Update agents.py

agents.py

@@ -1,202 +1,152 @@
 import os
-import gradio as gr
-import requests
-import inspect
-import pandas as pd
-from agents import create_agent_flow
-from langchain_core.messages import HumanMessage
-
-# (Keep Constants as is)
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
-    
-    def __init__(self):
-        print("BasicAgent initialized.")
-        self.agent = create_agent_flow()
-    
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-      
-        question = [HumanMessage(content=question)]
-        question_ask = self.agent.invoke({"messages": question})
-        response = question_ask['messages'][-1].content
-        print(f"Agent returning fixed answer: {response}")
-        return response[8:]
-
-def run_and_submit_all( profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
-    """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
-
-    if profile:
-        username= f"{profile.username}"
-        print(f"User logged in: {username}")
+
+from langgraph.graph import StateGraph, START, MessagesState
+from langgraph.prebuilt import ToolNode, tools_condition
+
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_groq import ChatGroq
+from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
+
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
+from langchain_community.vectorstores import SupabaseVectorStore
+
+from langchain_core.messages import SystemMessage, HumanMessage
+from langchain_core.tools import tool
+
+from supabase.client import create_client, Client
+
+
+# Load environment variables
+
+# ---- Basic Arithmetic Utilities ---- #
+@tool
+def multiply(a: int, b: int) -> int:
+    """Returns the product of two integers."""
+    return a * b
+
+@tool
+def add(a: int, b: int) -> int:
+    """Returns the sum of two integers."""
+    return a + b
+
+@tool
+def subtract(a: int, b: int) -> int:
+    """Returns the difference between two integers."""
+    return a - b
+
+@tool
+def divide(a: int, b: int) -> float:
+    """Performs division and handles zero division errors."""
+    if b == 0:
+        raise ValueError("Division by zero is undefined.")
+    return a / b
+
+@tool
+def modulus(a: int, b: int) -> int:
+    """Returns the remainder after division."""
+    return a % b
+
+
+# ---- Search Tools ---- #
+@tool
+def search_wikipedia(query: str) -> str:
+    """Returns up to 2 documents related to a query from Wikipedia."""
+    docs = WikipediaLoader(query=query, load_max_docs=2).load()
+    return {"wiki_results": "\n\n---\n\n".join(
+        f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}'
+        for doc in docs
+    )}
+
+@tool
+def search_web(query: str) -> str:
+    """Fetches up to 3 web results using Tavily."""
+    results = TavilySearchResults(max_results=3).invoke(query=query)
+    return {"web_results": "\n\n---\n\n".join(
+        f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}'
+        for doc in results
+    )}
+
+@tool
+def search_arxiv(query: str) -> str:
+    """Retrieves up to 3 papers related to the query from ArXiv."""
+    results = ArxivLoader(query=query, load_max_docs=3).load()
+    return {"arvix_results": "\n\n---\n\n".join(
+        f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}'
+        for doc in results
+    )}
+
+
+system_message = SystemMessage(content="""You are a helpful assistant tasked with answering questions using a set of tools. Now, 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 a comma in the number and avoid units like $ or % unless specified otherwise.
+- If you are asked for a string, avoid using articles and abbreviations (e.g. for cities), and write digits in plain text unless specified otherwise.
+- If you are asked for a comma-separated list, apply the above rules depending on whether each item is a number or string.
+
+Your answer should start only with "FINAL ANSWER: ", followed by your result.""")
+
+toolset = [
+    multiply,
+    add,
+    subtract,
+    divide,
+    modulus,
+    search_wikipedia,
+    search_web,
+    search_arxiv,
+]
+
+
+# ---- Graph Construction ---- #
+def create_agent_flow(provider: str = "groq"):
+    """Constructs the LangGraph conversational flow with tool support."""
+
+    if provider == "google":
+        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
+    elif provider == "groq":
+        llm = ChatGroq(api_key="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" , model="qwen-qwq-32b", temperature=0) 
+    elif provider == "huggingface":
+        llm = ChatHuggingFace(llm=HuggingFaceEndpoint(
+            url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
+            temperature=0
+        ))
     else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
-
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
-
-    # 1. Instantiate Agent ( modify this part to create your agent)
-    try:
-        agent = BasicAgent()
-    except Exception as e:
-        print(f"Error instantiating agent: {e}")
-        return f"Error initializing agent: {e}", None
-    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
-
-    # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
-    try:
-        response = requests.get(questions_url, timeout=15)
-        response.raise_for_status()
-        questions_data = response.json()
-        if not questions_data:
-             print("Fetched questions list is empty.")
