# from transformers import pipeline, Conversation
# import gradio as gr
# import os
# from getpass import getpass

# model = os.getenv('bigcode/starcoder')

# chatbot = pipeline(task="text-generation")

# message_list = []
# response_list = []

# def YourCoder_chatbot(message, history):

#     python_code_examples = f"""
#   ---------------------
#   Example 1: Code Snippet
#  def calculate_average(numbers):
#   total = 0
#   for number in numbers:
#     total += number
#   average = total / len(numbers)
#   return average
#   Code Review: Consider using the sum() function to calculate the total sum of the numbers
#    instead of manually iterating over the list.
#    This would make the code more concise and efficient.
#   ---------------------
#   Example 2: Code Snippet
#   def find_largest_number(numbers):
#   largest_number = numbers[0]
#   for number in numbers:
#     if number > largest_number:
#       largest_number = number
#   return largest_number
#   Code Review: Refactor the code using the max() function to find the largest number in the list.
#    This would simplify the code and improve its readability.
#   ---------------------
#   """

#     prompt = f"""
#   I will provide you with code snippets,
#   and you will review them for potential issues and suggest improvements.
#    Please focus on providing concise and actionable feedback, highlighting areas
#    that could benefit from refactoring, optimization, or bug fixes.
#    Your feedback should be constructive and aim to enhance the overall quality and maintainability of the code.
#    Please avoid providing explanations for your suggestions unless specifically requested. Instead, focus on clearly identifying areas for improvement and suggesting alternative approaches or solutions.
#    Few good examples of Python code output between #### separator:
#   ####
#   {python_code_examples}
#   ####
#   Code Snippet is shared below, delimited with triple backticks:
#   ```
#   {message}
#   ```
#   """
    
#     conversation = chatbot(prompt)
#     return conversation[0]['generated_text']

# chatbot = gr.ChatInterface(YourCoder_chatbot, title="YourCoder Chatbot", description="Enter piece of code to generate a code review!")
# chatbot.launch()

# import gradio as gr

# # def YourCoder_chatbot(message, history):
# #     gr.load("models/bigcode/starcoder")

# # chatbot = gr.ChatInterface(YourCoder_chatbot, title="YourCoder Chatbot", description="Enter piece of code to generate a code review!")
# chatbot = gr.Interface(fn=gr.load("models/bigcode/starcoder"), inputs=[gr.Textbox(label="Insert Code Snippet",lines=5)],
#                     outputs=[gr.Textbox(label="Review Here",lines=8)],
#                     title="Code Reviewer"
#                     )
# # gr.load("models/bigcode/starcoder").launch()

# chatbot.launch()


#####################
import os
import gradio as gr
from transformers import pipeline

# Get the token from environment variables
# token = os.getenv("HUGGINGFACE_TOKEN")
# if token is None:
#     raise ValueError("Hugging Face token is not set in the environment variables.")

# Load the model from the Hugging Face Model Hub with authentication
generator = pipeline('text-generation', model='bigcode/starcoder', use_auth_token=token)

# Define the prediction function
def generate_text(prompt):
    result = generator(prompt, max_length=50)
    return result[0]['generated_text']

# Create the Gradio interface
iface = gr.Interface(fn=generate_text, inputs="text", outputs="text")

# Launch the app
iface.launch()