import gradio as gr
import pandas as pd
import requests
import json
import tiktoken

PRICES_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"

# Ensure TOKEN_COSTS is up to date when the module is loaded
try:
    response = requests.get(PRICES_URL)
    if response.status_code == 200:
        TOKEN_COSTS = response.json()
    else:
        raise Exception(f"Failed to fetch token costs, status code: {response.status_code}")
except Exception as e:
    print(f'Failed to update token costs with error: {e}. Using static costs.')
    with open("model_prices.json", "r") as f:
        TOKEN_COSTS = json.load(f)

TOKEN_COSTS = pd.DataFrame.from_dict(TOKEN_COSTS, orient='index').reset_index()
TOKEN_COSTS.columns = ['model'] + list(TOKEN_COSTS.columns[1:])

def count_string_tokens(string: str, model: str) -> int:
    """Returns the number of tokens in a text string."""
    try:
        encoding = tiktoken.encoding_for_model(model.split('/')[-1])
    except KeyError:
        print(f"Model {model} not found. Using cl100k_base encoding.")
        encoding = tiktoken.get_encoding("cl100k_base")
    return len(encoding.encode(string))

def calculate_total_cost(prompt_tokens: int, completion_tokens: int, model: str) -> float:
    """Calculate the total cost for a given model and number of tokens."""
    model_data = TOKEN_COSTS[TOKEN_COSTS['model'] == model].iloc[0]
    prompt_cost = prompt_tokens * model_data['input_cost_per_token']
    completion_cost = completion_tokens * model_data['output_cost_per_token']
    return prompt_cost + completion_cost

def update_model_list(function_calling, litellm_provider, max_price):
    filtered_models = TOKEN_COSTS[
        (TOKEN_COSTS['supports_function_calling'] == function_calling) &
        (TOKEN_COSTS['litellm_provider'] == litellm_provider) &
        (TOKEN_COSTS['input_cost_per_token'] + TOKEN_COSTS['output_cost_per_token'] <= max_price)
    ]
    return filtered_models['model'].tolist()

def compute_all(prompt_string, completion_string, model):
    prompt_tokens = count_string_tokens(prompt_string, model)
    completion_tokens = count_string_tokens(completion_string, model)
    cost = calculate_total_cost(prompt_tokens, completion_tokens, model)
    prompt_cost = prompt_tokens * TOKEN_COSTS[TOKEN_COSTS['model'] == model]['input_cost_per_token'].values[0]
    completion_cost = completion_tokens * TOKEN_COSTS[TOKEN_COSTS['model'] == model]['output_cost_per_token'].values[0]
    
    return (
        f"{prompt_tokens} tokens",
        f"${prompt_cost:.6f}",
        f"{completion_tokens} tokens",
        f"${completion_cost:.6f}",
        f"${cost:.6f}"
    )

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # Text-to-$$$: Calculate the price of your LLM runs
    Based on data from [litellm](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json).
    """)
    
    with gr.Row():
        with gr.Column(scale=2):
            prompt = gr.Textbox(label="Prompt", value="Tell me a joke about AI.", lines=3)
            completion = gr.Textbox(label="Completion", value="Here's a joke about AI: Why did the AI go to therapy? It had too many deep issues!", lines=3)
            
            with gr.Row():
                function_calling = gr.Checkbox(label="Supports Function Calling")
                litellm_provider = gr.Dropdown(label="LiteLLM Provider", choices=TOKEN_COSTS['litellm_provider'].unique().tolist())
            
            max_price = gr.Slider(label="Max Price per Token (input + output)", minimum=0, maximum=0.001, step=0.00001, value=0.001)
            
            model = gr.Dropdown(label="Model", choices=TOKEN_COSTS['model'].tolist())
            
            compute_button = gr.Button("Compute Costs", variant="primary")

        with gr.Column(scale=1):
            with gr.Group():
                prompt_tokens = gr.Textbox(label="Prompt Tokens", interactive=False)
                prompt_cost = gr.Textbox(label="Prompt Cost", interactive=False)
                completion_tokens = gr.Textbox(label="Completion Tokens", interactive=False)
                completion_cost = gr.Textbox(label="Completion Cost", interactive=False)
                total_cost = gr.Textbox(label="Total Cost", interactive=False)

    # Update model list based on criteria
    function_calling.change(update_model_list, inputs=[function_calling, litellm_provider, max_price], outputs=model)
    litellm_provider.change(update_model_list, inputs=[function_calling, litellm_provider, max_price], outputs=model)
    max_price.change(update_model_list, inputs=[function_calling, litellm_provider, max_price], outputs=model)

    # Compute costs
    compute_button.click(
        compute_all,
        inputs=[prompt, completion, model],
        outputs=[prompt_tokens, prompt_cost, completion_tokens, completion_cost, total_cost]
    )

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