Spaces:
Sleeping
Sleeping
File size: 3,447 Bytes
ad773e5 34ddc1f b0f6b9b 6ac13b6 ad773e5 34ddc1f ad773e5 34ddc1f ad773e5 34ddc1f 0ba7801 34ddc1f 6ac13b6 34ddc1f a6938ff 66ef9bd 34ddc1f 66ef9bd 0ba7801 34ddc1f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 |
import gradio as gr
from openai import OpenAI
import os
from fpdf import FPDF
import docx
css = '''
.gradio-container{max-width: 890px !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,
)
# Function to save generated text to a file
def save_file(content, filename, 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 line in content.split("\n"):
pdf.multi_cell(0, 10, line)
pdf.output(f"{filename}.pdf")
return f"{filename}.pdf"
elif file_format == "docx":
doc = docx.Document()
doc.add_paragraph(content)
doc.save(f"{filename}.docx")
return f"{filename}.docx"
elif file_format == "txt":
with open(f"{filename}.txt", "w") as f:
f.write(content)
return f"{filename}.txt"
else:
raise ValueError("Unsupported file format")
# Respond function with file saving
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-3.1-70B-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
return response, history + [(message, response)]
# Function to handle file saving after generation
def save_generated_file(response, filename, file_format):
saved_file = save_file(response, filename, file_format)
return saved_file
# Gradio interface using Blocks
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")
filename = gr.Textbox(value="output", label="Filename")
file_format = gr.Radio(["pdf", "docx", "txt"], label="File Format", value="pdf")
message = gr.Textbox(label="User Message")
chat_history = gr.State(value=[])
response_output = gr.Textbox(label="Generated Response")
file_output = gr.File(label="Download File")
generate_button = gr.Button("Generate")
save_button = gr.Button("Save to File")
generate_button.click(
respond,
inputs=[message, chat_history, system_message, max_tokens, temperature, top_p],
outputs=[response_output, chat_history]
)
save_button.click(
save_generated_file,
inputs=[response_output, filename, file_format],
outputs=[file_output]
)
if __name__ == "__main__":
demo.launch() |