api_key = "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" import streamlit as st from langchain_groq import ChatGroq from langchain_community.utilities import ArxivAPIWrapper, WikipediaAPIWrapper from langchain_community.tools import ArxivQueryRun, WikipediaQueryRun, DuckDuckGoSearchRun from langchain.agents import initialize_agent, AgentType import os import requests import pandas as pd from dotenv import load_dotenv # Load environment variables load_dotenv() # Constants for Basic Agent Evaluation DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" # Initialize search tools (with warm-up) @st.cache_resource def load_tools(): with st.spinner("Initializing tools (first time may take a few seconds)..."): api_wrapper_arxiv = ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=250) arxiv = ArxivQueryRun(api_wrapper=api_wrapper_arxiv) api_wrapper_wiki = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=250) wiki = WikipediaQueryRun(api_wrapper=api_wrapper_wiki) search = DuckDuckGoSearchRun(name="Search") # Warm up tools arxiv.run("machine learning") wiki.run("machine learning") return [search, arxiv, wiki] tools = load_tools() # Streamlit app layout st.title("Langchain - Chat with Search & Evaluation") # Sidebar for settings st.sidebar.title("Settings") api_key = st.sidebar.text_input("Enter your Groq API Key:", type="password") # Initialize chat messages if "messages" not in st.session_state: st.session_state["messages"] = [ {"role": "assistant", "content": "Hi, I am a Chatbot who can search the web and evaluate questions. How can I help you?"} ] # Display chat messages for msg in st.session_state.messages: st.chat_message(msg["role"]).write(msg["content"]) # Chat input if prompt := st.chat_input(placeholder="What is machine learning?"): st.session_state.messages.append({"role": "user", "content": prompt}) st.chat_message("user").write(prompt) if not api_key: st.error("Please enter your Groq API key in the sidebar.") st.stop() llm = ChatGroq(groq_api_key=api_key, model_name="llama3-70b-8192") search_agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, handle_parsing_errors=True) with st.chat_message("assistant"): response = search_agent.run(st.session_state.messages) st.session_state.messages.append({'role': 'assistant', "content": response}) st.write(response) # Basic Agent Evaluation Section st.sidebar.title("Basic Agent Evaluation") def run_evaluation(): """Function to run the evaluation with progress updates""" if not api_key: st.error("Please enter your Groq API key in the sidebar.") return "API key required", pd.DataFrame() # Setup progress tracking progress_bar = st.sidebar.progress(0) status_text = st.sidebar.empty() results_container = st.empty() username = "streamlit_user" api_url = DEFAULT_API_URL questions_url = f"{api_url}/questions" submit_url = f"{api_url}/submit" space_id = os.getenv("SPACE_ID", "local") agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id != "local" else "local_execution" try: # 1. Fetch Questions status_text.text("📡 Fetching questions...") response = requests.get(questions_url, timeout=15) response.raise_for_status() questions_data = response.json() total_questions = len(questions_data) status_text.text(f"✅ Found {total_questions} questions") if not questions_data: return "No questions found", pd.DataFrame() # 2. Initialize Agent (reuse tools from cache) llm = ChatGroq(groq_api_key=api_key, model_name="llama3-70b-8192") agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, handle_parsing_errors=True) # 3. Process Questions results_log = [] answers_payload = [] for i, item in enumerate(questions_data): progress = (i + 1) / total_questions progress_bar.progress(progress) status_text.text(f"🔍 Processing question {i+1}/{total_questions}...") task_id = item.get("task_id") question_text = item.get("question") if not task_id or not question_text: continue try: submitted_answer = agent.run(question_text) answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer}) results_log.append({"Task ID": task_id, "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text, "Submitted Answer": submitted_answer[:200] + "..." if len(submitted_answer) > 200 else submitted_answer}) # Update results table progressively if (i + 1) % 3 == 0 or (i + 1) == total_questions: # Update every 3 questions or at end results_container.dataframe(pd.DataFrame(results_log)) except Exception as e: results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"❌ Error: {str(e)}"}) # 4. Submit Answers status_text.text("📤 Submitting answers...") submission_data = {"username": username, "agent_code": agent_code, "answers": answers_payload} 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"📊 Score: {result_data.get('score', 'N/A')}%\n" f"📝 Correct: {result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')}\n" f"💬 Message: {result_data.get('message', 'No message')}" ) return final_status, pd.DataFrame(results_log) except Exception as e: return f"❌ Failed: {str(e)}", pd.DataFrame(results_log if 'results_log' in locals() else []) finally: progress_bar.empty() status_text.empty() # Evaluation button in sidebar if st.sidebar.button("🚀 Run Evaluation & Submit Answers"): with st.spinner("Starting evaluation..."): status, results = run_evaluation() st.sidebar.success("Evaluation completed!") st.sidebar.text_area("Results", value=status, height=150) if not results.empty: st.subheader("📋 Detailed Results") st.dataframe(results, use_container_width=True)