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| import streamlit as st | |
| import json | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| import time | |
| # Page configuration | |
| st.set_page_config( | |
| page_title="Portfolio Chatbot Test", | |
| page_icon="🤖", | |
| layout="wide" | |
| ) | |
| # Initialize session state | |
| if 'messages' not in st.session_state: | |
| st.session_state.messages = [] | |
| def load_knowledge_base(): | |
| """Load the knowledge base from JSON file""" | |
| try: | |
| with open('knowledge_base.json', 'r', encoding='utf-8') as f: | |
| return json.load(f) | |
| except Exception as e: | |
| st.error(f"Error loading knowledge base: {str(e)}") | |
| return {} | |
| def get_context(query: str, knowledge_base: dict) -> str: | |
| """Get relevant context from knowledge base based on query""" | |
| query_lower = query.lower() | |
| contexts = [] | |
| # Project context | |
| if "project" in query_lower: | |
| if "projects" in knowledge_base: | |
| contexts.extend([ | |
| f"{name}: {desc}" | |
| for name, desc in knowledge_base["projects"].items() | |
| ]) | |
| # Skills context | |
| elif any(keyword in query_lower for keyword in ["skill", "experience", "capability"]): | |
| if "personal_details" in knowledge_base and "skills" in knowledge_base["personal_details"]: | |
| contexts.extend([ | |
| f"{skill}: {desc}" | |
| for skill, desc in knowledge_base["personal_details"]["skills"].items() | |
| ]) | |
| # Default context | |
| else: | |
| contexts = [ | |
| f"Name: {knowledge_base.get('personal_details', {}).get('full_name', 'Manyue')}", | |
| "Summary: I am an aspiring AI/ML engineer with experience in Python, Machine Learning, and Data Analysis." | |
| ] | |
| return "\n".join(contexts) | |
| def initialize_model(): | |
| """Initialize the model and tokenizer""" | |
| try: | |
| # For testing, use a smaller model | |
| model_name = "meta-llama/Llama-2-7b-chat-hf" # You'll need to adjust this | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_name, | |
| torch_dtype=torch.float16, | |
| device_map="auto" | |
| ) | |
| return tokenizer, model | |
| except Exception as e: | |
| st.error(f"Error initializing model: {str(e)}") | |
| return None, None | |
| def main(): | |
| st.title("Portfolio Chatbot Testing Interface") | |
| st.write("Test the chatbot's responses and interaction patterns") | |
| # Load knowledge base | |
| knowledge_base = load_knowledge_base() | |
| # Create two columns for layout | |
| col1, col2 = st.columns([2, 1]) | |
| with col1: | |
| st.subheader("Chat Interface") | |
| # Display chat messages from history | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| # Accept user input | |
| if prompt := st.chat_input("What would you like to know?"): | |
| # Add user message to chat history | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| # Get context for the query | |
| context = get_context(prompt, knowledge_base) | |
| # For now, just echo back a response (replace with actual model response later) | |
| response = f"Test Response: Let me tell you about that based on my experience..." | |
| # Display assistant response in chat message container | |
| with st.chat_message("assistant"): | |
| st.markdown(response) | |
| # Add assistant response to chat history | |
| st.session_state.messages.append({"role": "assistant", "content": response}) | |
| with col2: | |
| st.subheader("Testing Tools") | |
| if st.button("Clear Chat History"): | |
| st.session_state.messages = [] | |
| st.experimental_rerun() | |
| st.subheader("Sample Questions") | |
| if st.button("Tell me about your ML projects"): | |
| st.session_state.messages.append({ | |
| "role": "user", | |
| "content": "Tell me about your ML projects" | |
| }) | |
| st.experimental_rerun() | |
| if st.button("What are your Python skills?"): | |
| st.session_state.messages.append({ | |
| "role": "user", | |
| "content": "What are your Python skills?" | |
| }) | |
| st.experimental_rerun() | |
| if __name__ == "__main__": | |
| main() |