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| import streamlit as st | |
| import pandas as pd | |
| import os | |
| from huggingface_hub import InferenceClient | |
| # Initialize HF Inference client | |
| def get_client(): | |
| # Try to get token from environment variables (works in Hugging Face Spaces) | |
| hf_token = os.environ.get("HF_TOKEN") | |
| if not hf_token: | |
| try: | |
| hf_token = st.secrets["HF_TOKEN"] # Fallback to Streamlit secrets | |
| except: | |
| st.error("HF_TOKEN not found. Please set it in your Space's settings or secrets.toml") | |
| st.stop() | |
| return InferenceClient(token=hf_token) | |
| client = get_client() | |
| # Define the LLM call function | |
| def call_llama(prompt, model="mistralai/Mistral-7B-Instruct-v0.2", max_tokens=500): | |
| try: | |
| response = client.text_generation( | |
| prompt=prompt, | |
| model=model, | |
| max_new_tokens=max_tokens, | |
| temperature=0.7 | |
| ) | |
| return response | |
| except Exception as e: | |
| st.error(f"Error calling LLM: {e}") | |
| return "Sorry, I encountered an error." | |
| # Set page config | |
| st.set_page_config(layout="wide") | |
| st.title("Duel Agent Simulation 🦙🦙") | |
| # Sidebar setup | |
| with st.sidebar: | |
| with st.expander("Instruction Manual"): | |
| st.markdown(""" | |
| # 🦙🦙 Duel Agent Simulation | |
| ## Overview | |
| This app simulates an interview with two AI agents: | |
| 1. **Interviewer**: Asks questions about your topic | |
| 2. **Interviewee**: Attempts to answer (poorly at first) | |
| 3. **Judge**: Provides feedback after each answer | |
| ## How to Use | |
| 1. Enter a topic below | |
| 2. Click "Run Simulation" | |
| 3. Watch the conversation unfold | |
| 4. See the evaluation results | |
| The simulation stops when the interviewee gives a good answer (8/10 or higher). | |
| """) | |
| # User inputs | |
| user_topic = st.text_input("Enter a topic", "Artificial Intelligence") | |
| submit_button = st.button("Run Simulation!") | |
| if st.button("Clear Session"): | |
| st.session_state.clear() | |
| st.rerun() | |
| # Initialize session state | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| if "simulation_data" not in st.session_state: | |
| st.session_state.simulation_data = { | |
| "iterations": [], | |
| "questions": [], | |
| "answers": [], | |
| "feedback": [], | |
| "scores": [] | |
| } | |
| # Display chat history | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| # Run simulation when button is pressed | |
| if submit_button: | |
| iter_count = 0 | |
| current_prompt = f"Ask a technical interview question about: {user_topic}" | |
| # Display initial topic | |
| with st.chat_message("user"): | |
| st.markdown(f"**Topic:** {user_topic}") | |
| st.session_state.messages.append({"role": "user", "content": f"Topic: {user_topic}"}) | |
| with st.spinner("Running simulation..."): | |
| while iter_count < 6: # Max 6 iterations | |
| # Interviewer asks question | |
| question = call_llama( | |
| f"""You are a technical interviewer. Ask one specific question about {user_topic}. | |
| Make it challenging but answerable. Return only the question.""" | |
| ).strip() | |
| with st.chat_message("assistant"): | |
| st.markdown(f"**Interviewer:** {question}") | |
| st.session_state.messages.append({"role": "assistant", "content": f"Interviewer: {question}"}) | |
| # Interviewee answers | |
| if iter_count < 2: # First attempts are poor | |
| answer_prompt = f"""You are a nervous interviewee. Give a mediocre answer to: | |
| "{question}". Return only the answer.""" | |
| else: # Later attempts improve | |
| feedback = st.session_state.simulation_data["feedback"][-1] if iter_count > 0 else "" | |
| answer_prompt = f"""You're learning to answer better. Previous feedback was: | |
| "{feedback}". Now answer: "{question}". Return only the improved answer.""" | |
| answer = call_llama(answer_prompt).strip() | |
| with st.chat_message("user"): | |
| st.markdown(f"**Interviewee:** {answer}") | |
| st.session_state.messages.append({"role": "user", "content": f"Interviewee: {answer}"}) | |
| # Judge evaluates | |
| feedback_prompt = f"""Evaluate this interview exchange: | |
| Question: {question} | |
| Answer: {answer} | |
| Provide specific feedback and a score from 1-10 (10=best). | |
| Format: Feedback: [your feedback] Score: [1-10]""" | |
| judge_response = call_llama(feedback_prompt).strip() | |
| # Extract score | |
| score = 5 # default | |
| if "Score:" in judge_response: | |
| try: | |
| score_part = judge_response.split("Score:")[1].strip() | |
| score = int(score_part.split()[0]) | |
| except (ValueError, IndexError): | |
| pass | |
| # Store data | |
| st.session_state.simulation_data["iterations"].append(iter_count) | |
| st.session_state.simulation_data["questions"].append(question) | |
| st.session_state.simulation_data["answers"].append(answer) | |
| st.session_state.simulation_data["feedback"].append(judge_response) | |
| st.session_state.simulation_data["scores"].append(score) | |
| # Show feedback | |
| with st.chat_message("assistant"): | |
| st.markdown(f"**Judge:** {judge_response}") | |
| st.session_state.messages.append({"role": "assistant", "content": f"Judge: {judge_response}"}) | |
| # Display results table | |
| with st.expander("Detailed Results"): | |
| results_df = pd.DataFrame({ | |
| "Round": st.session_state.simulation_data["iterations"], | |
| "Question": st.session_state.simulation_data["questions"], | |
| "Answer": st.session_state.simulation_data["answers"], | |
| "Score": st.session_state.simulation_data["scores"], | |
| "Feedback": st.session_state.simulation_data["feedback"] | |
| }) | |
| st.dataframe(results_df, use_container_width=True) | |
| # Check stopping condition | |
| if score >= 8: | |
| st.success("🎉 Simulation complete! The interviewee passed with a good answer.") | |
| break | |
| iter_count += 1 | |
| current_prompt = f"Ask a follow-up question about: {user_topic}" | |
| if iter_count == 6: | |
| st.warning("Simulation ended - maximum rounds reached") |