraph, MessagesState
-from langgraph.prebuilt import tools_condition,ToolNode
-from langchain_core.messages import SystemMessage, HumanMessage
-from langchain.schema import HumanMessage, SystemMessage
-import json
-from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
-from langchain.agents import initialize_agent
-from langchain.agents.agent_types import AgentType
-from IPython.display import Image, display
-import pandas as pd
-from langchain_community.document_loaders import WikipediaLoader
-from langchain_community.document_loaders import ArxivLoader
-import sympy
-from sympy import sympify
-
-load_dotenv()
-
-@tool
-def arvix_search(query: str) -> str:
-    """
-    Search Arxiv for a query and return up to 3 results.
-
-    Args:
-        query: The search query.
-    
-    Returns:
-        A string with formatted Arxiv search results (truncated to 1000 chars each).
-    """
-    search_docs = ArxivLoader(query=query, load_max_docs=3).load()
-    formatted_search_docs = "\n\n---\n\n".join(
-        [
-            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
-            for doc in search_docs
-        ]
-    )
-    return formatted_search_docs
-
-@tool
-def wiki_search(query: str) -> str:
-    """
-    Search Wikipedia for a query and return up to 2 formatted results.
-
-    Args:
-        query: The search query.
-    
-    Returns:
-        A string with formatted Wikipedia search results.
-    """
-    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
-    formatted_search_docs = "\n\n---\n\n".join(
-        [
-            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
-            for doc in search_docs
-        ]
-    )
-    return formatted_search_docs
-
-@tool
-def analyze_excel_file(input_str: str) -> str:
-    """
-    Analyze an Excel file using pandas and answer a question about it.
-
-    Args:
-        input_str: JSON string with fields:
-            - file_path: Path to the Excel file
-            - query: A question about the file contents (optional)
-        
-    Returns:
-        A summary of the file contents or an error message.
-    """
-    try:
-        import json
-        import pandas as pd
-
-        # Parse JSON input
-        data = json.loads(input_str)
-        file_path = data.get("file_path")
-        query = data.get("query")
-
-        if not file_path:
-            return "Error: 'file_path' is required."
-
-        # Read the Excel file (all sheets)
-        xls = pd.ExcelFile(file_path)
-        sheet_names = xls.sheet_names
-
-        result = f"Excel file loaded with sheets: {', '.join(sheet_names)}.\n\n"
-
-        # Analyze the first sheet as default
-        df = pd.read_excel(xls, sheet_name=sheet_names[0])
-
-        result += f"First sheet '{sheet_names[0]}' loaded with {len(df)} rows and {len(df.columns)} columns.\n"
-        result += f"Columns: {', '.join(df.columns)}\n\n"
-        result += "Summary statistics:\n"
-        result += str(df.describe(include='all'))
-
-        if query:
-            result += f"\n\nQuery: {query} (No advanced query handling implemented yet.)"
-
-        return result
-
-    except json.JSONDecodeError:
-        return "Error: Input must be a valid JSON string with 'file_path' and optional 'query'."
-    except Exception as e:
-        return f"Error analyzing Excel file: {str(e)}"
-
-@tool
-def web_search(query: str) -> str:
-    """
-    Perform a web search using Tavily and return the result.
-    """
-    try:
-        search = TavilySearch()
-        result = search.invoke(query)
-        
-        if isinstance(result, dict) and "results" in result:
-            docs = result["results"]
-            return "\n\n---\n\n".join(
-                [f"{doc['title']}\n{doc['url']}\n{doc['content']}" for doc in docs]
-            )
-        else:
-            return f"Error: Unexpected Tavily response format: {result}"
-    except Exception as e:
-        return f"Error using TavilySearch: {str(e)}"
-    
-@tool
-def analyze_csv_file(input_str: str) -> str:
-    """
-    Analyze a CSV file using pandas and answer a question about it.
-
-    Args:
-        input_str: JSON string with fields:
-            - file_path: Path to the CSV file
-            - query: A question about the file contents
-        
-    Returns:
-        A basic analysis of the file or an error message
-    """
-    try:
-
-        # Parse the JSON string
-        data = json.loads(input_str)
-        file_path = data.get("file_path")
-        query = data.get("query")
-
-        if not file_path:
-            return "Error: 'file_path' is required."
