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