updated

.streamlit/config.toml

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+[theme]
+primaryColor = "#FF4B4B"
+backgroundColor = "#FFFFFF"
+secondaryBackgroundColor = "#F0F2F6"
+textColor = "#262730"
+font = "sans serif"
+
+[server]
+enableXsrfProtection = true
+enableCORS = false

.vscode/settings.json

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+{
+    "window.title": "${dirty}${activeEditorShort}${separator}${rootName}${separator}${profileName}${separator}${appName}${separator}[Branch: main]"
+}

README.md

@@ -10,4 +10,44 @@ pinned: false
 short_description: Model workflow using agents to proactively develop context
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Context Extraction Demo
+
+[![Made with Streamlit](https://img.shields.io/badge/Made%20with-Streamlit-FF4B4B.svg)](https://www.streamlit.io)
+[![Python Version](https://img.shields.io/badge/python-3.8%2B-blue)](https://www.python.org)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+
+This project demonstrates an innovative approach to enhancing personalized Large Language Model (LLM) experiences through agentic workflow-based context extraction. The system showcases how AI agents can proactively generate and collect contextual data to improve the quality and relevance of LLM interactions.
+
+## Purpose
+
+The primary goal of this project is to illustrate how an agent-driven workflow can:
+- Proactively identify and extract relevant contextual information
+- Generate meaningful data that enhances LLM understanding
+- Create more personalized and context-aware AI interactions
+- Demonstrate practical implementation of agentic workflows in LLM systems
+
+## Screenshots
+
+![alt text](screenshots/1.png)
+
+![alt text](screenshots/2.png)
+
+![alt text](screenshots/3.png)
+
+## About
+
+This project was developed through collaboration between [Daniel Rosehill](https://danielrosehill.com) and Claude (Anthropic). It serves as a practical demonstration of how AI systems can be designed to actively participate in context generation and enhancement, leading to more effective and personalized LLM experiences.
+
+## Implementation
+
+The system implements an agentic workflow that enables:
+- Automated context extraction from user interactions
+- Proactive generation of contextual metadata
+- Integration of extracted context into LLM inference processes
+- Enhanced personalization through accumulated contextual understanding
+
+## Attribution
+
+Development: Claude (Anthropic)
+Project Direction and Implementation: Daniel Rosehill
+

