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from huggingface_hub import InferenceClient
import pandas as pd
import logging
import gradio as gr
logging.basicConfig(level=logging.INFO)
class FinanceSummary:
"""
Class for generating a detailed summary of financial data using the Mixtral model.
"""
def __init__(self):
self.client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
self.logger = logging.getLogger(__name__)
def format_prompt(self, data: pd.DataFrame) -> str:
"""
Format prompt for Mixtral model.
Args:
data (pd.DataFrame): Financial data in a DataFrame.
Returns:
str: Formatted prompt for the model.
"""
prompt = "<s>"
prompt += f"[INST] analyze given csv sheet and give me a detailed summary mention the range of money transactions,describe each column{data}[/INST]"
return prompt
def to_dataframe(self,filepath: str) -> pd.DataFrame:
"""
Read financial data from a CSV file and return it as a DataFrame.
Args:
filepath (str): Path to the CSV file containing financial data.
Returns:
pd.DataFrame: DataFrame containing the financial data.
"""
financial_data = pd.read_csv(filepath)
return financial_data
def generate(self, filepath: str, temperature: float = 0.9, max_new_tokens: int = 5000,
top_p: float = 0.95, repetition_penalty: float = 1.0) -> str:
"""
Generate a detailed summary of financial data.
Args:
data (pd.DataFrame): Financial data in a DataFrame.
temperature (float): Controls the randomness of the predictions. Defaults to 0.9.
max_new_tokens (int): Maximum number of tokens to generate. Defaults to 5000.
top_p (float): The cumulative probability for sampling from the logits. Defaults to 0.95.
repetition_penalty (float): Penalty for repetition. Defaults to 1.0.
Returns:
str: Generated summary.
"""
try:
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(top_p)
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
seed=42,
)
data = self.to_dataframe(filepath.name)
formatted_prompt = self.format_prompt(data)
stream = self.client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True,
return_full_text=False)
output = ""
for response in stream:
output += response.token.text
return output.replace("</s>", "")
except Exception as e:
self.logger.error(f"An error occurred: {e}")
return ""
if __name__ == "__main__":
finance_summary = FinanceSummary()
with gr.Blocks(css="style.css",theme=gr.themes.Soft()) as demo:
with gr.Row():
filepath = gr.File(label="Upload CSV File",elem_classes="upload-file")
with gr.Row():
submit_btn = gr.Button(value="Submit")
with gr.Row():
summary = gr.Textbox(label="Detailed Summary",lines=20)
submit_btn.click(finance_summary.generate,filepath,summary)
demo.launch() |