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import gradio as gr | |
from analyzer import analyze_code | |
# System prompt for the chatbot | |
CHATBOT_SYSTEM_PROMPT = ( | |
"You are a helpful assistant. Your goal is to help the user describe their ideal open-source repo. " | |
"Ask questions to clarify what they want, their use case, preferred language, features, etc. " | |
"When the user clicks 'End Chat', analyze the conversation and return about 5 keywords for repo search. " | |
"Return only the keywords as a comma-separated list." | |
) | |
# Store the conversation | |
conversation_history = [] | |
# Function to handle chat | |
def chat_with_user(user_message, history): | |
from openai import OpenAI | |
client = OpenAI() | |
# Build the message list for the LLM | |
messages = [ | |
{"role": "system", "content": CHATBOT_SYSTEM_PROMPT} | |
] | |
for msg in history: | |
messages.append({"role": "user", "content": msg[0]}) | |
if msg[1]: | |
messages.append({"role": "assistant", "content": msg[1]}) | |
messages.append({"role": "user", "content": user_message}) | |
response = client.chat.completions.create( | |
model="gpt-4o-mini", | |
messages=messages, | |
max_tokens=256, | |
temperature=0.7 | |
) | |
assistant_reply = response.choices[0].message.content | |
return assistant_reply | |
# Function to end chat and extract keywords | |
def extract_keywords_from_conversation(history): | |
# Combine all user and assistant messages into a single string | |
conversation = "\n".join([f"User: {msg[0]}\nAssistant: {msg[1]}" for msg in history if msg[1]]) | |
prompt = ( | |
"Given the following conversation between a user and an assistant about finding an ideal open-source repo, " | |
"extract about 5 keywords that best represent what the user is looking for. " | |
"Return only the keywords as a comma-separated list.\n\nConversation:\n" + conversation | |
) | |
keywords = analyze_code(prompt) | |
return keywords | |
with gr.Blocks() as chatbot_demo: | |
gr.Markdown("## Repo Recommendation Chatbot") | |
chatbot = gr.Chatbot() | |
state = gr.State([]) # conversation history | |
user_input = gr.Textbox(label="Your message", placeholder="Describe your ideal repo or answer the assistant's questions...") | |
send_btn = gr.Button("Send") | |
end_btn = gr.Button("End Chat and Extract Keywords") | |
keywords_output = gr.Textbox(label="Extracted Keywords for Repo Search", interactive=False) | |
def user_send(user_message, history): | |
assistant_reply = chat_with_user(user_message, history) | |
history = history + [[user_message, assistant_reply]] | |
return history, history, "" | |
def end_chat(history): | |
keywords = extract_keywords_from_conversation(history) | |
return keywords | |
send_btn.click(user_send, inputs=[user_input, state], outputs=[chatbot, state, user_input]) | |
end_btn.click(end_chat, inputs=state, outputs=keywords_output) | |
if __name__ == "__main__": | |
chatbot_demo.launch() |