-             return "Fetched questions list is empty or invalid format.", None
-        print(f"Fetched {len(questions_data)} questions.")
-    except requests.exceptions.RequestException as e:
-        print(f"Error fetching questions: {e}")
-        return f"Error fetching questions: {e}", None
-    except requests.exceptions.JSONDecodeError as e:
-         print(f"Error decoding JSON response from questions endpoint: {e}")
-         print(f"Response text: {response.text[:500]}")
-         return f"Error decoding server response for questions: {e}", None
-    except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
-
-    # 3. Run your Agent
-    results_log = []
-    answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
-        task_id = item.get("task_id")
-        question_text = item.get("question")
-        if not task_id or question_text is None:
-            print(f"Skipping item with missing task_id or question: {item}")
-            continue
-        try:
-            submitted_answer = agent(question_text)
-            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
-        except Exception as e:
-             print(f"Error running agent on task {task_id}: {e}")
-             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
-
-    if not answers_payload:
-        print("Agent did not produce any answers to submit.")
-        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
-
-    # 4. Prepare Submission 
-    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
-    print(status_update)
-
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
-    try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
-        result_data = response.json()
-        final_status = (
-            f"Submission Successful!\n"
-            f"User: {result_data.get('username')}\n"
-            f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
-            f"Message: {result_data.get('message', 'No message received.')}"
-        )
-        print("Submission successful.")
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
-    except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
-        try:
-            error_json = e.response.json()
-            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
-        except requests.exceptions.JSONDecodeError:
-            error_detail += f" Response: {e.response.text[:500]}"
-        status_message = f"Submission Failed: {error_detail}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.Timeout:
-        status_message = "Submission Failed: The request timed out."
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.RequestException as e:
-        status_message = f"Submission Failed: Network error - {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-
-
-# --- Build Gradio Interface using Blocks ---
-with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
-    gr.Markdown(
-        """
-        **Instructions:**
-        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
-        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
-        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-        ---
-        **Disclaimers:**
-        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
-        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
-        """
-    )
-
-    gr.LoginButton()
-
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
-
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
+        raise ValueError("Unsupported provider. Choose from: 'google', 'groq', 'huggingface'.")
+
+    llm_toolchain = llm.bind_tools(toolset)
+
+    # Assistant node behavior
+    def assistant_node(state: MessagesState):
+        response = llm_toolchain.invoke(state["messages"])
+        return {"messages": [response]}
+
+    
+    # Build the conversational graph
+    graph01 = StateGraph(MessagesState)
+    graph01.add_node("assistant", assistant_node)
+    graph01.add_node("tools", ToolNode(toolset))
+    graph01.add_edge(START, "assistant")
+    graph01.add_conditional_edges("assistant", tools_condition)
+    graph01.add_edge("tools", "assistant")
+
+    return graph01.compile()
+
 
 if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for SPACE_HOST and SPACE_ID at startup for information
-    space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
-
-    if space_host_startup:
-        print(f"✅ SPACE_HOST found: {space_host_startup}")
-        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
-    else:
-        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
+    question = "What is the capital of France?"
 
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
-        print(f"✅ SPACE_ID found: {space_id_startup}")
-        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
-        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
-    else:
-        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
+    # Build the graph
+    compiled_graph = create_agent_flow(provider="groq")
+
+    # Prepare input messages
+    messages = [system_message, HumanMessage(content=question)]
 
-    print("-"*(60 + len(" App Starting ")) + "\n")
+    # Run the graph
+    output_state = compiled_graph.invoke({"messages": messages})
 
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    # Print the final output
+    for m in output_state["messages"]:
+        print(m.content)