Create app.py

app.py

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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{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)
+            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()