-        
-        # Read the CSV
-        df = pd.read_csv(file_path)
-
-        # Basic metadata
-        result = f"CSV file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
-        result += f"Columns: {', '.join(df.columns)}\n\n"
-        result += "Summary statistics:\n"
-        result += str(df.describe(include='all', datetime_is_numeric=True))
-
-        # Optionally handle a query (not implemented in detail here)
-        if query:
-            result += f"\n\nQuery: {query} (No logic implemented yet to answer it.)"
-
-        return result
-
-    except json.JSONDecodeError:
-        return "Error: Input must be a valid JSON string with 'file_path' and optional 'query'."
-    except Exception as e:
-        return f"Error analyzing CSV file: {str(e)}"
-
-
-@tool
-def download_file_from_url(input_str: str) -> str:
-    """
-    Downloads a file from a URL and saves it in the 'saved_files' directory.
-
-    Args:
-        input_str (str): A JSON string with keys:
-            - "url": the URL to download from (required)
-            - "filename": optional filename to save as
-
-    Returns:
-        A message indicating success and file path, or an error message.
-    """
-    try:
-        # Parse the input string
-        data = json.loads(input_str)
-        url = data.get("url")
-        filename = data.get("filename", None)
-
-        if not url:
-            return "Error: 'url' is required in the input JSON."
-
-        # Create directory if not exists
-        new_dir = os.path.join(os.getcwd(), "saved_files")
-        os.makedirs(new_dir, exist_ok=True)
-
-        # Generate filename if not provided
-        if not filename:
-            path = urlparse(url).path
-            filename = os.path.basename(path) or f"downloaded_{os.urandom(4).hex()}"
-
-        filepath = os.path.join(new_dir, filename)
-
-        # Download the file
-        response = requests.get(url, stream=True)
-        response.raise_for_status()
-
-        # Save the file
-        with open(filepath, 'wb') as f:
-            for chunk in response.iter_content(chunk_size=8192):
-                f.write(chunk)
-
-        return f"File downloaded to {filepath}. You can now process this file."
-
-    except json.JSONDecodeError:
-        return "Error: Invalid JSON input. Expected format: {\"url\": \"...\", \"filename\": \"optional_name\"}"
-    except Exception as e:
-        return f"Error: {str(e)}"
-    
-@tool
-def find_file_for_question(input_str: str) -> str:
-    """
-    Constructs a multimodal question prompt for the agent to answer.
-
-    Args:
-        input_str (str): JSON string with keys:
-            - task_id: ID of the file
-            - question: The actual question
-            - file_name: (optional) file name, if image is involved
-
-    Returns:
-        A full natural language prompt that includes the file URL if needed.
-    """
-    try:
-        data = json.loads(input_str)
-        task_id = data.get("task_id")
-        question = data.get("question")
-        file_name = data.get("file_name")
-
-        if not task_id or not question:
-            return "Error: Missing 'task_id' or 'question' in input."
-
-        prompt = question
-
-        if file_name:
-            file_url = f"https://agents-course-unit4-scoring.hf.space/files/{task_id}"
-            prompt += f"\n\nImage file to consider: {file_url}"
-
-        return prompt
-
-    except json.JSONDecodeError:
-        return "Error: Invalid input. Provide JSON with 'task_id', 'question', and optional 'file_name'."
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-@tool
-def calculate_math_expression(expr: str) -> str:
-    """
-    Evaluate a symbolic math expression (e.g., algebraic, numeric, or arithmetic).
-
-    Use this tool if the input is a math expression like '2 + 3*sqrt(4)', 'sin(pi/2)', or '3 ** 2'.
-
-    Input:
-        A raw string expression. Example: '2 + 3 * sqrt(4)'
-
-    Returns:
-        A float result as a string if successful,
-        otherwise a string with the error message.
-    """
-    try:
-        result = sympify(expr)
-        # Check if the result is an actual sympy object with evalf
-        if hasattr(result, "evalf"):
-            return str(result.evalf())
-        else:
-            return str(result)  # Already a number or something that can't be evaluated further
-    except Exception as e:
-        return f"Error: {str(e)}"
-    
-class AgentState(TypedDict):
-    messages: str                   # The original input question
-    attachments: Dict[str, Any]     # Attachments (e.g., images, files) related to the question
-    context: List[Dict]             # Retrieved context (e.g., search results, documents)
-    reasoning: List[str]            # Step-by-step reasoning traces
-    partial_answer: Optional[str]   # Intermediate answer (if multi-step)
-    final_answer: Optional[str]     # Final answer to return
-    tools_used: List[str]           # Track which tools were called (for debugging)
-
-tools = [
-    find_file_for_question,
-    analyze_excel_file,
-    analyze_csv_file,
-    web_search,
-    arvix_search,
-    wiki_search,
-    download_file_from_url,
-    calculate_math_expression]
-
-
-# Build graph function
-def build_graph():
-    """Build the graph"""
-    llm = HuggingFaceEndpoint(
-    repo_id="meta-llama/Llama-4-Scout-17B-16E-Instruct",
-    temperature= 0,
-    provider="novita",
-
-    )
-
-    chat_model = ChatHuggingFace(llm=llm)
-
-    agent = initialize_agent(
-        tools=tools,
-        llm=chat_model,
-        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
-        verbose=True,
-        handle_parsing_errors=True
-    )
-
-    def assistant(state: AgentState):
-        system_prompt = f"""
-        You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER].
