import os
import requests
import gradio as gr
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

# Fetch token from environment variables
CLOUDFLARE_TOKEN = os.getenv("CLOUDFLARE_TOKEN")
if not CLOUDFLARE_TOKEN:
    raise EnvironmentError("CLOUDFLARE_TOKEN not found in environment variables.")

# Function to send API requests to the Gemma model
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    try:
        # Construct the messages payload
        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})

        # Define API endpoint and headers
        url = "https://api.cloudflare.com/client/v4/accounts/e16531aac7469b4b54ef1e8108e93495/ai/run/@cf/google/gemma-2b-it-lora"
        headers = {
            "Authorization": f"Bearer {CLOUDFLARE_TOKEN}",
            "Content-Type": "application/json",
        }

        # Payload with model settings and messages
        payload = {
            "messages": messages,
            "raw": "true",
            "lora": "1b3c4e5c-ba8a-4e98-973b-573c572cfb34",
            "max_tokens": max_tokens,
            "temperature": temperature,
            "top_p": top_p,
        }

        # Make the API request
        response = requests.post(url, headers=headers, json=payload)

        if response.status_code == 200:
            data = response.json()
            # Extract response content
            result = data.get("result", {}).get("response", "No response found.")
            return result
        else:
            return f"Error: {response.status_code} - {response.text}"
    except Exception as e:
        return f"An error occurred: {str(e)}"


# Gradio Interface
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()