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import gradio as gr
from openai import OpenAI
import os
css = '''
.gradio-container{max-width: 1000px !important}
h1{text-align:center}
footer {
visibility: hidden
}
'''
ACCESS_TOKEN = os.getenv("HF_TOKEN")
client = OpenAI(
base_url="https://api-inference.huggingface.co/v1/",
api_key=ACCESS_TOKEN,
)
def respond(
message,
history,
temperature,
max_tokens,
):
SYS_PROMPT = """
Extract the following information from the given text:
Identify the specific areas where the work needs to be done and Add the furniture that has to be changed.
Do not specify the work that has to be done.
Format the extracted information in the following JSON structure:
{
"Area Type1": {
"Furnture1",
"Furnture2",
...
}
"Area Type2": {
"Furnture1",
"Furnture2",
...
}
}
Requirements:
1. Each area type (e.g., lobby, bar, etc.) should have its own node.
3. List the furniture on which the work needs to be performed without specifying the work or units of items.
4. Ignore any personal information or irrelevant details.
5. Follow the JSON pattern strictly and ensure clarity and accuracy in the extracted information.
Example:
Given the paragraph: "In the lobby, replace 5 light fixtures and remove 2 old carpets. In the bar,
install 3 new tables and remove 4 broken chairs."
The JSON output should be:
{
"Lobby": {
"Light fixtures"
"Old carpets"
},
"Bar": {
"New tables"
"Broken chairs"
}
}
}
Please ensure that the output JSON is well-structured and includes only relevant details about the work to be done.
"""
messages = [{"role": "system", "content": SYS_PROMPT}]
if len(history) == 0:
pass
else:
history.pop()
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat.completions.create(
model="meta-llama/Meta-Llama-3.1-8B-Instruct",
max_tokens=max_tokens,
stream=True,
temperature=temperature,
messages=messages,
):
token = message.choices[0].delta.content
response += token
yield response
DESCRIPTION = '''
<div>
<h1 style="text-align: center;">ContenteaseAI custom trained model</h1>
</div>
'''
LICENSE = """
<p/>
---
For more information, visit our [website](https://contentease.ai).
"""
PLACEHOLDER = """
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
<h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">ContenteaseAI Custom AI trained model</h1>
<p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Enter the text extracted from the PDF:</p>
</div>
"""
css = """
h1 {
text-align: center;
display: block;
}
"""
chatbot = gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface')
with gr.Blocks(fill_height=True, css=css) as demo:
gr.Markdown(DESCRIPTION)
gr.ChatInterface(
fn=respond,
chatbot=chatbot,
fill_height=True,
additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
additional_inputs=[
gr.Slider(minimum=0, maximum=1, step=0.1, value=0.9, label="Temperature", render=False),
gr.Slider(minimum=128, maximum=2000, step=1, value=2000, label="Max new tokens", render=False),
]
)
gr.Markdown(LICENSE)
if __name__ == "__main__":
try:
demo.launch(show_error=True, debug = True)
except Exception as e:
logger.error(f"Error launching Gradio demo: {e}") |