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| Final_Assignment_Template\app.py | |
| import os | |
| import gradio as gr | |
| import requests | |
| import inspect | |
| import pandas as pd | |
| from agent import build_graph | |
| # (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.graph = build_graph() | |
| def __call__(self, question: str) -> str: | |
| print(f"Agent received question (first 50 chars): {question[:50]}...") | |
| # Wrap the question in a HumanMessage from langchain_core | |
| messages = [HumanMessage(content=question)] | |
| messages = self.graph.invoke({"messages": messages}) | |
| answer = messages['messages'][-1].content | |
| return answer[14:] | |
| 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}") | |
| 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] | |
| ) | |
| 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?).") | |
| 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.") | |
| print("-"*(60 + len(" App Starting ")) + "\n") | |
| print("Launching Gradio Interface for Basic Agent Evaluation...") | |
| demo.launch(debug=True, share=False) | |
| Final_Assignment_Template\agent.py: | |
| import os | |
| import json | |
| from dotenv import load_dotenv | |
| from langchain_core.messages import HumanMessage | |
| load_dotenv() | |
| os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" | |
| hf_token = os.getenv("HUGGINGFACE_INFERENCE_TOKEN") | |
| from langgraph.graph import START, StateGraph, MessagesState | |
| from langgraph.prebuilt import tools_condition, ToolNode | |
| from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings | |
| from langchain_google_genai import ChatGoogleGenerativeAI | |
| from langchain_community.tools.tavily_search import TavilySearchResults | |
| from langchain_community.document_loaders import WikipediaLoader, ArxivLoader | |
| from langchain_community.vectorstores import Chroma | |
| from langchain_core.messages import SystemMessage, HumanMessage | |
| from langchain_core.tools import tool | |
| from langchain.schema import Document | |
| # ---- Tool Definitions (with docstrings) ---- | |
| @tool | |
| def multiply(a: int, b: int) -> int: | |
| """Multiply two integers and return the result.""" | |
| return a * b | |
| @tool | |
| def add(a: int, b: int) -> int: | |
| """Add two integers and return the result.""" | |
| return a + b | |
| @tool | |
| def subtract(a: int, b: int) -> int: | |
| """Subtract second integer from the first and return the result.""" | |
| return a - b | |
| @tool | |
| def divide(a: int, b: int) -> float: | |
| """Divide first integer by second and return the result as a float.""" | |
| if b == 0: | |
| raise ValueError("Cannot divide by zero.") | |
| return a / b | |
| @tool | |
| def modulus(a: int, b: int) -> int: | |
| """Return the remainder when first integer is divided by second.""" | |
| return a % b | |
| @tool | |
| def wiki_search(query: str) -> str: | |
| """Search Wikipedia for the query and return text of up to 2 documents.""" | |
| search_docs = WikipediaLoader(query=query, load_max_docs=2).load() | |
| formatted = "\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} | |
| @tool | |
| def web_search(query: str) -> str: | |
| """Search the web for the query using Tavily and return up to 3 results.""" | |
| search_docs = TavilySearchResults(max_results=3).invoke(query=query) | |
| formatted = "\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 {"web_results": formatted} | |
| @tool | |
| def arvix_search(query: str) -> str: | |
| """Search Arxiv for the query and return content from up to 3 papers.""" | |
| search_docs = ArxivLoader(query=query, load_max_docs=3).load() | |
| formatted = "\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} | |
| # Build vector store once | |
| embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") | |
| json_QA = [json.loads(line) for line in open("metadata.jsonl", "r")] | |
| documents = [ | |
| Document( | |
| page_content=f"Question : {sample['Question']}\n\nFinal answer : {sample['Final answer']}", | |
| metadata={"source": sample["task_id"]} | |
| ) for sample in json_QA | |
| ] | |
| vector_store = Chroma.from_documents( | |
| documents=documents, | |
| embedding=embeddings, | |
| persist_directory="./chroma_db", | |
| collection_name="my_collection" | |
| ) | |
| print("Documents inserted:", vector_store._collection.count()) | |
| @tool | |
| def similar_question_search(query: str) -> str: | |
| """Search for questions similar to the input query using the vector store.""" | |
| matched_docs = vector_store.similarity_search(query, 3) | |
| formatted = "\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 matched_docs | |
| ) | |
| return {"similar_questions": formatted} | |
| # ---- System Prompt ---- | |
| system_prompt = """ | |
| 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... | |
| """ | |
| sys_msg = SystemMessage(content=system_prompt) | |
| tools = [ | |
| multiply, add, subtract, divide, modulus, | |
| wiki_search, web_search, arvix_search, similar_question_search | |
| ] | |
| # ---- Graph Builder ---- | |
| def build_graph(provider: str = "huggingface"): | |
| if provider == "huggingface": | |
| llm = ChatHuggingFace( | |
| llm=HuggingFaceEndpoint( | |
| repo_id="mosaicml/mpt-30b", | |
| temperature=0, | |
| huggingfacehub_api_token=hf_token | |
| ) | |
| ) | |
| elif provider == "google": | |
| llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0) | |
| else: | |
| raise ValueError("Invalid provider: choose 'huggingface' or 'google'.") | |
| llm_with_tools = llm.bind_tools(tools) | |
| def assistant(state: MessagesState): | |
| return {"messages": [llm_with_tools.invoke(state["messages"])]} | |
| def retriever(state: MessagesState): | |
| similar = vector_store.similarity_search(state["messages"][0].content) | |
| if similar: | |
| example_msg = HumanMessage(content=f"Here is a similar question:\n\n{similar[0].page_content}") | |
| return {"messages": [sys_msg] + state["messages"] + [example_msg]} | |
| return {"messages": [sys_msg] + state["messages"]} | |
| builder = StateGraph(MessagesState) | |
| builder.add_node("retriever", retriever) | |
| builder.add_node("assistant", assistant) | |
| builder.add_node("tools", ToolNode(tools)) | |
| builder.add_edge(START, "retriever") | |
| builder.add_edge("retriever", "assistant") | |
| builder.add_conditional_edges("assistant", tools_condition) | |
| builder.add_edge("tools", "assistant") | |
| return builder.compile() | |
| Final_Assignment_Template\metadata.jsonl: | |
| Final_Assignment_Template\requirements.txt: | |
| gradio | |
| requests | |
| langchain | |
| langchain-community | |
| langchain-core | |
| langchain-google-genai | |
| langchain-huggingface | |
| langchain-groq | |
| langchain-tavily | |
| langchain-chroma | |
| langgraph | |
| sentence-transformers | |
| huggingface_hub | |
| supabase | |
| arxiv | |
| pymupdf | |
| wikipedia | |
| pgvector | |
| python-dotenv | |
| protobuf==3.20.3 | |
| Final_Assignment_Template\system_prompt.txt: | |
| 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 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. | |
| Your answer should only start with "FINAL ANSWER: ", then follows with the answer. |