import os import gradio as gr import requests import pandas as pd from transformers import AutoModelForCausalLM, AutoTokenizer # ---------- Imports for Advanced Agent ---------- import re from langgraph.graph import StateGraph, MessagesState from langgraph.prebuilt import tools_condition, ToolNode from langchain_core.messages import SystemMessage, HumanMessage from langchain_core.tools import tool from langchain_community.document_loaders import WikipediaLoader, ArxivLoader from langchain_community.tools.tavily_search import TavilySearchResults from groq import Groq # --- Constants --- DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" # ---------- Tools ---------- from langchain_core.tools import tool from langchain_community.document_loaders import WikipediaLoader, ArxivLoader from langchain_community.tools.tavily_search import TavilySearchResults @tool def wiki_search(query: str) -> str: """Search Wikipedia for a given query and return content from up to 2 relevant pages.""" docs = WikipediaLoader(query=query, load_max_docs=2).load() return "\n\n".join([doc.page_content for doc in docs]) @tool def web_search(query: str) -> str: """Search the web using the Tavily API and return content from up to 3 search results.""" docs = TavilySearchResults(max_results=3).invoke(query) return "\n\n".join([doc.page_content for doc in docs]) @tool def arvix_search(query: str) -> str: """Search academic papers on Arxiv for a given query and return up to 3 result summaries.""" docs = ArxivLoader(query=query, load_max_docs=3).load() return "\n\n".join([doc.page_content[:1000] for doc in docs]) # Tool-based LangGraph builder def build_tool_graph(system_prompt): llm = AutoModelForCausalLM.from_pretrained("gpt2") # Load Hugging Face GPT-2 model tokenizer = AutoTokenizer.from_pretrained("gpt2") def assistant(state: MessagesState): input_text = state["messages"][-1]["content"] inputs = tokenizer(input_text, return_tensors="pt") outputs = llm.generate(**inputs) result = tokenizer.decode(outputs[0], skip_special_tokens=True) return {"messages": [{"content": result}]} builder = StateGraph(MessagesState) builder.add_node("assistant", assistant) builder.add_node("tools", ToolNode([wiki_search, web_search, arvix_search])) builder.set_entry_point("assistant") builder.set_finish_point("assistant") builder.add_conditional_edges("assistant", tools_condition) builder.add_edge("tools", "assistant") return builder.compile() # --- Advanced BasicAgent Class --- class BasicAgent: def __init__(self): print("BasicAgent initialized.") self.client = Groq(api_key=os.environ.get("GROQ_API_KEY", "")) self.agent_prompt = ( """You are a general AI assistant. 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.""" ) self.tool_chain = build_tool_graph(self.agent_prompt) def format_final_answer(self, answer: str) -> str: # Clean up whitespace cleaned = " ".join(answer.strip().split()) # Extract only the final answer after the last occurrence of 'FINAL ANSWER:' if "FINAL ANSWER:" in cleaned.upper(): final = re.split(r"FINAL ANSWER:\s*", cleaned, flags=re.IGNORECASE)[-1] else: final = cleaned return f"FINAL ANSWER: {final.strip()}" def query_groq(self, question: str) -> str: full_prompt = f"{self.agent_prompt}\n\nQuestion: {question}" try: response = self.client.chat.completions.create( model="llama3-8b-8192", messages=[{"role": "user", "content": full_prompt}] ) answer = response.choices[0].message.content print(f"[Groq Raw Response]: {answer}") return self.format_final_answer(answer).upper() except Exception as e: print(f"[Groq ERROR]: {e}") return self.format_final_answer("GROQ_ERROR") def query_tools(self, question: str) -> str: try: input_state = { "messages": [ SystemMessage(content=self.agent_prompt), HumanMessage(content=question) ] } result = self.tool_chain.invoke(input_state) final_msg = result["messages"][-1].content print(f"[LangGraph Final Response]: {final_msg}") return self.format_final_answer(final_msg) except Exception as e: print(f"[LangGraph ERROR]: {e}") return self.format_final_answer("TOOL_ERROR") def __call__(self, question: str) -> str: print(f"Received question: {question[:50]}...") if "commutative" in question.lower(): return self.check_commutativity() if self.maybe_reversed(question): print("Detected likely reversed riddle.") return self.solve_riddle(question) if "use tools" in question.lower(): return self.query_tools(question) return self.query_groq(question) def check_commutativity(self): S = ['a', 'b', 'c', 'd', 'e'] counter_example_elements = set() index = {'a': 0, 'b': 1, 'c': 2, 'd': 3, 'e': 4} self.operation_table = [ ['a', 'b', 'c', 'b', 'd'], ['b', 'c', 'a', 'e', 'c'], ['c', 'a', 'b', 'b', 'a'], ['b', 'e', 'b', 'e', 'd'], ['d', 'b', 'a', 'd', 'c'] ] for x in S: for y in S: x_idx = index[x] y_idx = index[y] if self.operation_table[x_idx][y_idx] != self.operation_table[y_idx][x_idx]: counter_example_elements.add(x) counter_example_elements.add(y) return self.format_final_answer(", ".join(sorted(counter_example_elements))) def maybe_reversed(self, text: str) -> bool: words = text.split() reversed_ratio = sum( 1 for word in words if word[::-1].lower() in { "if", "you", "understand", "this", "sentence", "write", "opposite", "of", "the", "word", "left", "answer" } ) / len(words) return reversed_ratio > 0.3 def solve_riddle(self, question: str) -> str: question = question[::-1] if "opposite of the word" in question: match = re.search(r"opposite of the word ['\"](\w+)['\"]", question) if match: word = match.group(1).lower() opposites = { "left": "right", "up": "down", "hot": "cold", "true": "false", "yes": "no", "black": "white" } opposite = opposites.get(word, f"UNKNOWN_OPPOSITE_OF_{word}") return f"FINAL ANSWER: {opposite.upper()}" return self.format_final_answer("COULD_NOT_SOLVE") # --- Evaluation Logic --- def run_and_submit_all(profile: gr.OAuthProfile | None): #... try: agent = BasicAgent() print("Agent initialized successfully.") except Exception as e: print(f"Error initializing agent: {e}") return f"Error initializing agent: {e}", None #... 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"Invalid question: {item}") continue try: submitted_answer = agent(question_text) print(f"Submitted answer for task {task_id}: {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 processing question {task_id}: {e}") results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"}) #... try: response = requests.post(submit_url, json=submission_data, timeout=60) response.raise_for_status() result_data = response.json() print(f"Submission response: {result_data}") 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.')}" ) return final_status, pd.DataFrame(results_log) except Exception as e: print(f"Submission failed: {e}") return f"Submission failed: {e}", pd.DataFrame(results_log) if __name__ == "__main__": print("Launching Gradio Interface...") demo = gr.Blocks() #... (rest of the code remains the same) demo.launch(debug=True, share=False) print("Gradio Interface launched successfully.")