import gradio as gr
from huggingface_hub import InferenceClient
from datasets import load_dataset
import time

def log(message):
    print(f"✅ {message}")


# ✅ Load the datasets
datasets = {
    "sales": load_dataset("goendalf666/sales-conversations", trust_remote_code=True),
    "blended": load_dataset("blended_skill_talk", trust_remote_code=True),
    "dialog": load_dataset("daily_dialog", trust_remote_code=True),
    "multiwoz": load_dataset("multi_woz_v22", trust_remote_code=True),
}

# Optional: Print dataset names and sizes
for name, dataset in datasets.items():
    print(f"{name}: {len(dataset['train'])} examples")

# Initialize the model client (use correct model for chatbot)
client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")

# Chatbot response function
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_completions(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message["choices"][0]["delta"]["content"]
        response += token
        yield response


# Gradio interface for chatbot
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)",
        ),
    ],
)

def start_embedding():
    # Include your embedding logic here (from embeddings.py)
    log("Embedding started...")
    time.sleep(2)  # Simulating embedding process
    log("Embedding process finished.")

# Create Gradio interface with a button to start the embedding
demo = gr.Interface(
    fn=start_embedding,
    inputs=None,
    outputs="text",
    live=True,
    title="Embedding Trigger"
)


# Launch Gradio app
if __name__ == "__main__":
    demo.launch()