style: applied formatting and types for some env variables

agent.py

@@ -1,34 +1,27 @@
-import os
 from pathlib import Path
 from typing import TypedDict, Annotated
 from uuid import uuid4
+import os
 import requests
 
-from langgraph.graph.message import add_messages
 from langchain_core.messages import AnyMessage, HumanMessage, AIMessage, SystemMessage
-from langgraph.prebuilt import ToolNode
-from langgraph.graph import START, StateGraph
+from langchain_core.rate_limiters import InMemoryRateLimiter
+from langchain_google_genai import ChatGoogleGenerativeAI
 from langgraph.checkpoint.memory import MemorySaver
+from langgraph.graph import START, StateGraph
+from langgraph.graph.message import add_messages
+from langgraph.prebuilt import ToolNode
 from langgraph.prebuilt import tools_condition
-
-# from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_core.rate_limiters import InMemoryRateLimiter
 from smolagents import CodeAgent, HfApiModel
 
 # Import our custom tools from their modules
 from tools import basic_tools
 
-# HF Chat interface
-# llm = HuggingFaceEndpoint(
-#     repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
-#     huggingfacehub_api_token=os.environ["HUGGINGFACEHUB_API_TOKEN"],
-# )
-# chat = ChatHuggingFace(llm=llm, verbose=True)
+ChatHuggingFace(llm=llm, verbose=True)
 
 # Google's chat interface
-RPM = os.environ.get("AGENT_MODEL_RPM", 9)
-TPM = os.environ.get("AGENT_MODEL_TPM", 250000)
+RPM = int(os.environ.get("AGENT_MODEL_RPM", 8))
+TPM = int(os.environ.get("AGENT_MODEL_TPM", 200000))
 FILES_ENDPOINT = os.environ.get(
     "FILES_ENDPOINT", "https://agents-course-unit4-scoring.hf.space"
 )

app.py

@@ -1,36 +1,34 @@
-import os
+from agent import (
+    builder,
+    HumanMessage,
+    memory,
+    create_config,
+    get_system_prompt,
+    insert_file_into_query,
+    download_requested_file,
+)
+from pprint import pprint
 import gradio as gr
-import requests
 import inspect
+import os
 import pandas as pd
-from pprint import pprint
-from agent import builder, HumanMessage, memory, create_config, get_system_prompt, insert_file_into_query, download_requested_file
+import requests
 
 # (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.")
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
-        
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+
+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
+    space_id = os.getenv("SPACE_ID")  # Get the SPACE_ID for sending link to the code
 
     if profile:
-        username= f"{profile.username}"
+        username = f"{profile.username}"
         print(f"User logged in: {username}")
     else:
         print("User not logged in.")
@@ -43,26 +41,28 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     # 1. Instantiate Agent ( modify this part to create your agent)
     try:
         # agent = builder.compile(checkpointer=memory)
-        agent = builder.compile() # Memory less
+        agent = builder.compile()  # Memory less
         agent_config = create_config()
         system_prompt = get_system_prompt()
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
 
-    # define function to call agent    
+    # define function to call agent
     def run_agent(query: str, file_name: str = ""):
         separator = "-" * 20 + "\n"
-        print(separator + f"Agent received question (first 50 chars): {query[:50]}...")
+        print(separator + f"Agent received question (first 100 chars): {query[:100]}...")
         if file_name != "":
             query = insert_file_into_query(query, file_name)
         response = agent.invoke(
             {"messages": [system_prompt, HumanMessage(content=query)]},
-                                )
+        )
         # Print debug message
         print("All messages >>")
-        pprint(response["messages"])
-        text_response = str(response["messages"][-1].content).split("FINAL ANSWER:")[-1].strip()
+        pprint(response["messages"], compact=False)
+        text_response = (
+            str(response["messages"][-1].content).split("FINAL ANSWER:")[-1].strip()
+        )
         print(f"Agent returning answer: {text_response}\n" + separator)
         return text_response
 
@@ -77,16 +77,16 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         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("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
+        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
@@ -101,7 +101,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         if not task_id or question_text is None:
             print(f"Skipping item with missing task_id or question: {item}")
             continue
-        
+
         # Get task file if any
         question_file = item.get("file_name", "")
         try:
@@ -113,12 +113,26 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         question_file = str(file_path) if file_path is not None else ""
         try:
             submitted_answer = run_agent(question_text, question_file)
-            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})
+            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}"})
-        
+            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}",
+                }
+            )
+
         # Clean files
         if file_path is not None:
             os.remove(file_path)
@@ -127,8 +141,12 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         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}
+    # 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)
 
@@ -198,20 +216,19 @@ with gr.Blocks() as demo:
 
     run_button = gr.Button("Run Evaluation & Submit All Answers")
 
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+    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]
-    )
+    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
 
 if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
+    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
+    space_id_startup = os.getenv("SPACE_ID")  # Get SPACE_ID at startup
 
     if space_host_startup:
         print(f"✅ SPACE_HOST found: {space_host_startup}")
@@ -219,14 +236,18 @@ if __name__ == "__main__":
     else:
         print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
 
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
+    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")
+        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.")
+        print(
+            "ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined."
+        )
 
-    print("-"*(60 + len(" App Starting ")) + "\n")
+    print("-" * (60 + len(" App Starting ")) + "\n")
 
     print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch(debug=True, share=False)

tools.py

@@ -1,18 +1,16 @@
-# Tools extracted from one of leader repos > https://huggingface.co/spaces/baixianger/RobotPai/blob/main/agent.py
-
-from langchain_tavily import TavilySearch
-from langchain_community.document_loaders import WikipediaLoader
+from google import genai
 from langchain_community.document_loaders import ArxivLoader
+from langchain_community.document_loaders import WikipediaLoader
 from langchain_community.vectorstores import SupabaseVectorStore
 from langchain_core.messages import SystemMessage, HumanMessage
+from langchain_core.messages import ToolMessage
 from langchain_core.tools import tool
+from langchain_tavily import TavilySearch
 from langchain.tools.retriever import create_retriever_tool
-from langchain_core.messages import ToolMessage
-from urllib.parse import urlparse
-from typing import Dict
 from markitdown import MarkItDown
 from pathlib import Path
-from google import genai
+from typing import Dict
+from urllib.parse import urlparse
 import os
 
 
@@ -75,12 +73,12 @@ def modulus(a: int, b: int) -> int:
 
 @tool(parse_docstring=True)
 def wiki_search(query: str) -> Dict[str, list]:
-    """Search Wikipedia for a query and return maximum 2 results.
+    """Search Wikipedia for a query and return maximum 3 results.
 
     Args:
         query: The search query.
     """
-    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
+    search_docs = WikipediaLoader(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}\n</Document>'
@@ -97,7 +95,7 @@ def web_search(query: str) -> ToolMessage:
     Args:
         query: The search query.
     """
-    return TavilySearch(max_results=4, include_images=False).invoke({"query": query})
+    return TavilySearch(max_results=5, include_images=False).invoke({"query": query})
 
 
 @tool(parse_docstring=True)