-        YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. 
-        If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. 
-        If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. 
-        If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
-        """
-        sys_msg = SystemMessage(content= system_prompt)
-        
-        return {
-        "messages": [agent.invoke({"input": [sys_msg] + state["messages"]})],
-        }
-
-    builder = StateGraph(AgentState)
-
-    # Define nodes: these do the work
-    builder.add_node("assistant", assistant)
-    builder.add_node("tools", ToolNode(tools))
-
-    # Define edges: these determine how the control flow moves
-    builder.add_edge(START, "assistant")
-    builder.add_conditional_edges(
-        "assistant",
-        # If the latest message requires a tool, route to tools
-        # Otherwise, provide a direct response
-        tools_condition,
-    )
-    builder.add_edge("tools", "assistant")
-
-    return builder.compile()
-
-
-if __name__ == "__main__":
-    #test the agent with a sample question
-    question = "what was the first university in the world?"
-    messages = [HumanMessage(content=question)]
-    output = build_graph().invoke({"messages": messages})
-    #print out the response 
-    for entry in output["messages"]:
-        for msg in entry["input"]:
-            if isinstance(msg, HumanMessage):
-                print("🧑 Human:", msg.content)
-            elif isinstance(msg, SystemMessage):
-                print("⚙️ System:", msg.content)
-        print("🤖 Output:", entry["output"])
-        print("-" * 50)
-
-
+"""langgraph ReAct LLAMA instruct agent"""
+from dotenv import load_dotenv
+import os
+from typing import TypedDict, List, Dict, Any, Optional
+from langchain_tavily import TavilySearch
+from langchain_core.tools import tool
+import requests
+from urllib.parse import urlparse
+from langgraph.graph import START, StateGraph, MessagesState
+from langgraph.prebuilt import tools_condition,ToolNode
+from langchain_core.messages import SystemMessage, HumanMessage
+from langchain.schema import HumanMessage, SystemMessage
+import json
+from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
+from langchain.agents import initialize_agent
+from langchain.agents.agent_types import AgentType
+import pandas as pd
+from langchain_community.document_loaders import WikipediaLoader
+from langchain_community.document_loaders import ArxivLoader
+import sympy
+from sympy import sympify
+
+load_dotenv()
+
+@tool
+def arvix_search(query: str) -> str:
+    """
+    Search Arxiv for a query and return up to 3 results.
+
+    Args:
+        query: The search query.
+    
+    Returns:
+        A string with formatted Arxiv search results (truncated to 1000 chars each).
+    """
+    search_docs = ArxivLoader(query=query, load_max_docs=3).load()
+    formatted_search_docs = "\n\n---\n\n".join(
+        [
+            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
+            for doc in search_docs
+        ]
+    )
+    return formatted_search_docs
+
+@tool
+def wiki_search(query: str) -> str:
+    """
+    Search Wikipedia for a query and return up to 2 formatted results.
+
+    Args:
+        query: The search query.
+    
+    Returns:
+        A string with formatted Wikipedia search results.
+    """
+    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
+    formatted_search_docs = "\n\n---\n\n".join(
+        [
+            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
+            for doc in search_docs
+        ]
+    )
+    return formatted_search_docs
+
+@tool
+def analyze_excel_file(input_str: str) -> str:
+    """
+    Analyze an Excel file using pandas and answer a question about it.
+
+    Args:
+        input_str: JSON string with fields:
+            - file_path: Path to the Excel file
+            - query: A question about the file contents (optional)
+        
+    Returns:
+        A summary of the file contents or an error message.
+    """
+    try:
+        import json
+        import pandas as pd
+
+        # Parse JSON input
+        data = json.loads(input_str)
+        file_path = data.get("file_path")
+        query = data.get("query")
+
+        if not file_path:
+            return "Error: 'file_path' is required."
