COLLEAGUE-AI / app.py
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import os
import openai
import gradio as gr
openai.api_key = os.getenv('OPEN_AI_KEY')
hf_t_key = ('HF_TOKEN_KEY')
def predict(message, history):
history_openai_format = []
for human, assistant in history:
history_openai_format.append({"role": "user", "content": human })
history_openai_format.append({"role": "assistant", "content": assistant})
history_openai_format.append({"role": "user", "content": message})
response = openai.ChatCompletion.create(
model='ft:gpt-3.5-turbo-1106:2292030-peach-tech::8WnRqZjS',
messages= history_openai_format,
temperature=1.0,
stream=True
)
partial_message = ""
for chunk in response:
if len(chunk['choices'][0]['delta']) != 0:
partial_message = partial_message + chunk['choices'][0]['delta']['content']
yield partial_message
A1 = gr.ChatInterface(predict,
title="COLLEAGUE",
description="An AI Productivity Assistant with Chat, Vision, and Image Generation Capabilities, Built By Peach State Innovation and Technology. Select The Corresponding Tab For Tool Accessibility",
textbox=gr.Textbox(placeholder="Enter your question/prompt here..."),
theme= gr.themes.Glass(primary_hue="amber", neutral_hue="lime"),
retry_btn=None,
clear_btn="Clear Conversation")
A2 = gr.load(
"models/Salesforce/blip-image-captioning-large",
title=" ",
description="Take and Upload a Photo or Existing Image, I'll Give You Its Description",
outputs=[gr.Textbox(label="I see...")],
theme= gr.themes.Glass(primary_hue="amber", neutral_hue="lime"))
A3 = gr.load(
"huggingface/stabilityai/stable-diffusion-xl-base-1.0",
inputs=[gr.Textbox(label="Enter Your Image Description")],
outputs=[gr.Image(label="Image")],
title=" ",
description="Bring Your Imagination Into Existence On The Digital Canvas With COLLEAGUE, Powered With Stable Diffusion",
allow_flagging="never",
examples=["A monster wandering the streets of downtown Atlanta","A robot eating pizza in a Brazilian favela"])
pcp = gr.TabbedInterface([A1, A2, A3], ["Chat", "Describe", "Create"], theme= gr.themes.Glass(primary_hue="amber", neutral_hue="lime"))
pcp.queue().launch()