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Building
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import gradio as gr
from openai import OpenAI
import os
from fpdf import FPDF # For PDF conversion
from docx import Document # For DOCX conversion
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):
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 = "output.pdf"
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 = "output.docx"
doc.save(file_name)
elif conversion_type == "TXT":
file_name = "output.txt"
with open(file_name, "w") as f:
f.write(f"User Query: {input_text}\n\nResponse: {output_text}")
else:
return None
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.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-P",
),
gr.Dropdown(choices=["PDF", "DOCX", "TXT"], label="Conversion Type"),
gr.Button("Convert and Download"),
],
css=css,
theme="allenai/gradio-theme",
)
def on_convert_and_download(history, conversion_type):
file_path = convert_and_download(history, conversion_type)
return file_path
demo.launch(on_event={"Convert and Download": on_convert_and_download})
if __name__ == "__main__":
demo.launch() |