+
+        # Read the Excel file (all sheets)
+        xls = pd.ExcelFile(file_path)
+        sheet_names = xls.sheet_names
+
+        result = f"Excel file loaded with sheets: {', '.join(sheet_names)}.\n\n"
+
+        # Analyze the first sheet as default
+        df = pd.read_excel(xls, sheet_name=sheet_names[0])
+
+        result += f"First sheet '{sheet_names[0]}' loaded with {len(df)} rows and {len(df.columns)} columns.\n"
+        result += f"Columns: {', '.join(df.columns)}\n\n"
+        result += "Summary statistics:\n"
+        result += str(df.describe(include='all'))
+
+        if query:
+            result += f"\n\nQuery: {query} (No advanced query handling implemented yet.)"
+
+        return result
+
+    except json.JSONDecodeError:
+        return "Error: Input must be a valid JSON string with 'file_path' and optional 'query'."
+    except Exception as e:
+        return f"Error analyzing Excel file: {str(e)}"
+
+@tool
+def web_search(query: str) -> str:
+    """
+    Perform a web search using Tavily and return the result.
+    """
+    try:
+        search = TavilySearch()
+        result = search.invoke(query)
+        
+        if isinstance(result, dict) and "results" in result:
+            docs = result["results"]
+            return "\n\n---\n\n".join(
+                [f"{doc['title']}\n{doc['url']}\n{doc['content']}" for doc in docs]
+            )
+        else:
+            return f"Error: Unexpected Tavily response format: {result}"
+    except Exception as e:
+        return f"Error using TavilySearch: {str(e)}"
+    
+@tool
+def analyze_csv_file(input_str: str) -> str:
+    """
+    Analyze a CSV file using pandas and answer a question about it.
+
+    Args:
+        input_str: JSON string with fields:
+            - file_path: Path to the CSV file
+            - query: A question about the file contents
+        
+    Returns:
+        A basic analysis of the file or an error message
+    """
+    try:
+
+        # Parse the JSON string
+        data = json.loads(input_str)
+        file_path = data.get("file_path")
+        query = data.get("query")
+
+        if not file_path:
+            return "Error: 'file_path' is required."
+        
+        # Read the CSV
+        df = pd.read_csv(file_path)
+
+        # Basic metadata
+        result = f"CSV file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
+        result += f"Columns: {', '.join(df.columns)}\n\n"
+        result += "Summary statistics:\n"
+        result += str(df.describe(include='all', datetime_is_numeric=True))
+
+        # Optionally handle a query (not implemented in detail here)
+        if query:
+            result += f"\n\nQuery: {query} (No logic implemented yet to answer it.)"
+
+        return result
+
+    except json.JSONDecodeError:
+        return "Error: Input must be a valid JSON string with 'file_path' and optional 'query'."
+    except Exception as e:
+        return f"Error analyzing CSV file: {str(e)}"
+
+
+@tool
+def download_file_from_url(input_str: str) -> str:
+    """
+    Downloads a file from a URL and saves it in the 'saved_files' directory.
+
+    Args:
+        input_str (str): A JSON string with keys:
+            - "url": the URL to download from (required)
+            - "filename": optional filename to save as
+
+    Returns:
+        A message indicating success and file path, or an error message.
+    """
+    try:
+        # Parse the input string
+        data = json.loads(input_str)
+        url = data.get("url")
+        filename = data.get("filename", None)
+
+        if not url:
+            return "Error: 'url' is required in the input JSON."
+
+        # Create directory if not exists
+        new_dir = os.path.join(os.getcwd(), "saved_files")
+        os.makedirs(new_dir, exist_ok=True)
+
+        # Generate filename if not provided
+        if not filename:
+            path = urlparse(url).path
+            filename = os.path.basename(path) or f"downloaded_{os.urandom(4).hex()}"
+
+        filepath = os.path.join(new_dir, filename)
+
+        # Download the file
+        response = requests.get(url, stream=True)
+        response.raise_for_status()
+
+        # Save the file
+        with open(filepath, 'wb') as f:
+            for chunk in response.iter_content(chunk_size=8192):
+                f.write(chunk)
+
+        return f"File downloaded to {filepath}. You can now process this file."
+
+    except json.JSONDecodeError:
+        return "Error: Invalid JSON input. Expected format: {\"url\": \"...\", \"filename\": \"optional_name\"}"
+    except Exception as e:
+        return f"Error: {str(e)}"
+    
+@tool
+def find_file_for_question(input_str: str) -> str:
+    """
+    Constructs a multimodal question prompt for the agent to answer.
