Spaces:
Sleeping
Sleeping
import gradio as gr | |
from openai import OpenAI | |
import os | |
from fpdf import FPDF # For PDF conversion | |
from docx import Document # For DOCX conversion | |
import tempfile | |
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: list[tuple[str, str]], | |
system_message, | |
max_tokens, | |
temperature, | |
top_p, | |
): | |
messages = [{"role": "system", "content": system_message}] | |
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, | |
top_p=top_p, | |
messages=messages, | |
): | |
token = message.choices[0].delta.content | |
response += token | |
yield response | |
def save_as_file(input_text, output_text, conversion_type): | |
file_name = None | |
if conversion_type == "PDF": | |
pdf = FPDF() | |
pdf.add_page() | |
pdf.set_font("Arial", size=12) | |
pdf.multi_cell(0, 10, f"User Query: {input_text}\n\nResponse: {output_text}") | |
file_name = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf").name | |
pdf.output(file_name) | |
elif conversion_type == "DOCX": | |
doc = Document() | |
doc.add_heading('Conversation', 0) | |
doc.add_paragraph(f"User Query: {input_text}\n\nResponse: {output_text}") | |
file_name = tempfile.NamedTemporaryFile(delete=False, suffix=".docx").name | |
doc.save(file_name) | |
elif conversion_type == "TXT": | |
file_name = tempfile.NamedTemporaryFile(delete=False, suffix=".txt").name | |
with open(file_name, "w") as f: | |
f.write(f"User Query: {input_text}\n\nResponse: {output_text}") | |
return file_name | |
def convert_and_download(history, conversion_type): | |
if not history: | |
return None | |
input_text = "\n".join([f"User: {h[0]}" for h in history if h[0]]) | |
output_text = "\n".join([f"Assistant: {h[1]}" for h in history if h[1]]) | |
file_path = save_as_file(input_text, output_text, conversion_type) | |
return file_path | |
demo = gr.Blocks(css=css) | |
with demo: | |
with gr.Row(): | |
system_message = gr.Textbox(value="", label="System message") | |
max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens") | |
temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature") | |
top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-P") | |
chatbot = gr.ChatInterface( | |
fn=respond, | |
additional_inputs=[system_message, max_tokens, temperature, top_p], | |
) | |
with gr.Row(): | |
conversion_type = gr.Dropdown(choices=["PDF", "DOCX", "TXT"], label="Conversion Type") | |
download_button = gr.Button("Convert and Download") | |
file_output = gr.File() | |
download_button.click( | |
convert_and_download, | |
inputs=[chatbot.history, conversion_type], | |
outputs=file_output | |
) | |
if __name__ == "__main__": | |
demo.launch() |