Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""app.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1eWv35WwsifbbT9K-FV8p8S_FAEekBnLz
+"""
+
+import streamlit as st
+import os
+import pandas as pd
+from together import Together
+from utils.helper import *
+
+
+st.set_page_config(layout="wide")
+st.title("Duel Agent Simulation 🦙🦙")
+
+
+with st.sidebar:
+    with st.expander("Instruction Manual"):
+        st.markdown("""
+            # 🦙🦙 Duel Agent Simulation Streamlit App
+
+            ## Overview
+
+            Welcome to the **Duel Agent Simulation** app! This Streamlit application allows you to chat with Meta's Llama3 model in a unique interview simulation. The app features two agents in an interview scenario, with a judge providing feedback. The best part? You simply provide a topic, and the simulation runs itself!
+
+            ## Features
+
+            ### 📝 Instruction Manual
+
+            **Meta Llama3 🦙 Chatbot**
+
+            This application lets you interact with Meta's Llama3 model through a fun interview-style chat.
+
+            **How to Use:**
+            1. **Input:** Type a topic into the input box labeled "Enter a topic".
+            2. **Submit:** Press the "Submit" button to start the simulation.
+            3. **Chat History:** View the previous conversations as the simulation unfolds.
+
+            **Credits:**
+            - **Developer:** Yiqiao Yin
+               - [Site](https://www.y-yin.io/)
+               - [LinkedIn](https://www.linkedin.com/in/yiqiaoyin/)
+               - [YouTube](https://youtube.com/YiqiaoYin/)
+        """)
+
+    # Text input
+    user_topic = st.text_input("Enter a topic", "Data Science")
+
+    # Add a button to submit
+    submit_button = st.button("Run Simulation!")
+
+    # Add a button to clear the session state
+    if st.button("Clear Session"):
+        st.session_state.messages = []
+        st.experimental_rerun()
+
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+
+# Create agents
+interviewer = call_llama
+interviewee = call_llama
+judge = call_llama
+
+
+# React to user input
+iter = 0
+list_of_iters = []
+list_of_questions = []
+list_of_answers = []
+list_of_judge_comments = []
+list_of_passes = []
+if submit_button:
+
+    # Initiatization
+    prompt = f"Ask a question about this topic: {user_topic}"
+
+    # Display user message in chat message container
+    # Default: user=interviewee, assistant=interviewer
+    st.chat_message("user").markdown(prompt)
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    while True:
+
+        # Interview asks a question
+        question = interviewer(prompt)
+
+        # Display assistant response in chat message container
+        st.chat_message("assistant").markdown(question)
+        st.session_state.messages.append({"role": "assistant", "content": question})
+
+        # Interviewee attempts an answer
+        if iter < 5:
+            answer = interviewee(
+                f"""
+                    Answer the question: {question} in a mediocre way
+                    Because you are an inexperienced interviewee.
+                """
+            )
+            st.chat_message("user").markdown(answer)
+            st.session_state.messages.append({"role": "user", "content": answer})
+        else:
+            answer = interviewee(
+                f"""
+                    Answer the question: {question} in a mediocre way
+                    Because you are an inexperienced interviewee but you really want to learn,
+                    so you learn from the judge comments: {judge_comments}
+                """
+            )
+            st.chat_message("user").markdown(answer)
+            st.session_state.messages.append({"role": "user", "content": answer})
+
+        # Judge thinks and advises but the thoughts are hidden
+        judge_comments = judge(
+            f"""
+                The question is: {question}
+                The answer is: {answer}
+                Provide feedback and rate the answer from 1 to 10 while 10 being the best and 1 is the worst.
+            """
+        )
+
+
+        # Collect all responses
+        passed_or_not = 1 if '8' in judge_comments else 0
+        list_of_iters.append(iter)
+        list_of_questions.append(question)
+        list_of_answers.append(answer)
+        list_of_judge_comments.append(judge_comments)
+        list_of_passes.append(passed_or_not)
+        results_tab = pd.DataFrame({
+            "Iter.": list_of_iters,
+            "Questions": list_of_questions,
+            "Answers": list_of_answers,
+            "Judge Comments": list_of_judge_comments,
+            "Passed": list_of_passes
+        })
+
+        with st.expander("See explanation"):
+            st.table(results_tab)
+
+        # Stopping rule
+        if '8' in judge_comments:
+            break
+
+        # Checkpoint
+        iter += 1