+
+    Args:
+        input_str (str): JSON string with keys:
+            - task_id: ID of the file
+            - question: The actual question
+            - file_name: (optional) file name, if image is involved
+
+    Returns:
+        A full natural language prompt that includes the file URL if needed.
+    """
+    try:
+        data = json.loads(input_str)
+        task_id = data.get("task_id")
+        question = data.get("question")
+        file_name = data.get("file_name")
+
+        if not task_id or not question:
+            return "Error: Missing 'task_id' or 'question' in input."
+
+        prompt = question
+
+        if file_name:
+            file_url = f"https://agents-course-unit4-scoring.hf.space/files/{task_id}"
+            prompt += f"\n\nImage file to consider: {file_url}"
+
+        return prompt
+
+    except json.JSONDecodeError:
+        return "Error: Invalid input. Provide JSON with 'task_id', 'question', and optional 'file_name'."
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+@tool
+def calculate_math_expression(expr: str) -> str:
+    """
+    Evaluate a symbolic math expression (e.g., algebraic, numeric, or arithmetic).
+
+    Use this tool if the input is a math expression like '2 + 3*sqrt(4)', 'sin(pi/2)', or '3 ** 2'.
+
+    Input:
+        A raw string expression. Example: '2 + 3 * sqrt(4)'
+
+    Returns:
+        A float result as a string if successful,
+        otherwise a string with the error message.
+    """
+    try:
+        result = sympify(expr)
+        # Check if the result is an actual sympy object with evalf
+        if hasattr(result, "evalf"):
+            return str(result.evalf())
+        else:
+            return str(result)  # Already a number or something that can't be evaluated further
+    except Exception as e:
+        return f"Error: {str(e)}"
+    
+class AgentState(TypedDict):
+    messages: str                   # The original input question
+    attachments: Dict[str, Any]     # Attachments (e.g., images, files) related to the question
+    context: List[Dict]             # Retrieved context (e.g., search results, documents)
+    reasoning: List[str]            # Step-by-step reasoning traces
+    partial_answer: Optional[str]   # Intermediate answer (if multi-step)
+    final_answer: Optional[str]     # Final answer to return
+    tools_used: List[str]           # Track which tools were called (for debugging)
+
+tools = [
+    find_file_for_question,
+    analyze_excel_file,
+    analyze_csv_file,
+    web_search,
+    arvix_search,
+    wiki_search,
+    download_file_from_url,
+    calculate_math_expression]
+
+
+# Build graph function
+def build_graph():
+    """Build the graph"""
+    llm = HuggingFaceEndpoint(
+    repo_id="meta-llama/Llama-4-Scout-17B-16E-Instruct",
+    temperature= 0,
+    provider="novita",
+
+    )
+
+    chat_model = ChatHuggingFace(llm=llm)
+
+    agent = initialize_agent(
+        tools=tools,
+        llm=chat_model,
+        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+        verbose=True,
+        handle_parsing_errors=True
+    )
+
+    def assistant(state: AgentState):
+        system_prompt = f"""
+        You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER].
+        YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. 
+        If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. 
+        If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. 
+        If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
+        """
+        sys_msg = SystemMessage(content= system_prompt)
+        
+        return {
+        "messages": [agent.invoke({"input": [sys_msg] + state["messages"]})],
+        }
+
+    builder = StateGraph(AgentState)
+
+    # Define nodes: these do the work
+    builder.add_node("assistant", assistant)
+    builder.add_node("tools", ToolNode(tools))
+
+    # Define edges: these determine how the control flow moves
+    builder.add_edge(START, "assistant")
+    builder.add_conditional_edges(
+        "assistant",
+        # If the latest message requires a tool, route to tools
+        # Otherwise, provide a direct response
+        tools_condition,
+    )
+    builder.add_edge("tools", "assistant")
+
+    return builder.compile()
+
+
+if __name__ == "__main__":
+    #test the agent with a sample question
+    question = "what was the first university in the world?"
+    messages = [HumanMessage(content=question)]
+    output = build_graph().invoke({"messages": messages})
+    #print out the response 
+    for entry in output["messages"]:
+        for msg in entry["input"]:
+            if isinstance(msg, HumanMessage):
+                print("🧑 Human:", msg.content)
+            elif isinstance(msg, SystemMessage):
+                print("⚙️ System:", msg.content)
+        print("🤖 Output:", entry["output"])
+        print("-" * 50)
+
+