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| # app.py (Final version) | |
| import os | |
| import gradio as gr | |
| import requests | |
| import pandas as pd | |
| import base64 | |
| import json | |
| import operator | |
| from typing import Annotated, List, TypedDict | |
| from dotenv import load_dotenv | |
| from langchain_community.tools.tavily_search import TavilySearchResults | |
| from langchain_core.messages import BaseMessage, HumanMessage, AIMessage | |
| from langchain_core.prompts import ChatPromptTemplate | |
| from langchain_core.tools import tool | |
| from langchain_google_genai import ChatGoogleGenerativeAI | |
| from langgraph.graph import END, StateGraph | |
| from langgraph.prebuilt import ToolNode | |
| API_BASE_URL = "https://agents-course-unit4-scoring.hf.space" | |
| class GaiaLangGraphAgent: | |
| def __init__(self): | |
| print("Initializing GaiaLangGraphAgent...") | |
| load_dotenv() | |
| class AgentState(TypedDict): | |
| question: str | |
| intermediate_steps: Annotated[List[BaseMessage], operator.add] | |
| self.AgentState = AgentState | |
| web_search_tool = TavilySearchResults(max_results=4) | |
| def calculator(expression: str) -> str: | |
| """Evaluates a simple mathematical expression.""" | |
| try: | |
| import numexpr | |
| return str(numexpr.evaluate(expression).item()) | |
| except Exception as e: return f"Error: {e}" | |
| llm_vision = ChatGoogleGenerativeAI(model="gemini-1.5-pro-latest") | |
| def get_file_path(file_name: str) -> str: | |
| if not os.path.exists("task_files"): os.makedirs("task_files") | |
| return os.path.join("task_files", file_name) | |
| def file_reader(file_name: str) -> str: | |
| """Reads a file, downloading if necessary. Handles text and images.""" | |
| local_path = get_file_path(file_name) | |
| if not os.path.exists(local_path): | |
| download_url = f"{API_BASE_URL}/files/{file_name}" | |
| print(f"Downloading: {download_url}") | |
| try: | |
| response = requests.get(download_url); response.raise_for_status() | |
| with open(local_path, "wb") as f: f.write(response.content) | |
| except Exception as e: return f"Error downloading {file_name}: {e}" | |
| try: | |
| if any(file_name.lower().endswith(ext) for ext in ['.png', '.jpg', '.jpeg', '.webp']): | |
| with open(local_path, "rb") as image_file: b64_image = base64.b64encode(image_file.read()).decode('utf-8') | |
| vision_prompt = HumanMessage(content=[ | |
| {"type": "text", "text": "Describe this image in detail, focusing on text or identifiable objects."}, | |
| {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{b64_image}"}} | |
| ]) | |
| return llm_vision.invoke([vision_prompt]).content | |
| else: | |
| with open(local_path, 'r', encoding='utf-8') as f: return f.read() | |
| except Exception as e: return f"Error processing {file_name}: {e}" | |
| tools = [web_search_tool, file_reader, calculator] | |
| llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash-latest", temperature=0, convert_system_message_to_human=True) | |
| llm_with_tools = llm.bind_tools(tools) | |
| planner_prompt = ChatPromptTemplate.from_messages([ | |
| ("system", """You are a world-class AI assistant. | |
| **Principles:** 1. Analyze the question for nuances. 2. Create multi-step plans. 3. Use tools intelligently (search, file read, calculator) or solve logic puzzles directly. 4. Provide exact-match answers. | |
| **Execution:** Loop through plan->act cycles until you have the final answer."""), | |
| ("human", "{question}\n\n{intermediate_steps}"), | |
| ]) | |
| def planner_node(state: AgentState): | |
| print("\n---PLANNER---") | |
| chain = planner_prompt | llm_with_tools | |
| response = chain.invoke(state) | |
| print(f"Planner decision: {'Tool call' if response.tool_calls else 'Final Answer'}") | |
| return {'intermediate_steps': [response]} | |
| tool_node = ToolNode(tools) | |
| def should_continue(state: AgentState): | |
| last_message = state['intermediate_steps'][-1] | |
| if isinstance(last_message, AIMessage): | |
| if len(getattr(last_message, "tool_calls", [])) > 0: return "action" | |
| return END | |
| workflow = StateGraph(AgentState) | |
| workflow.add_node("planner", planner_node) | |
| workflow.add_node("action", tool_node) | |
| workflow.set_entry_point("planner") | |
| workflow.add_conditional_edges("planner", should_continue) | |
| workflow.add_edge("action", "planner") | |
| self.app = workflow.compile() | |
| print("GaiaLangGraphAgent initialized successfully.") | |
| def __call__(self, question: str) -> str: | |
| print(f"\n>>>>>> AGENT EXECUTING FOR QUESTION: {question[:70]}...") | |
| initial_state = {"question": question, "intermediate_steps": []} | |
| final_state = self.app.invoke(initial_state, config={"recursion_limit": 15}) | |
| final_answer = final_state["intermediate_steps"][-1].content | |
| print(f"<<<<<< AGENT FINISHED. FINAL ANSWER: {final_answer}") | |
| return final_answer | |
| def run_and_submit_all(profile: gr.OAuthProfile | None): | |
| if not profile: return "Please Login to Hugging Face with the button first.", None | |
| space_id = os.getenv("SPACE_ID") | |
| if not space_id: return "CRITICAL ERROR: SPACE_ID not found. Run this from a deployed Hugging Face Space.", None | |
| username = profile.username | |
| print(f"User logged in: {username}") | |
| questions_url = f"{API_BASE_URL}/questions" | |
| submit_url = f"{API_BASE_URL}/submit" | |
| try: | |
| agent = GaiaLangGraphAgent() | |
| except Exception as e: return f"Error initializing agent: {e}", None | |
| agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" | |
| print(f"Fetching questions from: {questions_url}") | |
| try: | |
| response = requests.get(questions_url, timeout=20); response.raise_for_status() | |
| questions_data = response.json() | |
| except Exception as e: return f"Error fetching questions: {e}", None | |
| results_log, answers_payload = [], [] | |
| print(f"Running agent on {len(questions_data)} questions. This may take several minutes...") | |
| for item in questions_data: | |
| task_id, question_text = item.get("task_id"), item.get("question") | |
| 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: | |
| results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"}) | |
| submission_data = {"username": username, "agent_code": agent_code, "answers": answers_payload} | |
| print(f"Submitting {len(answers_payload)} answers...") | |
| try: | |
| response = requests.post(submit_url, json=submission_data, timeout=60); response.raise_for_status() | |
| result_data = response.json() | |
| final_status = (f"Submission Successful!\nUser: {result_data.get('username')}\n" | |
| f"Score: {result_data.get('score', 'N/A')}% " | |
| f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)") | |
| return final_status, pd.DataFrame(results_log) | |
| except Exception as e: return f"Submission Failed: {e}", pd.DataFrame(results_log) | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# GAIA - Advanced Agent Runner") | |
| gr.Markdown("Log in and click 'Run' to evaluate the agent.") | |
| gr.LoginButton() | |
| run_button = gr.Button("Run Evaluation & Submit All Answers") | |
| status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False) | |
| 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("Launching Gradio Interface...") | |
| demo.launch() |