Update app.py

app.py

@@ -1,77 +1,88 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModel
-import random
-
-class TokenizerModelInitializer:
-    def __init__(self, model_name: str):
-        """
-        Initializes the TokenizerModelInitializer with a specified model name.
-        """
-        if not model_name:
-            raise ValueError("Model name must not be empty.")
-        
-        self.model_name = model_name
-        try:
-            # Load tokenizer and model
-            self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
-            self.model = AutoModel.from_pretrained(self.model_name)
-        except Exception as e:
-            raise Exception(f"Failed to load the model or tokenizer: {str(e)}")
-
-    def tokenize_text(self, text: str):
-        """
-        Tokenizes the input text using the initialized tokenizer.
-        """
-        if not text:
-            raise ValueError("Input text must not be empty.")
-        
-        tokens = self.tokenizer.tokenize(text)
-        return tokens
-
-# Instantiate the model and tokenizer
-model_name = "bert-base-uncased"
-initializer = TokenizerModelInitializer(model_name)
-
-# Gradio Function to Display Tokenized Text
-def gradio_tokenize(input_text):
+import pandas as pd
+import os
+
+def load_dataset(file_path: str) -> pd.DataFrame:
+    """
+    Loads a dataset from a specified file path into a Pandas DataFrame.
+
+    This function reads a dataset from a given file path. The file can be in various formats
+    supported by Pandas, such as CSV, Excel, or JSON. The function returns the dataset as a
+    Pandas DataFrame, which is a powerful data structure for data manipulation and analysis.
+
+    Parameters:
+    - file_path (str): The path to the dataset file. This should be a string representing
+                        the location of the file on the filesystem.
+
+    Returns:
+    pd.DataFrame: A DataFrame containing the loaded dataset.
+
+    Raises:
+    - FileNotFoundError: If the specified file path does not exist or cannot be found.
+    - ValueError: If the file format is not supported or if the file is empty.
+    - pd.errors.EmptyDataError: If the file is empty and cannot be read into a DataFrame.
+    - pd.errors.ParserError: If there is an error while parsing the file.
+    - TypeError: If the file path is not a string or is an unsupported file format.
+
+    Examples:
+    >>> df = load_dataset('data/my_dataset.csv')
+    >>> print(df.head())
+    """
+
+    # Checking if file path is a string
+    if not isinstance(file_path, str):
+        raise TypeError(f"Expected file path to be a string, but got {type(file_path).__name__}.")
+
+    # Checking if the file exists
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"File not found: {file_path}. Please check the path and try again.")
+
+    # Attempting to load the dataset based on the file extension
     try:
-        tokens = initializer.tokenize_text(input_text)
-        return f"Tokens: {tokens}"
+        # Determine the file extension and load the file accordingly
+        file_extension = file_path.split('.')[-1].lower()
+
+        if file_extension == 'csv':
+            dataset = pd.read_csv(file_path)
+        elif file_extension in ['xlsx', 'xls']:
+            dataset = pd.read_excel(file_path)
+        elif file_extension == 'json':
+            dataset = pd.read_json(file_path)
+        else:
+            raise ValueError(f"Unsupported file format: {file_extension}. Supported formats are CSV, Excel, and JSON.")
+
+        # Checking if the dataset is empty
+        if dataset.empty:
+            raise pd.errors.EmptyDataError(f"The file at {file_path} is empty and cannot be loaded into a DataFrame.")
+
+        return dataset
+
+    except ValueError as value_error:
+        raise ValueError(f"Error loading the dataset from {file_path}. Please ensure the file is in a supported format and not empty.") from value_error
+
+    except pd.errors.EmptyDataError as empty_data_error:
+        raise pd.errors.EmptyDataError(f"The file at {file_path} is empty and cannot be loaded into a DataFrame.") from empty_data_error
+
+    except pd.errors.ParserError as parser_error:
+        raise pd.errors.ParserError(f"Error parsing the file at {file_path}. Please check the file format and contents.") from parser_error
+
     except Exception as e:
-        return f"Error: {str(e)}"
-
-# Gradio Chatbot Function (simulated)
-def chatbot_response(user_input):
-    responses = [
-        "That's an interesting question! Let me think about it.",
-        "I'm still learning, but I will improve soon!",
-        "Thanks for your input! I'm always evolving.",
-        "Great question! I'll keep working on my answers.",
-    ]
-    return random.choice(responses)
-
-# Create Gradio Interface for Tokenization
-tokenization_interface = gr.Interface(
-    fn=gradio_tokenize,
-    inputs="text",
-    outputs="text",
-    title="Tokenization Interface",
-    description="Enter a text and I'll tokenize it using the specified model's tokenizer.",
-    theme="compact"
-)
-
-# Create Gradio Interface for Chatbot
-chatbot_interface = gr.Interface(
-    fn=chatbot_response,
-    inputs=gr.Textbox(label="Ask me anything!"),
-    outputs="text",
-    title="Competing Chatbot",
-    description="Challenge me with your questions while the model is fine-tuning!",
-    theme="compact"
-)
-
-# Combine both interfaces in a tabbed interface
-demo = gr.TabbedInterface([tokenization_interface, chatbot_interface], tab_names=["Tokenization", "Chatbot"])
-
-# Launch the interface
-demo.launch()
+        raise Exception(f"An error occurred while loading the file: {file_path}. Error details: {str(e)}") from e
+
+
+# Example usage of the load_dataset function:
+try:
+    # Example 1: Loading a dataset from a CSV file
+    dataset = load_dataset('data/my_dataset.csv')
+    print("Dataset loaded successfully!")
+    print(dataset.head())  # Displaying the first few rows of the dataset
+
+    # Example 2: Loading a dataset from an Excel file
+    dataset = load_dataset('data/my_dataset.xlsx')
+    print("Dataset loaded successfully!")
+    print(dataset.head())  # Displaying the first few rows of the dataset
+
+    # Example 3: Attempting to load a non-existent file (should raise an error)
+    dataset = load_dataset('data/non_existent_file.csv')
+
+except Exception as e:
+    print(f"An error occurred: {e}")