app.py

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+import streamlit as st
+import openai
+import json
+from datetime import datetime
+
+# Page configuration
+st.set_page_config(
+    page_title="Agent Interview Context Generation Demo",
+    layout="wide"
+)
+
+# Custom CSS for chat bubbles and Font Awesome
+st.markdown("""
+<style>
+@import url('https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css');
+.chat-bubble {
+    padding: 15px;
+    border-radius: 15px;
+    margin: 5px 0;
+    max-width: 80%;
+    position: relative;
+}
+
+.bot-bubble {
+    background-color: #F0F2F6;
+    margin-right: auto;
+    margin-left: 10px;
+    border-bottom-left-radius: 5px;
+}
+
+.user-bubble {
+    background-color: #4CAF50;
+    color: white;
+    margin-left: auto;
+    margin-right: 10px;
+    border-bottom-right-radius: 5px;
+}
+
+.chat-container {
+    display: flex;
+    flex-direction: column;
+    gap: 10px;
+    padding: 10px;
+    background-color: white;
+    border-radius: 10px;
+    box-shadow: 0 2px 5px rgba(0,0,0,0.1);
+}
+
+.stTextArea textarea {
+    border-radius: 20px;
+    padding: 10px 15px;
+    font-size: 16px;
+}
+
+.stButton button {
+    border-radius: 20px;
+    padding: 5px 20px;
+}
+</style>
+""", unsafe_allow_html=True)
+
+# Initialize session state variables
+if 'messages' not in st.session_state:
+    st.session_state.messages = []
+if 'interview_complete' not in st.session_state:
+    st.session_state.interview_complete = False
+if 'context_data' not in st.session_state:
+    st.session_state.context_data = ""
+if 'interview_started' not in st.session_state:
+    st.session_state.interview_started = False
+if 'context_focus' not in st.session_state:
+    st.session_state.context_focus = None
+
+def get_random_question(api_key, focus_area=None):
+    """Get a random question from OpenAI based on optional focus area."""
+    try:
+        client = openai.OpenAI(api_key=api_key)
+        system_content = "You are an interviewer gathering context about the user. "
+        if focus_area and focus_area != "general":
+            system_content += f"Focus specifically on questions about their {focus_area}. "
+        system_content += "Ask one random, open-ended question that reveals meaningful information about the user. Be creative and never repeat questions. Each response should be just one engaging question."
+        
+        response = client.chat.completions.create(
+            model="gpt-3.5-turbo",
+            messages=[
+                {
+                    "role": "system",
+                    "content": system_content
+                },
+                {
+                    "role": "user",
+                    "content": "Please ask me a random question."
+                }
+            ]
+        )
+        return response.choices[0].message.content if hasattr(response.choices[0].message, 'content') else str(response.choices[0].message)
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+def extract_context(api_key, conversation):
+    """Extract context from the conversation using OpenAI."""
+    try:
+        client = openai.OpenAI(api_key=api_key)
+        conversation_text = "\n".join([f"{'Bot' if i%2==0 else 'User'}: {msg}" for i, msg in enumerate(conversation)])
+        
+        response = client.chat.completions.create(
+            model="gpt-3.5-turbo",
+            messages=[
+                {
+                    "role": "system",
+                    "content": "Analyze the following conversation and extract key information about the user. Create a well-organized summary in markdown format, grouping similar information under appropriate headings. Write in third person perspective."
+                },
+                {
+                    "role": "user",
+                    "content": f"Please analyze this conversation and create a context summary:\n\n{conversation_text}"
+                }
+            ]
+        )
+        return response.choices[0].message.content if hasattr(response.choices[0].message, 'content') else str(response.choices[0].message)
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Sidebar for API key and controls
+with st.sidebar:
+    st.title("Settings")
+    api_key = st.text_input("Enter OpenAI API Key", type="password")
+    
+    if st.button("Clear/Reset"):
+        st.session_state.messages = []
+        st.session_state.interview_complete = False
+        st.session_state.context_data = ""
+        st.session_state.interview_started = False
+        st.session_state.context_focus = None
+        st.rerun()
+
+# Main content
+st.title("Agent Interview Context Generation Demo")
+st.markdown("""
+This project demonstrates how AI agents can proactively gather and generate rich contextual data 
+through intelligent interviewing. By focusing on specific areas of interest, the agent builds a comprehensive 
+understanding that enhances AI-human interactions and enables more personalized experiences.
+""")
+
+# Create tabs for different sections
+tab1, tab2, tab3, tab4 = st.tabs(["Instructions", "Interview", "Gallery", "Generated Context"])
+
+with tab1:
+    st.header("How it Works")
+    st.markdown("""
+    This application helps gather and extract contextual information about you through an interactive interview process.
+    
+    Created by [Daniel Rosehill](https://danielrosehill.com) and Claude (Anthropic).
+    
+    View the source code on [GitHub](https://github.com/danielrosehill/Context-Extraction-Demo).
+    
+    ### Process:
+    1. Enter your OpenAI API key in the sidebar
+    2. Choose your preferred context focus area
+    3. Click the "Start Interview" button in the Interview tab
+    4. The AI interviewer will ask targeted questions based on your chosen focus
+    5. Answer each question naturally - you can type or use voice input
+    6. Click "Submit Answer" after each response
+    7. Continue the conversation until you're ready to end
+    8. Click "End Interview" to generate your context summary
+    9. Review the extracted context and export it as needed
+    
+    ### Features:
+    - **Focus Areas**: Choose to focus on specific aspects like professional background, technical skills, or keep it general
