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import gradio as gr
from openai import OpenAI
import os
from fpdf import FPDF
from docx import Document
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_to_file(history, file_format):
if file_format == "PDF":
pdf = FPDF()
pdf.add_page()
pdf.set_auto_page_break(auto=True, margin=15)
pdf.set_font("Arial", size=12)
for user_message, assistant_message in history:
pdf.multi_cell(0, 10, f"User: {user_message}")
pdf.multi_cell(0, 10, f"Assistant: {assistant_message}")
file_name = "chat_history.pdf"
pdf.output(file_name)
elif file_format == "DOCX":
doc = Document()
for user_message, assistant_message in history:
doc.add_paragraph(f"User: {user_message}")
doc.add_paragraph(f"Assistant: {assistant_message}")
file_name = "chat_history.docx"
doc.save(file_name)
elif file_format == "TXT":
file_name = "chat_history.txt"
with open(file_name, "w") as file:
for user_message, assistant_message in history:
file.write(f"User: {user_message}\n")
file.write(f"Assistant: {assistant_message}\n")
return file_name
# Gradio Interface Setup
with gr.Blocks(css=css) as demo:
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")
save_as = gr.Radio(["PDF", "DOCX", "TXT"], label="Save As")
chat = gr.Chatbot()
msg = gr.Textbox(label="Your message")
def respond_wrapper(message, history):
response_generator = respond(
message,
history,
system_message.value,
max_tokens.value,
temperature.value,
top_p.value
)
response = next(response_generator)
return history + [(message, response)]
msg.submit(respond_wrapper, [msg, chat], [chat])
save_button = gr.Button("Save Conversation")
output_file = gr.File(label="Download File")
def handle_save(history, file_format):
return save_to_file(history, file_format)
save_button.click(handle_save, inputs=[chat, save_as], outputs=output_file)
if __name__ == "__main__":
demo.launch() |