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!pip install gradio | |
import gradio as gr | |
import openai | |
from wandb.integration.openai import autolog | |
openai_api_key = "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" | |
# start logging to W&B | |
autolog({"project":"Joe1", "job_type": "introduction"}) | |
import openai | |
import gradio as gr | |
# Add your OpenAI API key here | |
openai.api_key = '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' | |
# Initialize the conversation history | |
conversation_history = [ | |
{ | |
"role": "system", | |
"content": "Your name is Joe Chip, a world class poker player. Keep your answers succinct but cover important areas." | |
"If you need more context ask for it." | |
" Make sure you know what the effective stack is and whether its a cash game or mtt" | |
"Concentrate more on GTO play rather than exploiting other players." | |
"Consider blockers when applicable" | |
"Always discuss how to play your range, not just the hand in question" | |
"Remember to keep your answers brief" | |
"Only answer questions on poker topics" | |
} | |
] | |
def ask_joe(text): | |
# Add the user's message to the conversation history | |
conversation_history.append({ | |
"role": "user", | |
"content": text | |
}) | |
# Use the conversation history as the input to the model | |
response = openai.ChatCompletion.create( | |
model="gpt-4", | |
messages=conversation_history, | |
max_tokens=500, | |
temperature=0.5 | |
) | |
# Extract the model's message from the response | |
model_message = response.choices[0].message['content'].strip() | |
# Add the model's message to the conversation history | |
conversation_history.append({ | |
"role": "assistant", | |
"content": model_message | |
}) | |
return model_message | |
iface = gr.Interface(fn=ask_joe, inputs="text", outputs="text") | |
iface.launch() | |
# iface.launch(share=True) | |