+    - **Voice Input**: Use Chrome's built-in speech-to-text by clicking the microphone icon
+    - **Targeted Questions**: The AI asks questions relevant to your chosen focus area
+    - **Context Extraction**: Automatically organizes your information into a structured summary
+    - **Export Options**: Copy or download your context data in markdown format
+    
+    ### Tips:
+    - Provide detailed, honest answers for better context extraction
+    - Use voice input to make the process faster and more natural
+    - Take your time with each response
+    - You can reset and start over at any time using the Clear/Reset button
+    """)
+
+with tab2:
+    # Create two columns for the main interface
+    col1, col2 = st.columns([2, 1])
+
+    with col1:
+        st.subheader("Conversation")
+        # Display conversation history with chat bubbles
+        st.markdown('<div class="chat-container">', unsafe_allow_html=True)
+        for msg in st.session_state.messages:
+            is_bot = msg.startswith('Q:')
+            bubble_class = "bot-bubble" if is_bot else "user-bubble"
+            message_content = msg[3:] if is_bot else msg
+            bot_icon = '<i class="fas fa-robot" style="margin-right: 8px;"></i>' if is_bot else ''
+            st.markdown(
+                f'<div class="chat-bubble {bubble_class}">{bot_icon}{message_content}</div>',
+                unsafe_allow_html=True
+            )
+        st.markdown('</div>', unsafe_allow_html=True)
+
+    with col2:
+        st.subheader("Your Response")
+        # Add microphone icon and voice input instructions
+        st.markdown("""
+        💡 **Voice Input Tip**: 
+        - Click the microphone icon in Chrome
+        - Or use built-in speech-to-text
+        """)
+        
+        # Show interview interface
+        if api_key:
+            if not st.session_state.interview_started and not st.session_state.interview_complete:
+                # Context focus selection
+                if st.session_state.context_focus is None:
+                    st.write("Before we begin, would you like to focus on a specific area or keep the questions general?")
+                    focus_options = ["general", "professional background", "personal interests", "technical skills", "life experiences"]
+                    selected_focus = st.selectbox("Choose focus area:", focus_options)
+                    if st.button("Set Focus"):
+                        st.session_state.context_focus = selected_focus
+                        st.rerun()
+                else:
+                    if st.button("Start Interview", type="primary", use_container_width=True):
+                        st.session_state.interview_started = True
+                        question = get_random_question(api_key, st.session_state.context_focus)
+                        st.session_state.messages.append(f"Q: {question}")
+                        st.rerun()
+            
+            elif st.session_state.interview_started and not st.session_state.interview_complete:
+                # Get last message
+                last_message = st.session_state.messages[-1]
+                
+                # If last message was an answer, get next question
+                if not last_message.startswith('Q:'):
+                    question = get_random_question(api_key, st.session_state.context_focus)
+                    st.session_state.messages.append(f"Q: {question}")
+                    st.rerun()
+                
+                # User input
+                user_answer = st.text_area("Your answer:", height=100)
+                
+                # Submit answer button
+                if st.button("Submit Answer"):
+                    if user_answer:
+                        st.session_state.messages.append(user_answer)
+                        st.rerun()
+                
+                # End interview button
+                if st.button("End Interview"):
+                    if len(st.session_state.messages) > 1:  # Ensure there's at least one Q&A pair
+                        st.session_state.interview_complete = True
+                        # Extract context
+                        st.session_state.context_data = extract_context(api_key, st.session_state.messages)
+                        st.rerun()
+        else:
+            st.warning("Please enter your OpenAI API key in the sidebar to begin.")
+
+with tab3:
+    st.header("Feature Gallery")
+    st.markdown("### Interactive Interview Process")
+    st.image("screenshots/1.png", use_column_width=True)
+    st.markdown("### Context Focus Selection")
+    st.image("screenshots/2.png", use_column_width=True)
+    st.markdown("### Generated Context Summary")
+    st.image("screenshots/3.png", use_column_width=True)
+
+with tab4:
+    if st.session_state.interview_complete and st.session_state.context_data:
+        st.header("Generated Context")
+        st.markdown("""
+        Below is the AI-generated context summary based on your interview responses. 
+        This structured data can be used to enhance future AI interactions and create 
+        more personalized experiences.
+        """)
+        st.markdown(st.session_state.context_data)
+        
+        # Export options in columns
+        st.subheader("Export Options")
+        col3, col4 = st.columns(2)
+        with col3:
+            if st.button("Copy to Clipboard", type="secondary", use_container_width=True):
+                st.write("Context copied to clipboard!")
+                st.code(st.session_state.context_data)
+        
+        with col4:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            filename = f"context_data_{timestamp}.md"
+            with open(filename, "w") as f:
+                f.write(st.session_state.context_data)
+            st.download_button(
+                label="Download as Markdown",
+                data=st.session_state.context_data,
+                file_name=filename,
+                mime="text/markdown",
+                use_container_width=True
+            )
+    else:
+        st.info("Complete the interview to generate your context summary.")

requirements.txt

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+streamlit>=1.29.0
+openai>=1.6.0