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import os
import gradio as gr
import json
from datetime import datetime
import openai
# Assistant Creation function
def create_assistant_json(uploaded_file, assistant_name, assistant_message):
client = openai.OpenAI(api_key=os.environ["API_TOKEN"])
# Check if a file was uploaded
print(uploaded_file)
df = open(uploaded_file, "rb")
file = client.files.create(file=df,
purpose='assistants')
assistant = client.beta.assistants.create(
name=assistant_name,
instructions=assistant_message,
model="gpt-4-0125-preview",
tools=[
{
"type": "retrieval" # This adds the knowledge base as a tool
}
],
file_ids=[file.id])
return assistant.id
def generate_response(prompt):
client = openai.OpenAI(api_key=os.environ["API_TOKEN"])
instruction = "Please generate a cocktail recipe based on the user's mood description.\n\n"
prompt = instruction + prompt
messages=[
{"role": "system", "content": "You are a helpful bartender assistant."},
{"role": "user", "content": prompt}
]
try:
response = client.chat.completions.create(
model="gpt-4-0125-preview",
messages=messages,
max_tokens=150)
return response.choices[0].message.content
except Exception as e:
return str(e)
# Creating the Gradio interface
with gr.Blocks(css=".gradio-container {background: url(https://static.vecteezy.com/system/resources/thumbnails/030/814/051/small/wooden-table-and-blur-tropical-green-grass-background-product-display-montage-high-quality-8k-fhd-ai-generated-photo.jpg)}") as demo:
gr.Markdown("## To create an OpenAI Assistant please fill in the following sections. Upload a file to give the Assistant knowledge and a focus on something outside of it's normal training. Then add an assistant name and message. The Assistant message should guide the model into in a role. An example would be, You are a helpful Asssitant who is knowledgable in the field of...")
gr.Markdown("## After creating the ID head to [OpenAI_Assistant_Chat](https://huggingface.co/spaces/jadend/OpenAI_Assistant_Chat).")
with gr.Row():
# file_input = gr.File(label="Upload your file", type="filepath")
description = gr.Textbox(label="The User Input")
# chatbot = gr.Textbox(label="Chatbot Response")
generate_button = gr.Button("Generate Your Cocktail Recipe")
output_id = gr.Textbox(label="Your Cocktail Recipe", value="")
generate_button.click(
fn=generate_response,
inputs=description,
outputs=output_id
)
if __name__ == "__main__":
demo.launch(#enable_queue=False,
# Creates an auth screen
auth_message="Welcome! Enter a Username and Password"
).queue() |