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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() |