1er essai

agent.py

@@ -0,0 +1,154 @@
+from langchain_community.document_loaders import WikipediaLoader
+from langchain_community.document_loaders import ArxivLoader
+from langchain_core.tools import tool
+from langgraph_supervisor import create_supervisor
+from langchain.chat_models import init_chat_model
+import os
+from langchain_openai import ChatOpenAI
+from langgraph.prebuilt import create_react_agent
+from openai import OpenAI
+import re
+
+api_open_ai_agent_key=os.environ["OPENAI_API_KEY"]
+client = OpenAI(api_key=api_open_ai_agent_key)
+llm_4o = ChatOpenAI(
+    model_name="gpt-4o",
+    openai_api_key=api_open_ai_agent_key,  # ou variable d’environnement
+)
+
+
+@tool
+def wiki_search(query: str) -> str:
+    """Search Wikipedia for a query and return maximum 2 results.
+
+    Args:
+        query: The search query."""
+    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 {"wiki_results": formatted_search_docs}
+
+
+@tool
+def arvix_search(query: str) -> str:
+    """Search Arxiv for a query and return maximum 3 result.
+
+    Args:
+        query: The search query."""
+    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 {"arvix_results": formatted_search_docs}
+
+
+@tool
+def web_search_openai_tool(query: str) -> str:
+    """Call web search and the output is a structured text answering the query)
+
+    Args:
+        query: The search query."""
+    response = client.responses.create(
+        model="gpt-4o",
+        tools=[{"type": "web_search_preview"}],
+        input=prompt + query
+    )
+
+    return {"web_results": response.output_text}
+@tool
+def add(a: float, b: float):
+    """Add two numbers."""
+    return a + b
+
+@tool
+def multiply(a: float, b: float):
+    """Multiply two numbers."""
+    return a * b
+
+@tool
+def divide(a: float, b: float):
+    """Divide two numbers."""
+    return a / b
+
+research_agent = create_react_agent(
+    model=llm_4o,
+    tools=[wiki_search, arvix_search],
+    prompt=(
+        "You are a research agent.\n\n"
+        "INSTRUCTIONS:\n"
+        "- Assist ONLY with research-related tasks, DO NOT do any math\n"
+        "- After you're done with your tasks, respond to the supervisor directly\n"
+        "- Respond ONLY with the results of your work, do NOT include ANY other text."
+        "- You only have access to arxiv or wikipedia, no other website"
+    ),
+    name="research_agent",
+)
+
+web_search_openai_agent = create_react_agent(
+    model=llm_4o,
+    tools=[web_search_openai_tool],
+    prompt=(
+        "You are a websearch agent.\n\n"
+        "INSTRUCTIONS:\n"
+        "- Assist ONLY with internet related tasks. DO NOT do any math\n"
+        "- After you're done with your tasks, respond to the supervisor directly\n"
+        "- Respond ONLY with the results of your work, do NOT include ANY other text."
+        "- You can browse the web then give your results to your supervisor "
+    ),
+    name="web_search_openai_agent",
+)
+
+
+
+math_agent = create_react_agent(
+    model=llm_4o,
+
+    tools=[add, multiply, divide],
+    prompt=(
+        "You are a math agent.\n\n"
+        "INSTRUCTIONS:\n"
+        "- Assist ONLY with math-related tasks\n"
+        "- After you're done with your tasks, respond to the supervisor directly\n"
+        "- Respond ONLY with the results of your work, do NOT include ANY other text."
+    ),
+    name="math_agent",
+)
+
+
+supervisor = create_supervisor(
+    model=init_chat_model("openai:gpt-4o", api_key = api_open_ai_agent_key),
+    agents=[research_agent, math_agent, web_search_openai_agent],
+    prompt=(
+        "You are a supervisor managing three agents:\n"
+        "- research_agent: Specialised in ArXiv and Wikipedia. Assign research-related tasks to this agent.\n"
+        "- math_agent: Handles math-related tasks such as solving equations or performing calculations.\n"
+        "- web_search_openai_agent: Can browse the web to find up-to-date and relevant information. Assign web-related tasks to this agent.\n"
+        "Assign work to one agent at a time. Do not call agents in parallel.\n"
+        "When a new question arises, always first consult the research_agent — it may provide useful information.\n"
+        "If research_agent yields no results, then delegate the task to web_search_openai_agent.\n"
+        "Each time you receive information from an agent, you have to analyze, process it then decide what to do (call an agent or give your final answer)."
+        "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. If no punctuation is precised, don't add any. Respect the requested format"
+
+    ),
+    add_handoff_back_messages=True,
+    output_mode="full_history",
+).compile()
+def clean_response(response):
+    match = re.search(r'FINAL ANSWER:\s*(.+)', response['supervisor']['messages'][-1].content)
+    answer = match.group(1).strip() if match else None
+    return answer
+def response_from_agent(question):
+
+    for chunk in supervisor.stream(
+            {"messages": [{"role": "user", "content": question}]}
+    ):
+        response = chunk
+
+
+    response = clean_response(response)
+    return response

app.py

@@ -1,22 +1,23 @@
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
+from agent import response_from_agent, supervisor
 
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
+api_open_ai_agent_key=os.environ["OPENAI_API_KEY"]
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
     def __init__(self):
-        print("BasicAgent initialized.")
+
+        self.my_agent = supervisor
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "BER"
-        print(f"Agent returning fixed answer: {fixed_answer}")
+        fixed_answer = response_from_agent(question)
         return fixed_answer
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
@@ -45,7 +46,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         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"
+    agent_code = 'https://huggingface.co/spaces/Aurele000/Final_Assignment_Template/tree/main'
     print(agent_code)
 
     # 2. Fetch Questions

requirements.txt

@@ -1,2 +1,7 @@
 gradio
-requests
+requests
+langchain
+langgraph
+langchain-community
+langgraph-supervisor
+langchain-openai