import gradio as gr
from huggingface_hub import InferenceClient
import os
import json
import importlib
import sys

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

########

import os
import requests
import base64

GITHUB_API_KEY = os.environ.get('GITHUB_API_KEY')
GITHUB_API_URL_PY = os.environ.get('GITHUB_API_URL_PY')
GITHUB_API_URL_JSON = os.environ.get('GITHUB_API_URL_JSON')
GITHUB_API_URL = os.environ.get('GITHUB_API_URL')
HEADERS = {"Authorization": f"token {GITHUB_API_KEY}"}


def fetch_and_save_files():
    """
    Fetches all Python files from a specific folder in a private GitHub repository
    and saves them locally.
    """
    response = requests.get(GITHUB_API_URL, headers=HEADERS)
    if response.status_code != 200:
        raise Exception(f"Failed to list files: {response.status_code} - {response.text}")
    
    files = response.json()
    py_files = [f for f in files if f['name'].endswith('.py')]

    os.makedirs("temp_modules", exist_ok=True)  # Create a local folder to save the files

    for file in py_files:
        file_name = file["name"]
        file_url = file["url"]  # API URL to fetch content

        # Fetch file content
        file_response = requests.get(file_url, headers=HEADERS)
        if file_response.status_code != 200:
            print(f"Failed to fetch {file_name}")
            continue

        file_content = base64.b64decode(file_response.json()["content"]).decode("utf-8")

        # Save file locally
        local_path = os.path.join("temp_modules", file_name)
        with open(local_path, "w") as f:
            f.write(file_content)
            print(f"Saved {file_name} to {local_path}")

    print("All Python files have been saved locally.")

def load_modules():
    """
    Dynamically loads all saved Python files as modules.
    """
    temp_folder = "temp_modules"
    sys.path.append(temp_folder)  # Add folder to Python path

    modules = {}
    for file_name in os.listdir(temp_folder):
        if file_name.endswith(".py"):
            module_name = file_name[:-3]  # Strip ".py" extension
            try:
                modules[module_name] = importlib.import_module(module_name)
                print(f"Loaded module: {module_name}")
            except Exception as e:
                print(f"Failed to load module {module_name}: {e}")

    return modules

# Step 2: Fetch and save Python files, then load them dynamically
try:
    fetch_and_save_files()
    modules = load_modules()
    print("\nModules loaded successfully. You can now call their methods freely!")

    # Example usage
    # Assuming you have a class or function in main.py
    main_module = modules.get("main20")  # Access the 'main.py' module
    classes_module = modules.get("second_main")  # Access the 'classes.py' module

    if main_module and hasattr(main_module, "main_function"):
        main_module.main_function()  # Call a function from main.py

    if classes_module and hasattr(classes_module, "SomeClass"):
        obj = classes_module.SomeClass()  # Instantiate a class from classes.py
        obj.some_method()  # Call a method

except Exception as e:
    print("Error:", e)

a = classes_module.clio(4)
b = a.print_man()
print(b)
c = main_module.first_func(778)
print(c)

########


def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)


if __name__ == "__main__":
    demo.launch()