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from huggingface_hub import InferenceClient
import gradio as gr
import PyPDF2
import random
import pandas as pd
from io import StringIO
# Initialize the inference client with your chosen model
client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
def format_prompt(message, history):
prompt = "<s>"
for user_prompt, bot_response in history:
prompt += f"[INST] {user_prompt} [/INST]"
prompt += f" {bot_response}</s> "
prompt += f"[INST] {message} [/INST]"
return prompt
def extract_text_from_pdf(file):
pdf_reader = PyPDF2.PdfReader(file)
text = ""
for page in range(len(pdf_reader.pages)):
text += pdf_reader.pages[page].extract_text()
return text
def generate_synthetic_data(sentences, generate_kwargs):
synthetic_data = []
for sentence in sentences:
formatted_prompt = format_prompt(sentence, [])
stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream:
output += response.token.text
synthetic_data.append(output)
return synthetic_data
def generate(file, system_prompt, temperature, max_new_tokens, top_p, repetition_penalty):
# Extract text and split into sentences
text = extract_text_from_pdf(file)
sentences = text.split('.')
random.shuffle(sentences) # Shuffle sentences
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
seed=42,
)
synthetic_data = generate_synthetic_data(sentences, generate_kwargs)
# Convert synthetic data to CSV
df = pd.DataFrame(synthetic_data, columns=["Synthetic Data"])
csv_buffer = StringIO()
df.to_csv(csv_buffer, index=False)
return gr.File(value=csv_buffer.getvalue(), file_name="synthetic_data.csv")
additional_inputs = [
gr.Slider(label="Temperature", value=0.9, minimum=0.0, maximum=1.0, step=0.05, interactive=True, info="Higher values produce more diverse outputs"),
gr.Slider(label="Max new tokens", value=256, minimum=0, maximum=5120, step=64, interactive=True, info="The maximum numbers of new tokens"),
gr.Slider(label="Top-p (nucleus sampling)", value=0.90, minimum=0.0, maximum=1, step=0.05, interactive=True, info="Higher values sample more low-probability tokens"),
gr.Slider(label="Repetition penalty", value=1.2, minimum=1.0, maximum=2.0, step=0.05, interactive=True, info="Penalize repeated tokens"),
gr.File(label="Upload PDF File", file_count="single", file_types=[".pdf"]),
]
gr.Interface(
fn=generate,
inputs=[gr.File(label="Upload PDF File", file_count="single", file_types=[".pdf"]), "state", "number", "number", "number", "number"],
outputs="file",
additional_inputs=additional_inputs,
title="Synthetic Data Generation",
description="This tool generates synthetic data from the sentences in your PDF.",
allow_flagging="never",
).launch() |