from huggingface_hub import InferenceClient
import gradio as gr
from langchain_community.tools import DuckDuckGoSearchRun

client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")

# Initialize DuckDuckGo search tool
duckduckgo_search = DuckDuckGoSearchRun()

def format_prompt(message, history):
    prompt = "<s>"
    for user_prompt, bot_response in history:
        prompt += f"[INST] {user_prompt} [/INST]"
        prompt += f" {bot_response}</s> "
    prompt += f"[INST] {message} [/INST]"
    return prompt

def generate(prompt, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )

    formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
    
    # Generate response using model
    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    output = ""

    for response in stream:
        output += response.token.text
        # Yield model's response first
        yield output
        
    # Now, perform DuckDuckGo search and yield results
    search_result = duckduckgo_search.run(prompt)
    if search_result:
        yield search_result
    else:
        yield "Sorry, I couldn't find any relevant information."

additional_inputs = [
    gr.Textbox(
        label="System Prompt",
        max_lines=1,
        interactive=True,
    ),
    gr.Slider(
        label="Temperature",
        value=0.9,
        minimum=0.0,
        maximum=1.0,
        step=0.05,
        interactive=True,
        info="Higher values produce more diverse outputs",
    ),
    gr.Slider(
        label="Max new tokens",
        value=256,
        minimum=0,
        maximum=1048,
        step=64,
        interactive=True,
        info="The maximum numbers of new tokens",
    ),
    gr.Slider(
        label="Top-p (nucleus sampling)",
        value=0.90,
        minimum=0.0,
        maximum=1,
        step=0.05,
        interactive=True,
        info="Higher values sample more low-probability tokens",
    ),
    gr.Slider(
        label="Repetition penalty",
        value=1.2,
        minimum=1.0,
        maximum=2.0,
        step=0.05,
        interactive=True,
        info="Penalize repeated tokens",
    )
]

examples = [
    ["I'm planning a vacation to Japan. Can you suggest a one-week itinerary including must-visit places and local cuisines to try?", None, None, None, None, None],
    ["What are some best tourist places to visit in Lajpat nagar, Delhi?", None, None, None, None, None],
    ["Ronaldo or Messi?", None, None, None, None, None],
    ["Can you explain how the QuickSort algorithm works and provide a Python implementation?", None, None, None, None, None],
]

gr.ChatInterface(
    fn=generate,
    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
    additional_inputs=additional_inputs,
    title="🤗 friday2.0 🤗 WELCOME TO OPEN-SOURCE FREEDOM",
    examples=examples,
    concurrency_limit=20,
).launch(show_api=False, share=True)